Electric Mobility in Public Transport―Driving Towards Cleaner Air (Lecture Notes in Intelligent Transportation and Infrastructure) 3030674304, 9783030674304

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Lecture Notes in Intelligent Transportation and Infrastructure Series Editor: Janusz Kacprzyk

Krzysztof Krawiec Sylwester Markusik Grzegorz Sierpiński   Editors

Electric Mobility in Public Transport— Driving Towards Cleaner Air

Lecture Notes in Intelligent Transportation and Infrastructure Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Lecture Notes in Intelligent Transportation and Infrastructure” (LNITI) publishes new developments and advances in the various areas of intelligent transportation and infrastructure. The intent is to cover the theory, applications, and perspectives on the state-of-the-art and future developments relevant to topics such as intelligent transportation systems, smart mobility, urban logistics, smart grids, critical infrastructure, smart architecture, smart citizens, intelligent governance, smart architecture and construction design, as well as green and sustainable urban structures. The series contains monographs, conference proceedings, edited volumes, lecture notes and textbooks. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable wide and rapid dissemination of high-quality research output.

More information about this series at http://www.springer.com/series/15991

Krzysztof Krawiec Sylwester Markusik Grzegorz Sierpiński •

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Editors

Electric Mobility in Public Transport—Driving Towards Cleaner Air

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Editors Krzysztof Krawiec Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice, Poland

Sylwester Markusik Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice, Poland

Grzegorz Sierpiński Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice, Poland

ISSN 2523-3440 ISSN 2523-3459 (electronic) Lecture Notes in Intelligent Transportation and Infrastructure ISBN 978-3-030-67430-4 ISBN 978-3-030-67431-1 (eBook) https://doi.org/10.1007/978-3-030-67431-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Due to environmental concerns, electromobility is perceived as one of the methods to reduce the emissions of harmful substances emitted by the transport sector. There is a general opinion that public transport is suitable for the deployment of battery electric vehicles on a larger scale in the first place. Battery electric vehicles are already operated in public transport in many cities across the world. The vast majority of these vehicles are operated in China; however, there is an increase in the number of battery electric buses in Europe as well. As the conventional bus fleet conversion towards—at least partially—electric one is of a complex nature, in this monograph, we present various aspects of this issue. These aspects include the elements of transport policy on electromobility, economic view on the problem, energy consumption calculation for electric vehicles, charging-related and battery-related challenges. We also present other aspects of this issue, such as cybersecurity in public transport and basic information related to autonomous vehicles and hyperloop technology. The first four chapters discuss the transport policy on electromobility. In the first chapter, a brief historical view of electric mobility is presented in the context of the smart city concept. In the next one, e-mobility as a concept of public transport development in Poland is deliberated. Subsequently, the monograph contains two chapters in which the determinants of electromobility development are presented, in general, and with regards to the charging infrastructure. Further, an overview of challenges ahead of electrified public transport and technical solutions is set out. The latter include the methodology for probabilistic assessment of energy consumption by electric buses on routes, scheduling and balancing of electric buses’ charging operations and the evaluation of alternatives for realization of opportunity charging at bus stops (based on the power grid analysis). The issue of battery management system in electric vehicles is also raised. This part ends with the case study and cost analysis of bus fleet electrification in a Polish city of Wroclaw. In the three last chapters, attention is drawn to various issues related to the future operation of electrified transport systems. Among these, cybersecurity in public transport is of great importance. We also try to attempt to include autonomous v

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vehicles into the conventional-to-electric conversion process. The latter chapter discusses the hyperloop as a technology of the future. We would like to thank all the authors for their valuable contributions. Katowice, Poland

Krzysztof Krawiec Sylwester Markusik Grzegorz Sierpiński

Contents

Transport Policy on Electromobility Electromobility in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Krzysztof Patola and Janusz Szpytko

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E-Mobility as a Concept of Public Transport Development in Poland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ewelina Sendek-Matysiak

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Determinants for the Effective Electromobility Development in Public Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Barbara Kos, Grzegorz Krawczyk, and Robert Tomanek

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Determinants for the Effective Development and Operation of the Charging Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grzegorz Dydkowski and Anna Urbanek

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Technical Issues Related to Electromobility in Public Transport Electric Buses: A Review of Selected Concepts Solutions and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teresa Pamuła and Stanisław Krawiec Methodology for Probabilistic Assessment of Energy Consumption by Electric Buses on Routes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladimir Algin, Arkadi Goman, Andrei Skorokhodov, Oleg Bytsko, Sergey Chistov, and Sviatlana Fedasenka

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Scheduling and Balancing of Electric Buses’ Charging Operations in Public Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Sebastian Naumann and Christian Hübner Evaluation of Alternatives for Realization of Opportunity Charging at Transit Stops by Analyzing the Power Grid . . . . . . . . . . . . . . . . . . . . 125 Olaf Czogalla and Sebastian Naumann vii

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Application of Lithium-Ion Battery Thermal Management System in Electric Vehicle Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Feng Xie, Olaf Czogalla, and Huaiwei Shi Case Study and Cost Analysis of a Bus Fleet Electrification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Agnieszka Tubis, Sylwia Werbińska-Wojciechowska, Maria Skrętowicz, Zbigniew Sroka, and Joanna Świeściak Look into the Future of Public Transport—Selected Issues Cybersecurity in Electric Bus Public Transport Systems . . . . . . . . . . . . 169 Sylwester Markusik and Aleksander Bułkowski Autonomous Bus Fleet in the Context of the Conventional-to-Electric Fleet Conversion Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Krzysztof Krawiec and Marcin Jacek Kłos How to Connect Hyperloop Technology with the Smart City Transportation Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Arun Kumar Yadav and Janusz Szpytko

Transport Policy on Electromobility

Electromobility in Smart Cities Krzysztof Patola and Janusz Szpytko

Abstract The authors intended to present in a condensed form the vision of transport in cities of the intelligent type using electricity. The history of electric car development in the sense of ecology and evolution of the automotive industry was presented, as well as the evolution of the car in terms of new utility functions obtained as a result of using, among others, electronics, vehicle communication with the global network (environment), augmented reality. As a result, the concept of an autonomous vehicle was included in the concept of an intelligent city, where an intelligent electrical network is an important source of energy. There was also a debate on the concept of transport in smart cities. Keywords Electromobility


Electric vehicles
Smart cities

1 Introduction An intelligent city is a concept of ecological space, which, as a result of the technology used (in particular in terms of connectivity between elements of the structure and the use of artificial intelligence in decision-making processes), combines the possibility of professional (useful) work with the expected standard of human life. An important element in smart cities is safe and environmentally sustainable transport using so-called green energy sources. The concept of urban transport, in particular developed in recent years, is focused on electromobility. Integrated public transport using means of transport with an electric power source, in particular electric cars, is a priority in an intelligent city. The car, perK. Patola (&) Eurodiagnosta, PL 32-095 Krakow, Poland e-mail: [email protected] J. Szpytko AGH University of Science and Technology, PL 30-059 Krakow, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_1

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ceived as the property of natural persons or companies, will be gradually replaced by shared (shared) use systems, in which vehicles are made available by fleet operators for means of transport (Car-Sharing Concept). In the future, most of the means of transport in cities will be autonomous vehicles. Car manufacturers are already presenting the concepts of such vehicles. An example is the autonomous MINI Vision Next 100 [1] promoted as a revolutionary personalization of the car combined with its joint ownership system. This study presents selected stages of technology development focused on the safety, environmental friendliness and autonomy of cars as well as concepts for the integration of transport systems with smart city systems.

2 The History of an Electric Car At the beginning of the twentieth century, battery-powered electric motors were preferred to drive cars, providing comfort and ease of use that the gasoline engines of that time could not achieve. The production of electric vehicles reached the highest level at the turn of the nineteenth and twentieth centuries. The development of electromobility was accelerated by new batteries that were invented at the time. In 1901, Thomas Edison patented and introduced Ni-Fe (nickel-iron battery) batteries to the market. Soon, these batteries were a source of energy for electric vehicles. During this period, more electric cars than gasoline cars were driving the streets of major US cities. In 1876, Nikolaus Otto designed and constructed an internal combustion engine that caused the rapid development of the automotive industry: electric cars were no longer so readily bought. In 1908, the Ford Model T car was created. The car was mainly targeted at less affluent customers. This is how the era of wheeled vehicles (cars) produced on a mass scale began. The development of vehicles with internal combustion engines and the low price of oil and gasoline (compared to the price of electricity and its availability) led to the almost complete collapse of the concept of electric vehicles. The main reason for the displacement of electric vehicles from the beginning of the twentieth century were technological limitations enabling the development of electric drives and the adopted strategy and business model of the automotive industry [2–4].

3 Ecology and Automotive Industry The development of the automotive industry has caused an increase in air pollution (smog), especially in large cities. Cars emitting nitrogen oxides, sulfur, ozone and other oxides in the combustion process have a significant share in the formation of intense air pollution.

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Deterioration of the environmental conditions and life of residents in large urban agglomerations, climate changes that have occurred and observed, and in addition ecological activities are now resulting in a return to the concept of an electric car as a means of transport. The car ceases to be the best means of transport in cities. Lack of adequate parking spaces, separate zones of ecological transport, as well as closing cities for vehicles emitting pollution results in greater interest of buyers of new cars in low or zero emission vehicles. In 1967, the California Air Resources Board (CARB) was established in California [5]. CARB has contributed to the development of innovation in the global automotive industry through environmentally oriented projects, for example ZEV (Zero Emmision Vehicle) mandate, OBD (On-Board Diagnostic) and others. In 1990, regulations were issued ordering the sale of zero-emission ZEV type vehicles. The result of these regulations are new ZEV car designs, the operation of which should result in an environmental reduction of air pollution. The dynamic development of cities results in the need to search for solutions focused on effective human transport processes and necessary resources, with acceptable properties in terms of operational safety and reliability as well as environmentally friendly (means of transport, infrastructure, environment).

4 Electronics in the Automotive Industry (Selected Issues) The first transistor was constructed in 1947. Soon it was used in the automotive industry to build electronic ignition systems. The next step was to use the microprocessor as a control unit in the electronic fuel injection system. The traditional high-current electrical installation was too prone to failures, it did not allow computer diagnostics of devices located in different parts of the vehicle, so it was necessary to integrate them into one efficient and reliable system. As a result, BOSCH developed in 1986 a car digital CAN bus (Controller Area Network) [6]. Along with the development of technology, new solutions that affect safety were created, among others the anti-lock braking system (Anti-Lock Braking System), and AIRBAG airbags. More and more safety systems are used in modern cars, including: 1. ESP (Electronic Stability Program), vehicle stability system while driving. 2. ASR (Acceleration Slip Regulation), anti-slip system of the car’s wheels while driving. 3. BLIS (Blind Spot Information System), a system for detecting vehicles moving in the blind spot of the mirror. 4. TSR (Traffic Sign Recognition), road sign recognition system and car speed limits while driving. 5. TPMS (Tire-Pressure Monitoring System), car tire pressure monitoring system.

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6. LA (Line Assist), an assistant maintaining the correct lane for a car. 7. BAS (Brake Assist System), car emergency assistance system. A number of devices have been developed to improve driving comfort. A traditional radio receiver is replaced by multimedia systems equipped with driver support functions using cameras and GPS information (Global Positioning System). GPS was designed and manufactured by the US Department of Defense in the 1970s. Then it was improved and made available for civil applications. Currently, GPS navigation devices are commonly installed in most cars.

5 Vehicle Communication with the Global Network (Environment) Since 1996, General Motors has been developing a communication system with an On Star vehicle [7]. Two-way communication is carried out via a cellular network using a dedicated SIM (Subscriber Identification Module) card. Other car manufacturers use systems using the SIM card concept, but they differ in name (eCall Toyota, Opel) and functionality [8, 9]. On cars homologated in the European Union from April 2018, the On Star communication system is mandatory. The main task of the system is to ensure the safety of road users as a result of the generation of the SOS (Save Our Souls) rescue code. After the airbag deploys, the system automatically informs the services about the accident. The On Star system also has other functions: 1. Sends diagnostic information (including oil life, tire pressure). 2. Helps to locate the car after reporting its theft. 3. Remote control of vehicle functions (opening/closing doors or windows, activation of lights/sound signal and others). 4. It is possible to stop the vehicle remotely by the police. One of the new applications of the On-Star system is its use for the integration of electric and hybrid vehicles with Smart Grid type power grids. For example, electricity suppliers will use a Smart Grid system to remotely control battery charging (minimizing energy costs).

6 Augmented Reality (Selected Issues) Augmented Reality AR (Augmented Reality) is a system connecting the real world with a computer-generated. Manufacturers’ concept cars are equipped with on-board devices that use AR type augmented (virtual) reality technology, which adequately supplements the

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missing information about the road situation, while the intelligent driving system provides for possible solutions in the field of driving technique and/or route selection and other activities with decision-making processes. An example of an AR solution is the BMW concept with the digital Companion system. Companion is the future intelligent vehicle management system, supporting the driver and integrating vehicle safety systems. It is worth emphasizing that the information display function is performed by all vehicle windows.

7 Autonomous Vehicles (Selected Issues) The environmental friendliness of the car at the stage of production and operation, safety, operational reliability, striving to minimize the number and effects of road incidents (accidents and collisions), optimization of time and costs of the transport process are selected important requirements for means of road transport, in particular in cities. The dynamic development of automotive technology means that visions in the field of transport processes become reality. The car takes over the role of the driver, who can rest during the journey or take care of other matters, and not focus on driving. Public transport is in the future a fleet of automatically guided vehicles and autonomous vehicles. Autonomous vehicles can travel by persons without a driving license (for example, children and people with disabilities) who are unable to drive independently. Traveling with such a vehicle can be more effective due to the optimal use of energy and the right adjustment of the car’s speed to the environmental conditions. We distinguish between vehicles with autonomous functions and fully autonomous vehicles. The first group includes vehicles in which the system takes over part of the driver’s tasks, the second group consists of vehicles fully controlled by the system and without the participation of people in such a vehicle. An autonomous car must make decisions using information on environmental conditions (on the road) and apply criteria for their assessment defined by vehicle system designers [6]. The participation of autonomous vehicles in road traffic involves the need to develop guidelines for various aspects of the safety of car steering systems. Important for the safety of autonomous vehicles is IT security regarding both driving control (control) and personal data, as well as protection against unauthorized use of the car. Eleven companies in the automotive and IT industries (Aptiv: previously Delphi Corporation, Audi, Baidu, BMW, Continental, Daimler, FCA US LLC, HERE, Infineon, Intel, Volkswagen) have developed and published (2019) a report on construction, testing and safe service autonomous vehicles (the so-called SaFAD White Paper, Safety First for Automated Driving) [10]. Five levels of vehicle autonomy have been defined:

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1. Level 0, Driver assistance systems, for example ESP (Electronic stability control), ABS (Anti-lock Braking System), tempomat (Cruise Control). Many car models have such systems. 2. Level 1, Automation of one element required to drive a vehicle, for example: maintaining a constant distance from the vehicle ahead. 3. Level 2, Semi-autonomous driving: The vehicle controls the direction of travel, is responsible for the lane, acceleration, braking and steering, controls the distance from preceding vehicles (for example, driving on a highway). 4. Level 3, The computer takes control of the vehicle as in level 2 (for example when driving on the highway) and in simple traffic situations in the city. The driver only deals with difficult and complicated situations when driving in the city. 5. Level 4, Essentially fully autonomous driving: The driver does not need to drive the vehicle during complex maneuvers in the city and any other. The vehicle has manual steering devices (steering wheel, joystick) that are used at the request of the driver (wanting to drive himself). 6. Level 5, Driving a car fully autonomous, the vehicle has no manual steering equipment. For example, under an agreement signed in 2019 (Project Qatar Mobility), Volkswagen AG (owner of the SCANIA brand) launched a fleet of autonomous electric vehicles in the capital of Qatar, the city of Doha in 2022 [11]. An integrated sensor system allows the vehicle to scan the entire surroundings and obtain a 360 degree view. An important feature of autonomous vehicles is their modular design, which consists of the electronic part and systems of individual devices, as well as network devices and systems used for communication with the environment and data transmission. An important element of autonomous vehicles are vehicle database systems, use and maintenance processes, environmental conditions accompanying the use process (for example: driving history) and others. In addition, the databases are supplemented and analyzed by a decision system to better understand the environment in which the car is used and to improve the construction and operation process. The traditional service can be partially replaced by a remote system upgrade. Modernization of autonomous cars will be implemented primarily by updating the necessary software installed.

8 Autonomous Vehicles in an Intelligent City Currently, large cities must limit car traffic due to air pollution and lack of parking spaces. City centers are closing, the organization of the car movement process is changing, creating special zones of ecological transport. Combustion vehicles give way to low and zero emission ecological cars.

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Eco-friendly vehicles have special privileges in cities: they can travel on designated lanes for public transport buses and use free parking spaces in so-called paid parking zones. There are also a number of benefits of introducing eco-friendly vehicles. For example: the costs of transport with electric vehicles are much lower compared to inflexible traditional solutions. All these conditions make it necessary to change the management of the communication system in cities. The concept of modern environmentally sustainable city management with the use of intelligent decision systems that use information and communication technologies (IT) to increase the interactivity and efficiency of infrastructure is called the Smart City. Transport systems are an important element of smart cities. Vehicles in an intelligent city [12– 15] are connected wirelessly using various technologies, exchange information and use information shared, among others, from sources such as: satellite navigation systems, road databases, road signs and own and external sensors, cameras and more. Fleets of vehicles transporting people and goods monitor integrated logistics centers. Integrated public transport using electric vehicles is a priority in an intelligent city.

9 Car Power Supply: Intelligent Electrical Network Electricity will be supplied to the city structure using the Smart Grid. An important element of intelligent electrical systems are energy sources and the technique of electricity distribution. Electromobility changes the face of the city forcing urban and logistic changes, changes in the habits of residents, but at the same time, it contributes to improving the quality of life and air cleanliness. It allows you to reduce costs and save time. Electric vehicle batteries become a mobile storage of energy from renewable sources installed in the city (for example solar panels mounted on the roofs of buildings or facades). The two-way energy flow in an electric car is used to power the building. The energy storage system using the vehicle’s battery allows you to balance peak energy costs and also enjoy the additional security of an available backup energy source during power outages. The issue of the disposal of used batteries is another important ecological issue [16]. The percentage of battery capacity utilization is calculated based on an analysis of driving history and driver habits. The energy supplier can use the available battery capacity of the vehicle to optimize costs. This allows you to balance energy supply within the building community in a smart city. An intelligent power grid in cities enables the communication between all entities involved in the processes of distribution and energy consumption. This allows for dynamic network management. An important issue is to ensure network security in the aspect of continuous energy supply at a constant level.

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The Concept of Transport in Smart Cities

Initially, the Internet of Things (IoT) was called a network of interconnected computers, then the concept of a network of different devices was created, followed by the Internet of Everything (IoE). The concept of the Internet of Everything IoE enables the construction of a system of intelligent cities, with a structure of intelligent buildings, communications and infrastructure, among others. Individual car transport (especially in strict city centers) is replaced by intermodal (combined) means of public transport (electric car, scooter, scooter, other means of transport) ordered via the application on smartphones. Internal combustion buses running on designated routes are replaced by vehicles as well as electric buses and other means of transport (in the future also autonomous ones). Traditionally, public transport is gradually being replaced by more flexible on-demand transport. Such solutions do not have a fixed timetable, vehicles adapted to the individual needs of customers are used for transport, and stops are replaced by pickup points of passengers available according to notifications. Transport of goods, parcels, deliveries to shops and restaurants are carried out by electric vehicles and devices. Light packages are delivered using drones. Municipal cleaning services use electric sweepers remotely controlled by an integrated city cleaning system. The vehicles have autonomous functions. Traditional waste disposal using electric vehicles will eventually be replaced by another solution. Waste will be transported via the underground system to the development center (recycling, eco-incineration), and huge noise-giving garbage trucks will disappear from city streets. For electric vehicle owners, the city’s infrastructure provides the possibility of supplementing energy. The vehicle navigation system indicates free parking spaces that can be charged (often free). However, such places are deliberately located in some distance from the city center. The transformation of unused roof space into solar power plants is a direction for the development of modern cities. New buildings must have solar installations, according to building regulations. However, the growing number of distributed sources of electricity causes disturbances in the city’s energy balance.

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Examples of Vehicles for a Smart City

At the beginning of 2011, the German car manufacturer [17] presented a project of the electric car of the future called the vision and reality of the MCV (Megacity Vehicle). BMW engineers have developed the LifeDrive concept, which consists of two independent modules: Drive and Life. The Drive module (chassis) integrates the battery, drive system as well as structural and crash functions into one structure. The Life module consists primarily of a very durable and extremely light passenger compartment made of carbon fiber-reinforced plastic. The car was launched in 2013

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as part of the created BMWi brand (BMW i3). The BMW i3 type is the most commonly used means of transport in car services per minute (Carsharing) [18]. As a result of cooperation between the American manufacturer and the Japanese Honda, the vehicle of the future (Cruise Origin General Motors) is created, which will first hit the streets of San Francisco as a minibus in the ridesharing service. Ridesharing is sharing one car (e.g. on the way to work) with strangers on board. The manufacturer’s motto is: We didn’t just want to improve the car. We wanted to change the image of transport as if the car never existed.

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Final Remarks

Cities are places of meeting, creativity and innovation as well as centers of various production and services. Due to the population density of cities, they have great potential for energy savings and transformation opportunities towards a low-carbon economy, in particular a sustainable economy. The development of sustainable mobility is the most important challenge for cities of the future. Electric vehicles from the beginning of the 20th century were displaced by combustion vehicles. After over a century, they return to the automotive market as a result of the development of new technologies. The development of battery technology and propulsion control has meant that currently, electric cars meet the requirements of customers and the environment: it is possible that in the future they will replace combustion solutions as an energy source while using the existing known achievements in the field of automotive technology. Solutions in the field of safety systems and comfort of use, implemented and tested during the lifetime of combustion vehicles, combined into one electric vehicle system, will in the future be part of an integrated sustainable transport system in smart cities. It can be argued that in the future electric autonomous vehicles will be used in cities. It is possible that the use of autonomous electric vehicles over long distances will be possible. Soon, the car will recognize and transport each resident of the city safely to their destination as desired.

References 1. Vision vehicles and concept cars. https://www.mini.com/en_MS/home/automotive/conceptvehicles/next-100.html 2. EV1 Samochód, który wyprzedził swoje czasy o 20 lat. http://www.eurodiagnosta.pl/active/ activeEV1.html 3. General motors and honda to jointly develop next-generation. https://media.gm.com/media/ us/en/gm/news.detail.html/content/Pages/news/me/en/2020/gm/094-09-General-Motors-andHonda-to-Jointly-Develop-Next-Generation-Honda-Electric-Vehicles-Powered-by-GM-sUltium-Batteries.html

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4. Getting ready to operate the smarter grid. https://www.smart-energy.com/features-analysis/ getting-ready-to-operate-the-smarter-grid/ 5. California Air Resources Board. https://ww2.arb.ca.gov 6. Magistrala CAN—mózg współczesnego auta. https://motointegrator.com/pl/pl/poradniki/ technika/magistrala-can-mozg-wspolczesnego-auta 7. On star. https://pl.wikipedia.org/wiki/OnStar 8. System eCall gdy liczy się każda sekunda. https://www.toyota.pl/innovation/ecall 9. The Cruise Origin Story. https://medium.com/cruise/the-cruise-origin-story-b6e9ad4b47e5 10. Automotive and mobility industry leaders publish first-of-its-kind framework for safe automated driving systems. https://www.press.bmwgroup.com/global/article/detail/T0298103EN/ automotive-and-mobility-industry-leaders-publish-first-of-its-kind-framework-for-safe-automateddriving-systems 11. Scania. https://www.scania.com/group/en/home/newsroom/news/2019/how-scanias-nxtcommunicates-with-passengers-and-those-around.html 12. Inteligentne miasta przyszłości. https://www.bosch.pl/internet-rzeczy/local-dom/local-domartykuly/local-miasta-przyszlosci.html 13. Miasta przyszłości: Wyzwania, wizje, perspektywy. https://ec.europa.eu/regional_policy/pl/ information/publications/reports/2011/cities-of-tomorrow-challenges-visions-ways-forward 14. Miasto przyszłości. https://builderpolska.pl/2018/02/27/miasto-przyszlosci/ 15. Największe miasta świata, ranking 2019. https://inzynieria.com/budownictwo/rankingi/ 48810,najwieksze-miasta-swiata-ranking-2019,pozycja-rankingu-zestawienie-30-najwiekszychmiast-swiata 16. Battery storage system electrified by BMW i announced at EVS29 in Montreal. https://www. press.bmwgroup.com/usa/article/detail/T0261314EN_US/battery-storage-system-electrifiedby-bmw-i-announced-at-evs29-in-montreal?language=en_US 17. BMW historia samochodów elektrycznych BMW. http://www.eurodiagnosta.pl/testujemy/ article_d/index_bmw_i_history.html 18. Car sharing—BMW i3. https://bezpiecznapodroz.org/2019/05/04/car-sharing-bmw-i3/?gclid= CjwKCAjwrcH3BRApEiwAxjdPTRzMj9clfg6kBjpCuImLEkLEzBq_Go4iGD-auhOCz6XA 6YlmW-KMxRoCPAAQAvD_BwE

E-Mobility as a Concept of Public Transport Development in Poland Ewelina Sendek-Matysiak

Abstract Transport, including agglomeration transport, is one of the most important factors determining economic development. Developed and effective broadly understood transport infrastructure, i.e., both road and transport means contributes to strengthening social, economic, and spatial cohesion and contributes to the increase of competitiveness within the global economy. However, the development of the infrastructure referred to also has negative effects, such as high emissions of noise, dust, carbon dioxide, and other gases harmful to health and the environment. Achieving a balance between the ability of transport to serve economic development and respect for the natural environment and preservation of quality of life in the future, therefore, requires changes in the approach to public transport. This can be done, among others by increasing the number and share of passengers using public transport as well as by investing in low- and zero-emission vehicles: hybrid, gas and electric. Due to the fact that buses are currently the most popular and the most commonly used means of public transport, the present state of electromobility in Polish cities is shown, with an indication of electric buses and the most important factors stimulating its development. Keywords Electric bus


Electromobility
Transport policy
Public transport

1 Introduction Cities are the main hubs in national and regional transport networks. Sources and destinations focus on relatively small areas, and as a result, the streams of passenger and freight traffic also cross. Every day, a significant number of people, whether in connection with professional activities, living matters or other individual travel

E. Sendek-Matysiak (&) Faculty of Management and Computer Modeling, Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25314 Kielce, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_2

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needs—travel from the origin (most common: places of residence) to the goal of a specific activity (e.g. work, school, place of provision of different types of services). In addition, in urban areas, various types of economic activities focus. In addition to employee travel, they also generate significant flows of goods (materials necessary for production and ready goods for the market). According to some experts, the importance of transport in modern cities was to begin to decline. It was assumed that along with the development of communication technologies and an increase in the popularity of remote work, the need for mobility will be reduced. To date, however, it is difficult to notice this type of trend in Polish cities. On the contrary, the level of urban mobility is increasing. Locating many commercial, industrial and warehouse facilities on the outskirts of large cities means that many employees commute to them from the city/central area, while a large group of people move from suburban areas. Suburbanization has led to a situation in which the distances between home, work and other basic needs have increased significantly. What is more, along with the economic development and enrichment of the society, the range of activities undertaken by residents has also increased. It also generates additional needs related to moving in urban space. The constant increase in the transport needs of residents and the increasing number of vehicles traveling on urban roads caused excessive congestion, increased emissions of exhaust gases, noise, air pollution and, as a consequence, a decrease in the quality of life. Numerous economic studies show that the cost of congestion to society is high (in the European Union it is estimated at 270 billion euros per year [1] and that there is a direct relationship between urban traffic flow and the projected economic growth of these areas. In [2] it has been shown that in areas with high traffic congestion, smoother traffic could increase employee productivity by as much as 30%. In addition, road transport, among various anthropological departments, is the second largest source of greenhouse gas (GHG) emissions, accounting for about a quarter of their emissions (Fig. 1) [3].

Fig. 1 Greenhouse gas emissions by IPCC. Source Sector, EU-28, 2017 [4]

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It is the largest source of emissions of nitrogen oxides (NO and NO2) (Fig. 2a) and the second-largest source of carbon monoxide (Fig. 2b) and particulate emissions (Fig. 2c). In the European Union, 23% of CO2 emissions from transport come from urban areas [5]. In Poland, according to data from the National Center for Emissions Balancing and Management, road transport is responsible for 7.3 % PM2.5 emissions, 4.9 % PM10 emissions and 31.8 % NOx emissions [7]. It may not seem like much in the overall statement, but in individual cities and agglomerations the actual share of communication may be much higher, e.g. in Warsaw, 60–80% of pollution is emissions from road communication [8]. According to the World Health Organization, as many as 33 out of 50 EU cities with the highest PM2.5 concentrations are located in Poland (Fig. 3). In addition, transport is significantly responsible for the deterioration of the “acoustic climate”. According to the European Union report [10], about 40% of the European population is exposed to traffic noise above 55 decibels, while 20–30% to levels above 65 decibels during the day and 55 decibels at night. Figures 4 and 5 demonstrate the approximate number of people exposed to noise at a harmful level. Currently, due to excessive noise, at least 10,000 Europeans are dying prematurely [12], a cost resulting from the impact of road noise on public health is estimated at EUR 40 billion per year [13]. In the era of common communication problems in cities, public transport is a means of transport that can help organize a sustainable transport system that meets the needs of local communities related to movement. As noted in [14], an urban environment public transport is much more efficient than individual vehicle communication. This is due to its lower terrain, emissivity and energy consumption per user [15]. In addition, it should be noted that the last two parameters are constantly improving due to the implementation in cities of increasingly modern rolling stock that meets cyclically raised and updated environmental standards.

2 The Vehicles with an Electric Motor in Public Transport One of the solutions enabling a direct increase in the environmental efficiency of public transport is basing the fleet on zero-emission (at the place of use) electric motor buses, which include battery (battery electric bus), hydrogen vehicles and trolleybuses [16].

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Fig. 2 Emissions by IPCC. Source Sector, EU-28 a NOx; b CO; c PM2.5 [6]

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Estimated number of people (in millions) exposed to day-time noise ≥55 dB

Fig. 3 50 most polluted cities in the European Union in 2017 [9]

80 urban agglomeration

60

other

40 20 0 roads

railway

airports

industry

major roads

major railways major airports

Fig. 4 Number of people exposed to day-time noise (Lden)  55 dB in EU-28 in 2017 [11]

E. Sendek-Matysiak

Estimated number of people (in millions) exposed to nighttime noise ≥50 dB

18 60

urban agglomeration

other

40 20 0 roads

railway

airports

industry

major roads major railways major airports

Fig. 5 Number of people exposed to night-time noise (Lnight)  50 dB in EU-28 in 2017 [11]

2.1

Hydrogen Buses

A hydrogen bus is an electric vehicle where the electricity needed to travel is not supplied from the battery but from the combustion of hydrogen, which makes it completely zero-emission (the only by-product is steam). In addition, the advantage of using this type of vehicle is a short refueling time that lasts only a few minutes and a range of approx. 450 km on one refueling [17, 18] which is even more than the need for a public transport vehicle in one-day operation. The factors limiting the use of hydrogen buses are the very high price of the fuel cell system and the low availability of hydrogen of adequate purity—class 5. An example of these restrictions may be the fact that currently there is no hydrogen refueling station for buses in Poland [19].

2.2

Trolleybuses

The trolleybus is powered directly from the catenary to which it is connected during operation. Despite several advantages i.e.: • Favorable movement properties—trolleybuses are characterized by greater acceleration than buses (which results in a higher average speed in urban areas), better hill climbing ability, shorter braking distance compared to trams (resulting from better wheel grip) and a smooth change speed (no gear shifting required in the gearbox), • No emissions of harmful substances to the atmosphere, • The possibility of using energy from renewable energy sources, • Lower noise emission by the electric motor, • Use of energy recuperation during braking, • Low floor combined with simplification of the engine mechanism, • Lower vehicle weight compared to buses, • Compared to trams - greater mobility (no “sticking” to the track - trolleybus can bypass obstacles on the route, leaving the traction network for up to 2–3 m),

E-Mobility as a Concept of Public Transport Development Fig. 6 Trolleybus numbers in Poland in 2018 (own study on the basis of [22])

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21 Lublin Gdynia 120 90

Tychy

• Modern equipping modern trolleybuses with energy storage, thanks to which in case of network damage they can move (with limited speed and for some time) without external power supply, • Lower cost of building a trolleybus infrastructure compared to a tram infrastructure, • Lower operating costs of trolleybuses compared to buses (no complicated and unreliable components) [20] Trolleybus communication is quite unpopular - it functions only in three urban centers: Gdynia, Lublin and Tychy (Fig. 6) and so far, this public transport mode is not expected to be introduced in other cities in the country even despite the possibility of subsidizing expansion, development and modernization of the trolleybus network from the European Union budget [21].

2.3

Battery Electric Buses

Battery vehicles are currently the most common versions of electric controllers that they are beginning to use in Polish cities. Such buses, although more expensive to buy than ordinary combustion transport (currently about 2–3 times), make a relatively cheap introduction to urban transport. It does not require one very expensive investment in the development of linear infrastructure, but only a point investment in the charging infrastructure. Electric buses are less structurally available in vehicles than their counterparts with ICE engines, which includes less failure and the cost of spare parts and repairs. W [23] estimated the cost repaired, during the 15-year period of using a bus equipped with 110.5 thousand PLN and 221 thousand PLN in the case of a bus with a diesel engine. In addition, there are a lot of costs associated with “refueling” of such vehicles, which can be up to four times smaller than in the case of a combustion bus [24]. From the environmental point of view, the great advantage of electric buses is primarily the lack of emissions associated with the combustion of solid fuels at the

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place of their operation. The electric drive does not produce hydrocarbon compounds or solid particles [25], which can significantly contribute to reducing emissions along communication routes. It should be added, however, that zero-emission electric buses are not total. There will still be emissions related to the wear of brake pads or tires and the re-entrainment of dust from the road surface. Although these vehicles use energy, which in Polish conditions arises mainly in coal-fired power plants (Fig. 7), it is assumed that they will be charged primarily at night, i.e. use the part of the power plant’s production that is lost today. In addition, power plants that are moved away from home can be better equipped with dust extraction and exhaust purifying installations than the engine installed in the vehicle. The EU IED directive from 2010, which entered into force in 2016 and covers all installations with a fuel capacity equal to or greater than 50 MWt [26]. The directive provides for limiting emissions of harmful pollutants into the atmosphere—sulfur dioxide, nitrogen oxides and particulates of high combustion plants and will be in force from 2021 [27]. Undoubtedly, the advantages of bus services, the feeling of both residents and passengers of such vehicles is noticeably lower noise emissions. Already during the movement of such a bus stop, the level of generated noise is 16% lower than a combustion bus [29]. The noise that arises as a result of the electric bus moving in urban traffic reaches the level of 69 dB, when at a standstill approx. 63 dB and is mainly taken into account in auxiliary devices, including air conditioning. For comparison, a bus with a diesel engine emits around 77 dB in city traffic instead of 80 dB at a standstill [25] (Fig. 8). However, according to American studies, during which the noise levels of accelerating buses from 0 to 56 km/h were tested, the noise emissions for electric vehicles were 62.8 and 65.5 dB, respectively, for diesel—72.0 and 71.2 dB, and for CNG buses—74.7 and 73.9 dB. These are quite significant differences because already the 6 dB difference is perceived by the human ear as a 75% reduction in noise level.

RES 12.70%

Other industrial 2.80%

Pumped storage 0.20% Natural gas 7.50%

Lignite 29%

Fig. 7 Structure of electricity production by energy carrier [28]

Hard coal 47.80%

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120 100

dB

80 60 40 20 0 Electric bus

Hybrid bus

Bus with internal combustion engine

Fig. 8 External bus noise emissions according to Boren et al. [30]

Lower noise levels also mean greater comfort inside the bus, especially around the engine. According to [29], the noise level in the back of the bus is 28% lower than for combustion buses. The use of an electric motor further increases travel comfort by reducing vibration. In electric buses, the reduction of vibration on the driver’s seat is 76% compared to a combustion bus [29].

3 Current State and Development Process The most popular and commonly used means of public transport in Poland is a bus (Fig. 9). In 2018, the fleet of city buses in Poland had 12,058 vehicles (Fig. 10), which constituted 78% of their share in the entire public transport stock in the country. In 2019, 1.63% of all buses in Poland were electric [31], i.e. 216 vehicles that were operated in 25 cities (Fig. 11). It is expected that the number of such vehicles in Poland will grow. This will result, among others in the implementation of specific solutions imposed in the Act of 11 January 2018 on electromobility and alternative fuels [33], which implements the provisions of the Directive on the development of alternative fuels infrastructure in 2014 to Polish law [34]. According to the provisions of art. 36 paragraph 1 and in connection with art. 86 point 4 of that Act, from 1 January 2028 a local government unit, with the exception of municipalities and districts whose population does not exceed 50,000, will be able to provide or commission the provision of public transport service to an

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2017

Trams

2016

Trolleybuses

year

2015 2014 2013 2012 2011 2010 2009 0

20

40

share

60

80

100

Fig. 9 The share of bus, tram and trolleybus lines in public transport lines in Poland in 2009– 2018 (in percent) (own study by [22])

12,000 10,000 number

Buses 8,000

Trams

6,000

Trolleybuses

4,000 2,000 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 year

Fig. 10 Rolling stock of public transport in Poland (own study by [22])

entity whose share of zero-emission buses in the fleet of vehicles in use the area of this local government unit is at least 30%. One of the obligations provided for in the Act regarding organizers and operators of public collective transport is the requirement for local government units, referred to in Art. 36 paragraph 1 (i.e. local government units, excluding communes and districts whose population does not exceed 50,000), the share of zero-emission buses in the fleet of vehicles used, respectively:

45 40 35 30 25 20 15 10 5 0

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43

31 26

24

16 10 10 10 10 5

4

3

3

3

3

2

2

2

2

2

1

1

1

1

1

Zielona Góra Warszawa Kraków Jaworzno Poznań Inowrocław Stalowa Wola Szczecinek Rzeszów Katowice Ostrów Wlk. Bełchatów Sosnowiec Środa Śląska Włocławek Osrołęka Łomianki Polkowice Lublin RCKiK Katowice Ostróda Chodzieź Ciechanów Wągrowiec Września

number

E-Mobility as a Concept of Public Transport Development

city

Fig. 11 Number of electric buses in Poland in 2019 [32]

1. 5%—from January 1, 2021; 2. 10%—from January 1, 2023; 3. 20%—from January 1, 2025. To support local governments in acquiring electric vehicles, in 2017 a coordinated by the Polish Fund Development of the E-Bus program was launched, thanks to which it will be possible to finance rolling stock and charging infrastructure for electric vehicles in Polish cities [35]. In the program declared 45 communes their participation, which indicated a total willingness to purchase over 800 electric buses by the end of 2020, which will constitute about 7 % bus fleet in relation to the number of all city buses in Poland (Table 1). On the other hand, as part of the zero-emission Public Transport program run by the National Center for Research and Development, 26 cities and the Metropolis of Upper Silesia and Zaglebie concluded an agreement with the National Center for Research and Development (NCBiR) for the delivery of ready vehicles. These units declared a total of 507 electric buses to be purchased by the end of 2020 and 1500 to 2023 [36].

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Table 1 Communes and the number of electric buses in the E-Bus program Commune

Number

Commune

Number

Commune

Number

Brzeziny Bydgoszcz Ciechanów Czechowice-Dziedzice

2 20 8 4

Katowice Koło Kraków Lubin

20 1 100 10

3 15 16 5

Głowno Gmina Gorzów Wielkopolski Grudziądz

2 3 8

Lublin Łomża Nowy Sącz

66 10 4

Grudziądz Gmina

4

5

Inowrocław Izabelin Jasło Jawor Jaworzno Jerzmanowice-Przeginia Kalisz

15 0 5 4 20 4 15

Ostrów Wielkopolski Płock Polkowice Pomiechówek Poznań Rzeszów Siedlce Sokółka

Sosnowiec Stalowa Wola Szczecin Środa Wielkopolska Świdnik Tczew Tomaszów Mazowiecki Toruń

11 10 8 15 60 10 6

Tychy Warszawa Wrocław Września Zakliczyn Zielona Góra Żywiec

10 10 10 50 2 120 50 16 2 47 13

4 Summary The large-scale implementation of electric buses in public transport is undoubtedly important to reduce dust and gas emissions and noise, as well as to increase the comfort of public transport passengers, both those in the vehicle near buses (at stops) and residents. They can be used primarily for routes with dense, downtown buildings, to operate night lines leading near green areas or facilities requiring silence, e.g. schools or hospitals. Despite so many advantages, currently, the number of electric buses operated in public transport is small. The main barrier constituting a small share in the electric bus market is the price of electric buses, which are 150–250% more expensive than diesel buses. Additional costs are also generated by the infrastructure development dedicated to such vehicles, which in the cheapest version (wired charging in the depot) costs about 200 thousand PLN. In addition, buses are charged at significantly higher power than passenger vehicles. Therefore, along with the development of the electric bus fleet, in addition to the construction of new charging stations, investments related to connecting new chargers to the network as well as the development of power networks at various voltage levels are also necessary. On the side of the distribution system operator, this process will be combined with the analysis of

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potential customers in terms of meeting the relevant quality criteria and not causing interference in the power grid. Another obstacle to the development of the market for such vehicles in Poland is the attitude of public administration employees, which is associated with the fear of introducing changes that e-mobility technology brings with it. In addition, administrative staff often lack basic knowledge in the field of electromobility. Resistance to investing in electric buses also results from the belief that this is a relatively expensive investment. Policy makers often do not know the calculations for the cheaper operation of these vehicles, as well as other benefits associated with their use, such as the lack of direct emissions or noise, and public support for the development of electromobility. Therefore, for electric buses to become the basic type of vehicle in urban transport companies, they should be: • Cheaper than currently used diesel vehicles • Better perceived than currently used diesel vehicles • Stronger supported by the state. Only the implementation of these three factors at the same time will affect the decisions of local governments to invest in electromobility.

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13. European Commission: Report from the comission to the European Parliament and the Council on the implementation of the Environmental Noise Directive in accordance with Article 11 of Directive 2002/49/EC. COM(2011) 321 final (2011) 14. Newman P, Kenworthy J (1999) Cities and sustainability: overcoming automobile dependence. Island Press, Washington 15. Bannister D, Stead D, Steen P, Åkerman J, Dreborg K, Nijkamp P, Schleicher-Tappeser R (2000) European transport policy and sustainable mobility. E&FN Spon, London 16. Pikuła M, Piotrowski A, Sidorski F, Sierszyński M (2018) Autobusy napędzane silnikiem elektrycznym w zeroemisyjnym transporcie publicznym. Pozn Univ Technol Acad J Electr Eng 95:287–297 17. Stempien J, Chan SH (2017) Comparative study of fuel cell, battery and hybrid buses for renewable energy constrained areas. J Power Sour 340:347–355 18. Wielgus J, Kasperek D, Małek A, Łusia T (2017) Development generations of ursus electric buses. Autobusy Techn Eksploat Syst Transport 11:18–23 19. wysokie Napięcie.pl. The road to hydrogen cars in Poland. https://wysokienapiecie.pl/17436samochody-wodorowe-w-polsce-ceny-tankowanie/ 20. Pawełczyk M (2011) Trolleybuses—as a favorable alternative to public transport. Autobusy Tech Eksploat Syst Transport 12:253–255 21. Hebel K (2012) Directions of trolleybus communication development in the light of marketing research results in Gdynia, Logistyka, vol 3, pp 787–792 22. The Main Satistic Office. https://stat.gov.pl/ 23. MPK Tarnów: The methane or the current? Gas buses are an alternative to electric ones. The raport. http://www.mpk.tarnow.pl/pl/aktualnosci/2017-06-30/ promujemy-najlepsze-alternatywnenapedy 24. Transport Publiczny. https://www.transport-publiczny.pl/wiadomosci/jak-krakow-rozwija-swojaflote-autobusow-elektrycznych-58387.html 25. Grzelec K, Okrój D (2016) Perspektywy obsługi miast autobusami elektrycznymi na przykładzie Sopotu. Autobusy Tech Eksploat Syst Transport 11:26–32 26. Wilczyński M (2016) The EU directive on limiting industrial emissions and Polish coal Energy. http://www.chronmyklimat.pl/wiadomosci/polityka-klimatyczna/dyrektywa-ue-oograniczeniu-emisji-przemyslowych-a-polska-energetyka-weglowa 27. The Ministry of the Environment, The Guide for monitoring emissions, reporting and protecting the earth’s surface regarding integrated permits, Ministerstwo Środowiska (2020) 28. Rynekelektryczny.pl. https://www.rynekelektryczny.pl/produkcja-energii-elektrycznej-w-polsce/ 29. IMPACT REPORT. The Impact analysis of Solaris Bus & Coach S.A. on the economic, social and natural environment (2017). https://docplayer.pl/6198309-Poradnik-w-zakresiemonitorowania-emisji-sprawozdawczosci-i-ochrony-powierzchni-ziemi-dotyczacych-pozwoleozintegrowanych.html 30. Boren S, Nurhadi L, Ny H (2016) Preferences of electric buses in public transport; Conclusions from real life testing in eight Swedish municipalities. Int J Environ Chem Ecol Geol Geophys Eng 10:255–264 31. European alternative fuels observatory, AF market share new registrations M2 M3. https:// www.eafo.eu/vehicles-and-fleet/m2-m3 32. InfoBus. http://infobus.pl/ebus.html# 33. Dz. U. z 2018 r., poz. 317. http://prawo.sejm.gov.pl/isap.nsf/DocDetails.xsp?id=WDU20180000317 34. European Commission, Directive 2014/94/EU of the European Parliament and of the council of 22 October 2014 on the deployment of alternative fuels infrastructure. https://eur-lex. europa.eu/legal-content/EN/TXT/?uri=celex%3A32014L0094 35. The Ministry of Development. https://www.mr.gov.pl/media/20985/eBus_MJE_dobre.pdf 36. The Industrial Development Agency. https://www.pap.pl/centrum-prasowe/501873%2Carplaczy-sily-z-rafako-spolki-razem-wystartowaly-w-konkursie-ncbir.html

Determinants for the Effective Electromobility Development in Public Transport Barbara Kos, Grzegorz Krawczyk, and Robert Tomanek

Abstract Public transport is a basic instrument for sustainable mobility in urban areas. The core of public transport in cities is often electrified railway system, underground and trams. Trolleybuses are also widely used in many countries. Therefore, urban public transport is one of the most electrified transport subsystems. The process of implementing electric buses is currently seen in Poland, and its economic effectiveness depends, among other things, on the energy prices and energy mix of the country. Electric energy from non-renewable sources increases both own costs and external costs, which impacts the effectiveness of mobility. The chapter presents an overview of the directions of the European transport policy in the field of electromobility development. A particular emphasis has been put on presenting the problems of electromobility in Polish conditions, as well as systematizing the provision of strategic documents in the context of its implementation and development. The chapter also presents a TCO comparative analysis for the electric bus and combustion bus. Keywords Sustainable urban mobility


Public transport
Electric bus

1 Role of Public Transport in Sustaining Mobility Modern cities are facing mobility problems. Together with the population and spatial growth, the maladjustment of supply and demand quickly increases. The development of car transport is a source of negative external effects, and due to the limited capacity of transport infrastructure, it also becomes a bottleneck with regard B. Kos (&)
G. Krawczyk
R. Tomanek Department of Transport, University of Economics in Katowice, Katowice, Poland e-mail: [email protected] G. Krawczyk e-mail: [email protected] R. Tomanek e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_3

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to the fulfilment of mobility needs. Searching for alternatives to cars results in various innovative ideas, however, so far none of them has been able to change the fact that it is precisely public transport, existing for over 100 years and gradually pushed out by cars in the 20th century, which is a real and effective alternative to cars that are a source of transport congestion, greenhouse gas emission, as well as other external effects and costs. New innovative tools for fulfilling mobility needs shall be regarded as a supplement for public transport in sustainable mobility model. Cities have been the centers of civilization development for ages. The concentration of functions connected with power (including religious authorities), education, culture, as well as trade and craft, was related to increasing population and wealth produced and gathered in cities. Historic cities were so small that it was possible to move around them on foot without any mobility restrictions. The exceptions were empire capitals—for example, Rome, which, according to different estimates, could have had even up to one million inhabitants in the times of Julius Caesar and Augustus [1]. However, even in that case, people moved mostly on foot, and the rich were carried in sedans (for comfort and to separate themselves from the crowd). Carts would only pass through the city at night due to pedestrian congestion [2]. Paradoxically, Rome was the metropolis where the problem of mobility was solved in a sustainable way—by means of pedestrian movement. Obviously, to a large extent, this happened because there were no other technical capacities at that time (apart from carts, which were rejected for safety reasons, as we have seen). It was the growth of cities in the 19th and 20th centuries that allowed to use the fruits of technical revolution for transport development, which led to accelerated urbanization, because metropolitan railway, which initially used steam engine (London 1863) [3], caused a faster growth of cities. The development of cities meant the development of public transport, and then individual car transport. Based on the feedback effect, new transport solutions enabled even faster urbanization. While rural population dominated in the world until the end of the 20th century, currently (and in forecasts) these tendencies have been reversed. Already in 2016, the degree of urbanization for Asia amounted to 49%, whereas for North America, Latin America and Caribbean, it exceeded 80% [4]. There is a lot of evidence to suggest that these indicators will continue to grow—especially in the areas of lower urbanization (Asia and Africa). Not only is the increase of city size a challenge for the transport system. The structural changes which make the city different from what it was before are also important. Due to urbanisation, which is accompanied by the quality development of urban functions, city of the future will operate in a different manner than before. Because of the climate challenges, city of the future will have to be sustainable, i.e. the city where inhabitants’ needs are fulfilled without damage for future generations. The problem of uncontrolled (and therefore unsustainable) urban development has already been raised in 1987, in the so-called Bruntland Report. Since then, many initiatives have been undertaken in order to implement the idea of sustainable urban development. This approach has been presented in multiple European documents and declarations, especially in ‘Europe 2020’ strategy’ [5].

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European city of the future can be defined in different ways. The dominating terms to designate a city which is, to a certain extent, ideal [6], include smart city, intelligent city, knowledge city, sustainable city, talented city, wired city, digital city and eco-city. These concepts are based on the effects of applying IT and ICT technologies, as well as on the belief in the possibility to effectively implement complex urban and social functions thanks to ICT. The role of ICT in these concepts is also demonstrated by the definitions of the smart city [7]. In search of effective and efficient sustainable urban instruments, the aim should be to increase the value of social capital resulting from the use of knowledge gathered in the city area, as well as to develop the so-called creative class. One should also not ignore the capital connected with the operation of the existing urban infrastructure (especially public transport). The key component of a sustainable city is sustainable mobility. Apart from pedestrian crossings, the fulfilment of growing needs with regard to mobility operation also requires the development of the transport system. Mobility in modern cities is based on individual car transport, which is the source of high external costs. Therefore, the change in transport service model is the key challenge for sustainable urban development [8]. Mobility is a broader term than transport—it refers to any movements that are the effect of the distribution of traffic sources in space, as well as the result of specific operation and development of humankind when the movement was and continued to be a condition for development and survival of the humankind. Mobility should be regarded as an indication of individual freedom [9]. In particular, mobility includes pedestrian traffic. For the purpose of this chapter, the notions of mobility needs and transport needs will be treated as synonyms, which results from the practice of planning and managing sustainable urban development. Mobility in cities is intermodal, and specific methods for mobility execution are usually substitutional. Apart from pedestrian crossings, the most frequently mentioned methods for the fulfilment of mobility needs (further referred to as modal distribution of mobility) include cars, motorcycles, mopeds, public transport, bikes and modes of personal transport (i.e. scooters, skateboards). Particular categories of modal distribution are characterised by various effectiveness—depending e.g. on the journey length and its mass use. The car transport system is regarded as the least effective (due to external costs and land consumption). Its alternatives include public transport and bike transport (the latter for shorter journeys). In mobility sustaining mobility processes, particular attention is paid to zero emission bike and pedestrian system. Substitution between low-carbon public transport and zero emission urban transport systems can be regarded as undesired. There are more mutual relations, and the key challenge for sustainable urban mobility planning is the use of attributes of specific modal categories in order to replace substitutional transport solutions with complementary relations. The aim of activities undertaken by cities, and especially metropolises, is to increase the share of low-carbon and zero emission (pedestrian and bike) transport and public transport in fulfilling transport needs. According to research conducted by European Metropolitan Transport Authorities (EMTA) [10] in 25 metropolises

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in Europe and in Montreal, public transport has a share between 13% (Mallorca) and 47% (Warsaw) in all transport, whereas between 21% (Warsaw) and 66% people (Bilbao) move by bike and on foot, so the share of cars and motorcycles is between 12% (Bilbao) and 56% (Montreal). In metropolitan areas operated by public transport authorities associated in EMTA, the share of cars in fulfilling transport needs is higher (between 26% in Budapest and 69% in Montreal). Pedestrian mobility is widely accepted in the city, but in the metropolitan area, due to the distance of movements, it is replaced by cars and public transport [11]. When analysing the modal distribution, one should also take into account that pedestrian traffic is usually complementary to other modes of movement. In the case of greater distances, the only substitute for car is public transport (individually or as part of the intermodal journey). Substitutability of particular modal solutions is not full and its strength depends on spatial factors (influencing the length of the journey). In particular, there is a relationship between the population density and share of sustainable transport in fulfilling mobility—the higher the density, the bigger the share of sustainable mobility in modal distribution. A specific role in fulfilling mobility in metropolitan areas is played by public transport, which, thanks to its mass use, is capable of serving passenger flows in medium- and long-distance transport, and it might as well be a basis for intermodal transport systems that also use cars, bikes and pedestrian crossings, depending on the effectiveness of such modal solutions. While planning mobility solutions, one should take into account the secondary nature of transport needs in comparison with other needs. This means that the social, economic and spatial specificity determines the most effective mobility model in particular urban conditions. The authors of the article conducted research on transport behaviours and preferences of the creative class in the largest Polish agglomerations. The creative class sets trends in changing urban mobility models. It seems that representatives of this class share the values created by leaders of the new climate policy. Therefore, they can define tendencies consistent with the sustainable development paradigm, which will later be approved by entire urban communities [12]. Based on the survey concerning the mobility of the creative class in Polish metropolises, conducted by the authors in 2019 [13, 14], it was established that similarly as in the case of metropolises examined by EMTA, the creative class in Polish metropolises of Warsaw, Tricity (Gdańsk, Gdynia, Sopot) and The Metropolis of Upper Silesia and Zaglebie (in the area of Katowice; abbr. Silesian Metropolis) in modal distribution chooses mainly the car, whereas its alternative is public transport (Fig. 1). Based on this research, we can also determine the following directions for changes of mobility behaviours in urban and metropolitan areas: • Public transport is and will continue to be the basic instrument for fulfilling mobility needs in cities, and especially in metropolises, therefore, public transport development should be regarded as the main area of metropolitan sustainable mobility policy. • The role of bike in urban, intermodal mobility chains is mainly to serve the so-called “last mile”, and the development of bike infrastructure and

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Fig. 1 Modal distribution of creative class mobility in metropolis in Poland (2019) (own study)

bike-sharing should take these conditions into consideration. At the same time, the small share of bikes in mobility operation in many countries does not change the fact that the use of bikes has a beneficial effect on the quality of life in the city because it brings health benefits and promotes the development of leisure industry. • Individual electromobility, in a shorter time perspective, will not have a significant impact on mobility operation models; first of all, electric cars will not eliminate external costs such as congestion, land use and accidents, even if they achieve a significant share in the vehicle fleet. Sustaining mobility requires a systematic approach, always adapted to the local conditions [15]. Public transport is to play a special role, as it provides the possibility of mass use of transport, which is a specifically valuable feature in metropolitan conditions. Therefore, public transport is an effective alternative to individual car transport. The condition for withdrawal from car use in urban mobility is the increased availability of public transport services and intermodality allowing to increase transport availability and reduction of travel time. Restrictions on car movement without providing alternatives (mainly related to the offer of public transport services) are opposed by inhabitants, as we could see in the above-mentioned research on the creative class. The creative class also pays attention to travel costs, however, the key thing in assessing the possibility to shift to the public transport system is the improvement of service quality (improved public transport offer, increased frequency, improved availability)—see Table 1. This opinion means that sustaining mobility in cities requires significant investment

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and maintenance expenditure for the development and operation of public transport. In the case of many cities, it is a difficult project—in EMTA research, many cities have a high public subsidy ratio (above 50% of costs). However, the interesting thing is that the biggest public transport systems (such as London and Stockholm) live mainly on income from tickets [10]. The only Polish metropolis examined by EMTA is Warsaw, where an increasing number of initiatives for introducing free journeys (free fare public transport) have appeared in the recent years, with high subsidies at the level of 55%. At the same time, this system is characterized by one of the lowest ratios of public transport costs per inhabitant in Europe [10]. In this situation, the necessary expenditure for the improvement of public transport offer will be a great challenge for the city urban authorities. The share of bikes in fulfilling transport needs in European cities is not higher than between a few and several per cent (with the exception of Scandinavian and Dutch cities [16]). It seems that spatial factors are a sort of „glass ceiling” for the increase of this ratio, i.e. travel distance and time, which are usually stated as the causes of withdrawal from the use of car in urban travel (apart from the climate issues [17]). The readiness to use bikes in good weather conditions declared by respondents representing the creative class should be regarded as recognition of sport and leisure qualities of the bike (which is connected with a change in the leisure management model) [18]—see Table 2. The bike is very important for the improvement of social health [19]. European Cyclists’ Federation estimates the annual “bike” benefits in UE-28 (including external costs) at approximately EUR 513 billion [20]. Out of this amount, approximately EUR 191 billion is the effect of a positive impact of bike mobility on health. For this reason, the bike, together with pedestrian mobility and movement by boards, scooters, wheelchairs and ski desks, are considered as elements of the so-called active mobility or active transport—the which term refers to human-powered means of transport, whereas electrically powered means of transport (e.g. scooters) are regarded as examples of hybrid transport [21]. Active mobility and hybrid mobility inevitably bring significant health lifestyle changing benefits, and consequently become a factor in building the social capital of modern metropolises. In big cities, the importance of public bikes available based on collaborative consumption formula (bike sharing) is growing. Therefore, bikes, especially in the bike sharing formula, should be regarded as the so-called ‘last mile’ means of transport—bikes should increase the availability of public transport (including mainly rail transport). Such role of the bike will cause an increase of competitiveness of public transport, and it should be emphasized that the bike in mid-distance journeys can be an alternative to public transport. In such case, through a negative effect on the effectiveness of public transport system, it may deteriorate its competitiveness. In the second case, the offer may deteriorate or prices may increase, which would enhance the competitive position of car transport in substitutional long-distance journeys. Electromobility is regarded by the creative class as a rather unlikely alternative to traditionally-powered car transport. The respondents’ tendency to use the possibility of paid electric car rental is very low and negative responses dominate. The

18.8 14.6 23.6

19.0

25.4 13.6 28.9

22.9

Warsaw Tricity The Metropolis of Upper Silesia and Zaglebie Overall

Higher service frequency (%)

Ticket price reduction (%)

Scope of data

8.2

6.9 13.9 4.3

Building P&R car parks (%)

11.0

12.2 15.2 5.6

More convenient location of transport stops (%)

12.5

12.4 17.4 7.8

Comfort increase (%)

15.8

15.2 14.2 18.0

Development of connection network (%)

7.1

8.1 8.5 4.3

Building transfer nodes (%)

Table 1 Factors encouraging the withdrawal from individual transport for public transport by respondents (own study)

1.4

0.3 0.0 4.0

Other (%)

2.1

0.8 2.5 3.4

Nothing will convince me (%)

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Table 2 Use of bike by respondents depending on the weather conditions (own study) Data breakdown

Regardless of the weather conditions, also in the winter (%)

Regardless of the weather conditions, except for the winter (%)

Only in good weather (%)

Warsaw Tricity Silesian Metropolitan Union Overall

3.6 12.5 3.9

27.5 25.0 10.9

68.8 62.5 85.2

6.8

21.5

71.7

interest in individual electromobility is scarce, which probably results from the prices of electric or hybrid cars (70% of respondents do not intend to buy an electric or hybrid car)—see Table 3. The key barrier for the choosing an electric or hybrid car as the next individual vehicle is the barrier of income. Perhaps the expectations towards electromobility will be further cooled by the increase of energy prices due to the unsustainable energy mix in Poland, where as much as 75% of electric energy is produced from hard and brown coal [22]. Based on the literature review and conducted survey, it can be noticed that sustaining mobility must be based on the development of public transport systems. In metropolises, where particularly high traffic flows occur, public transport is based on high-efficiency rail transport (intercity rail, underground, trams). It should also be noted that rail transport in cities is electric, so it ensures sustainable mobility. Metropolitan public transport systems are supplemented by buses, which are increasingly often equipped with zero emission propulsion (electric- and hydrogen-powered vehicles) or low-carbon propulsion (hybrid or gas-powered vehicles). Therefore, only public transport can ensure sustainable mobility in metropolises. On the other hand, in specific conditions of small towns, which are more similar to historic cities, we can talk about the real alternative in the form of pedestrian movement or means of personal transport.

Table 3 Plans of respondents concerning the replacement of combustion car with an electric or hybrid car (own study) Data breakdown

I’m not going to replace the car (%)

Electric car (%)

Hybrid plug-in (%)

Hybrid (%)

Warsaw Tricity Silesian Metropolitan Union Overall

69.7 67.6 75.0

7.6 5.8 1.4

5.5 13.7 2.7

17.2 12.9 20.9

70.8

4.9

7.2

17.1

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2 Electromobility of Public Transport in the European Policy Many European cities are facing problems related to transport, such as congestion, air pollution, noise and road safety. Taking into account the growing population in urban areas and current problems resulting from the ineffectiveness of urban transport system, it is necessary to pay more attention to the solutions which promote sustainable urban mobility. The concept of sustainable mobility in cities is related to the goals concerning the improvement of both energy consumption and environmental indicators in cities. The development of current EU policy regarding urban transport has a long history. In 1992, European Commission presented the Green Paper on the impact of transport on environment: a community strategy for „sustainable mobility” [23]. The Green Paper contained the assessment of the general impact of transport on the environment and outlined the common strategy for sustainable mobility. Special attention was paid to air pollution, noise and congestion problem, which was defined as a recurrent temporary phenomenon with variable duration, resulting from the lack of balance between the demand and supply of transport infrastructure capacity. The following instruments that can be used for reducing congestion were indicated: proper public transport systems with a high utilization rate, traffic management systems, road tolls and restricted availability of crowded areas for passenger cars. The Green Paper was also connected with the European Commission’s statement of 1998 entitled Common Transport Policy—Sustainable Mobility: Perspectives for the Future [24]. In this document, high importance was attached to technical progress and telematics in the effective and sustainable development of integrated transport systems as one of the key priorities for the Commission. The improvement of the quality of local public transport, which is the only form of transport available to all citizens (especially in big cities), was indicated as a great challenge. The document also highlighted the negative transport impact on the natural environment, because the development of transport systems cannot take place at the expense of the quality of life of citizens, or cause environmental degradation. Therefore, environmental protection was considered to be an integral part of the transport policy. This goal was the basis for further EU strategic documents. The main instrument for its implementation was to be the so-called electromobility ecosystems formed in cities. In 2001, the Commission of the European Communities presented the White Paper „European Transport Policy 2010: Time to Decide” [25]. It determined directions for the transport policy of the European Union until 2010, emphasizing the significance and validity of the previous goal of the EU transport policy, i.e. sustainable development. The White Paper included sixteen specific proposals to be undertaken at the community level as part of the transport policy. The development of high quality urban transport was indicated among the detailed proposals. The Community suggested giving priority to better use of public transport and existing

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infrastructure in the light of the general degradation of the quality of life of European citizens. The following stage of EU transport policy consisted in adopting the “Green Paper—Towards a New Culture for Urban Mobility” on 25 August 2007 [26]. The document presented a new approach to urban mobility, consisting in the optimized use of different modes of public and individual transport, as well as creating good conditions for the execution of intermodal journeys. Particular attention was paid to the development of new urban mobility culture by education, trainings and raising awareness of the importance of sustainable mobility. It was highlighted that there was no single solution to reduce congestion in cities. The problems of mobility in the city are strictly related to the development of modern economy and society. Therefore, ecological solutions should be developed and promoted in order to reduce the negative impact of transport on the urban area environment, i.e. harmful emission, noise, etc. [27]. Another document concerning urban mobility is the Communication from the European Commission—“Action Plan on Urban Mobility” of 2009 [28]. It indicates more ecological urban transport as one of the six proposed activity areas (research and demonstration projects for lower and zero emission vehicles, Internet guide on clean and energy-efficient vehicles). The Community development strategy, which also addressed the problems of sustainable mobility, was developed in 2010. Europe 2020—A strategy for smart, sustainable and inclusive growth [29] is a document outlining the long-term vision of the development of the European Union until 2020. In Europe 2020 strategy, the problems of urban transport and mobility were included as part of actions towards smart and sustainable development. “The Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: Roadmap for moving to a competitive low-carbon economy in 2050” was published in March 2011 [30]. The communication states that mobility consistent with the rules of sustainable development can be achieved thanks to fuel efficiency, transition to electric-powered vehicles and appropriate prices. Technological innovations should support this process with regard to vehicle efficiency thanks to new engines (hybrid and electric), materials, and structures; use of more ecological energy thanks to the application of new fuels and drives. The new document entitled “White Paper: Roadmap to a Single European Transport Area—Towards a competitive and resource efficient transport system” was also adopted in March 2011 [31]. It included ten goals set for a competitive and resource efficient transport system. The document outlined that further development of the transport sector should be based e.g. on: Improvement of the energy efficiency performance of vehicles across all modes, optimization of the performance of multimodal logistic chains, as well as more efficient use of transport and infrastructure. Thanks to the development and introduction of new fuels and drives consistent with the rule of sustainable development, it was assumed that the number of conventionally-fueled cars in urban transport would be reduced by half by 2050; CO2-free city logistics would be achieved by 2030; low-carbon sustainable fuels in aviation would reach

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40% by 2050 and in line with the rule of sustainable development; emissions from maritime bunker fuels would be reduced by 40% (if feasible 50%) by 2050. All these would contribute to achieving the total 60% GHG emission reduction target in the first half of this century [32]. In December 2013, the European Commission adopted the Urban Mobility Package. The central element of the Urban Mobility Package is the Communication entitled “Together towards competitive and resource efficient urban mobility” [33]. It is supplemented by an annex that presents the concept of Sustainable Urban Mobility Plan, as well as four working documents concerning urban logistics, provisions on access to cities, implementation of intelligent transport system solutions in cities, as well as areas and safety of urban road traffic. In October 2014, the European Council agreed on the 2030 climate and energy framework [34], which set the achievement of at least 40% reduction of own greenhouse gas emissions by 2030 compared to 1990 by all economy sectors in order to achieve the long-term goal of reducing emissions by at least 80% by 2050. The necessary actions to be taken in order to move to low-carbon economy were specified, requiring changes in economic and investment activities, as well as introduction of incentives in all policy areas. European Strategy for Low-Carbon Economy of 2016 [35] stated low-carbon mobility as a necessary element for increasing transition to closed-loop low-carbon economy. The following three key activity areas were highlighted in the strategy for low-carbon economy: more effective transport system, low-carbon alternative energy sources for transport, as well as low-carbon and zero emission vehicles. Transformation to low-carbon or zero emission mobility will be supported by favorable horizontal enablers, such as the Energy Union strategy (connecting transport and energy systems), research and innovation policy, industrial and investment policy, the Digital Single Market Strategies and the skills agenda. It was noted in the strategy that the development prospects for low-carbon alternative energy sources are different among transport modes and the widest range of options currently exists for cars and buses. In the case of public buses, quick implementation of zero emission technologies seems to be feasible, because a majority of them are purchased through public procurement. Since a significant part of public procurement is undertaken by municipal and local authorities, in the case of public transport vehicles (such as buses), there is a particularly high potential for transition to low-carbon alternative energy sources. It was also emphasized that low-carbon mobility and innovations must become an integral part of the industrial policy of all Member States. The strategy also emphasized the significance of digital solutions in transport operation in line with Directive 2010/40/EU of the European Parliament and of the Council of 7 July 2010 on the framework for the deployment of Intelligent Transport Systems in the field of road transport and for interfaces with other modes of transport [36]. Intelligent Transport Systems (ITS) are a set of tools based on information and communication technologies, as well as telematic solutions applied in order to increase the effectiveness and integration of the whole urban transport system in accordance with the rules of sustainable development [37].

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In 2017, the European Commission announced the White Paper on the future of Europe. Reflections and scenarios for the EU27 by 2025 [38]. Further documents were issued in January 2019: Document opening the Reflection Paper “Towards a Sustainable Europe by 2030” [39] including three annexes. In this way, the Commission began discussion on sustainable development in the future as part of a broader debate initiated in March 2017 by the White Paper for the future of Europe. Annex I: The Juncker’s Commission’s contribution to the Sustainable Development Goals [40], with regard to urban mobility specified that in consequence of the strategy towards low-carbon mobility, the Commission adopted three packages supporting “Europe on the Move” mobility, in 2017 and 2018 respectively. “Europe on the Move” is a broadly designed set of initiatives that will increase the traffic safety, encourage to implement intelligent road toll collection systems, as well as reduce carbon dioxide emission, air pollution and traffic congestion. On 28 November 2018, the Commission set out a long-term strategic vision for a prosperous, modern, competitive and climate-neutral economy by 2050 [41]. The strategy shows how Europe can lead the way to achieve climate neutrality by investing in technological solutions, empowering citizens and aligning political actions in key areas, such as industrial policy, finance and research. Following the request of the European Parliament and of the Council, the vision of climate-neutral future presented by the European Commission includes almost all EU policy areas and is consistent with the goal of Paris Agreement [42], which is to maintain the temperature increase significantly below 2 °C and try to reduce this increase to the level of 1.5 °C [43]. In the field of transport, attention was paid to low-carbon and zero emission vehicles with efficient alternative propulsion systems. With various modern technologies available, electricity cannot be the only solution for all types of transport. Hydrogen-based technologies can be a zero emission alternative. Ensuring clean mobility also requires effective organization of the system based on digitization, data sharing and interoperation standards. The key elements shaping mobility future in the city include: urban planning, safe bike and pedestrian paths, clean public transport and mobility as a service, e.g. car or bike rental [44]. In December 2019 European Commission presented European Green Deal—a plan to build sustainable EU economy by 2050 [45]. European Green Deal concerns all economy sectors, in particular transport, energy, agriculture, construction, as well as such industry branches as steel, cement, ICT, textile and chemical industries. The process of achieving climate neutrality was described in the draft new law. According to the climate law, this process will have to be more cost-efficient and consistent with the latest scientific knowledge. In March 2020 European Commission launched the European Climate Pact to provide organizations and citizens with the possibility to speak out and play a role in developing new activities, exchanging information, initiating bottom-up initiatives and promoting solutions to be used by others. The consultations will last until 27 May 2020 [46]. The first discussion about the proposal for a new EU law is to take place on 22 April, and the vote on the proposal in the European Parliament may take place in June or July 2020.

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The legal basis for the development of electromobility in EU Member States is the Directive 2014/94/EU of the European Parliament and of the Council of 22 October 2014 on the deployment of alternative fuels [47] and Directive 2009/33/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of clean and energy-efficient road transport vehicles [48]. In November 2017, European Commission proposed to review Directive 2009/33/EC on the promotion of clean and energy-efficient road transport vehicles (directive on clean vehicles), because according to the assessment, the effects of the Directive were limited. Directive 2019/1161 of the European Parliament and of the Council of 20 June 2019 amending Directive 2009/33/EC on the promotion of clean and energy-efficient road transport vehicles [49], the so-called CVD (Clean Vehicles Directive), was published in the Official Journal of the European Union on 12 July 2019. The amendment of the provisions of the Directive is a consequence of the ex post evaluation, whose results indicate that the previous Directive 2009/33/EC did not stimulate the development of clean vehicles market in the EU. The purpose of the Directive is to promote low-carbon and zero emission vehicles, and thus to combat urban air pollution. According to the proposed changes in the document, state and self-government authorities are obliged to ensure that a part of purchased vehicles and contracted services (especially in public transport) are low-carbon or zero emission. The Directive mainly concerns the purchase of buses, lorries, vans, but also passenger cars. The amendment of CVD became effective on 1 August 2019, and the Member States are obliged to bring into force the legal regulations that are necessary to transpose the amendments of this Directive. Low-carbon or zero emission mobility in urban areas is possible to achieve e.g. thanks to fast development of the so-called low-carbon technologies using power sources other than petroleum: gas (CNG in compressed form, LNG in liquid form), hydrogen, hybrid and electric (trolleybus, electric bus). European Commission actively supports and initiates projects of cooperation in the field of sustainable urban mobility, starting from research, development of tools, presentations, trainings, promotion and other measures for the exchange of knowledge. Electromobility is one of the main factors shaping the modern transport system. The beneficiaries of electromobility development also include energy companies and business entities specialized in innovative technologies, e.g. ICT, as well as energy storage and battery production aspects. Electrically-powered vehicles will be a majority of fully autonomous vehicles, that constitute a disruptive change in the operation of passenger transport and mobility sector. Electrification of the transport sector is a very important element of creating the zero emission transport system. It may be assumed that the future of electromobility in Europe will still largely depend on the decisions taken at the EU level [50].

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3 Electromobility of Public Transport in Poland The natural driving forces for the development and implementation of innovations in public transport are metropolises—it is in these areas that capital, know-how, technologies, educated staff and strong stream of EU funds are concentrated. The large scale of activity of agglomeration bus operators in respect of fulfilling transport needs of inhabitants results in a stable market position of these entities and provides the possibility of increased opening to innovations. In the domestic market, public (bus) transport operators are more willing to implement innovations thanpresents the results of analyzing the provisions private entities [51]. The National Spatial Development Concept 2030 [52], which is the most important planning document regarding the creation of domestic spatial order, identified 10 metropolitan areas in Poland. The following cities were included in this group: Warsaw, Silesian Metropolis, Kraków, Łódź, Tricity, Poznań, Wrocław, bipol Bydgoszcz-Toruń, Szczecin, Lublin. The delimitation of urban functional areas was based on the objective criteria developed between government and self-government authorities, with participation of social and economic partners. The above-mentioned metropolitan areas met the following conditions (according to the data for 2009): • • • •

Population above 300,000, Employment in the market services sector above 40,000, Number of students learning in the agglomeration above 60,000, Cooperation of science and research institutions in 5th and 6th EU framework programme, • Location of airport with passenger transport services, • Number of four-star and five-star hotels, • Organization of international exhibitions in special exhibition facilities in 2006– 2008. Among the specified metropolitan areas, Lublin did not meet the condition of airport with passenger traffic service. However, due to its developmental importance, e.g. regarding the academic potential (large research center) and concentration of business activity, as well as being a contact place for countries situated to the east of Poland, it was included among the metropolitan areas. In order to determine the significance of electromobility development in the specified metropolises, an analysis of the provisions of municipal and metropolitan strategic documents on public transport planning was performed. Two types of documents were analyzed: • Sustainable Urban Transport Plans (SUTP)—strategic documents, mandatory, introduced by the Act of 2010 [53]. • Sustainable Urban Mobility Plans (SUMP)—presenting a relatively new approach to the problem of mobility and public transport.

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The first type of document represents the traditional approach to transport planning, based on complex strategic diagnoses and focused on the supply side of transport system. Sustainable Urban Mobility Plans (SUMPs) are a relatively new tool, covering a wide range of problems concerning the movement of people and cargo in the cities. In 2013, these plans were considered to be the most complex instruments in the Urban Mobility Package adopted by the European Commission [54]. The fundamental differences between the specified types of documents have been presented in Table 4. The analysis of provisions in the specified documents allows to recognize a broad strategic perspective of public transport development in the metropolitan areas. The provisions of documents were assessed in terms of three individually adopted criteria, which in turn were evaluated using a three-grade scale, in line with the entries in Table 5. Table 6 presents the results of analyzing the provisions of strategic documents in the context of the role of electromobility in public transport. Then, based on three previously presented criteria, the general assessment of the approach of self-government authorities to implementation of electric buses was made. If at least two criteria achieved the maximum grade, the provisions were evaluated as complex. Otherwise, the provisions were assessed as general, or the lack of any references was indicated. The table presents analysis results for 10 metropolitan areas, whereby the documents for Bydgoszcz and Torun were included separately from Gdynia and Gdańsk (without Sopot), because no common metropolitan documents were prepared for these areas. The strategic documents covered by the analysis mostly have a similar layout, where development goals and priorities are created on the basis of strategic diagnosis and analysis, as well as instruments for their achievement are defined. The documents were different from one another with the level of detail and manner of articulating strategic goals—functional system was adopted for certain documents, and branch system for others. On the basis of the analysis, it was concluded that three metropolises, i.e.: Upper Silesia Agglomeration, Poznań and Lublin, are characterized by the complex approach at the strategic management level to electromobility development in public transport. In the case of five metropolises, the provisions were assessed as general, whereas in further four cases, the conclusion was that such provisions were missing. Obviously, the lack of provisions does not block investments carried out mainly by public transport operators. The analysis of the provisions of strategic documents allows us to conclude that the implementation of e-buses is not the main goal or instrument for sustaining mobility. The documents are considerably more focused on transport availability, broadly understood integration and change of transport behaviours of the inhabitants [56]. Only in the case of some documents precise information concerning the development plans for electric bus fleet were included and the potential financing model of these investment was presented. The provisions of the above-referred strategic documents are not the only guidelines regarding the formation of rolling stock strategies in public transport. Broadly understood electromobility, including also in respect of public transport, is

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Table 4 Differences between traditional transport planning and new approach to mobility planning [55] Traditional transport planning (SUTP)

Sustainable urban mobility planning (SUMP)

Focus on traffic flows Main goals: ensuring high traffic fluidity and system capacity

Focus on people Main goals: ensuring availability and high quality of life, balancing economic activity, social equality, health and clean environment Focus on development of all modes of transport and moving towards more sustainable and ecological means of transport Integrated activities intended to achieve effective results Planning documents coherent with the complementary urban policy areas (e.g. spatial planning) Short and medium term plans embedded in long-term vision and strategy Inclusion of context of the entire functional area related to the source and destination traffic

Focus on division of transport tasks

Focus on infrastructure Sectoral planning documents

Short and medium term planning perspective Strong settlement within the administrative borders (e.g. restriction only to the area of municipality/city) Planning dominated by traffic engineers Planning by experts Limited impact assessment, usually one-off Focus on traffic flows Main goals: ensuring high traffic fluidity and system capacity Focus on division of transport tasks

Focus on infrastructure Sectoral planning documents

Short and medium term planning perspective Strong settlement within the administrative borders (e.g. restriction only to the area of municipality/city) Planning dominated by traffic engineers Planning by experts Limited impact assessment, usually one-off

Interdisciplinary project teams Involvement of transport system stakeholders in the planning process Regular monitoring and evaluation, constantly improved planning process Focus on people Main goals: ensuring availability and high quality of life, balancing economic activity, social equality, health and clean environment Focus on development of all modes of transport and moving towards more sustainable and ecological means of transport Integrated activities intended to achieve effective results Planning documents coherent with the complementary urban policy areas (e.g. spatial planning) Short and medium term plans embedded in long-term vision and strategy Inclusion of context of the entire functional area related to the source and destination traffic Interdisciplinary project teams Involvement of transport system stakeholders in the planning process Regular monitoring and evaluation, constantly improved planning process

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Table 5 Criteria for assessment of entries in strategic documents in the field of electromobility (own study) No.

Criterion

Assessment/interpretation

1

Position of public transport electromobility in the hierarchy of objectives Determination of the target dimension of electromobility

++ level of strategic objectives/priorities + level of activities/recommendations − lack of entries ++ precise entries with an estimation of the quantitative aspect of the rolling stock + general entries concerning the direction for changes − lack of entries ++ precise entries with highlighted dates and sources of financing + general entries − lack of entries

2

3

Roadmap for electromobility implementation

currently one of the important directions of the Polish economic policy. These activities fall within the Electromobility Program adopted by the Polish government, containing the package of regulations and strategic documents, including: • Electromobility Development Plan for Poland „Energy for the Future” (2017), • Domestic framework of the development policy for the infrastructure of alternative fuels (2017), • Act on electromobility and alternative fuels (2018), • Act establishing Low-Carbon Transport Fund (2018). The specified package is the result of adopting the Directive of the European Parliament and of the Council 2014/94/EU on the deployment of alternative fuels infrastructure in October 2014. According to the Directive referred to above, EU Member States are obliged to deploy alternative fuels infrastructure within the set time limits. In the context of development of sustainable urban mobility, “alternative fuels infrastructure” is understood as natural gas refueling points and electric vehicle charging points. The following items will be included among the enforcement activities announced in the document: promotion of electromobility, obligation to replace a part of public administration fleet with electric vehicles, obligation to build the proper charging infrastructure in/at public institution buildings, introduction of tax reliefs for electric vehicle users. Among the above-mentioned documents, in the context of forming public transport fleet, the provisions of the Act on electromobility and alternative fuels (2018) are the most binding. Art. 68 obliges public transport organizers, excluding the municipalities and districts with the number of population lower than 50,000, to achieve the share of zero emission buses in used vehicle fleet according to the following schedule: 5% from 1 January 2021, 10% from 1 January 2023, 20% from 1 January 2025.

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Table 6 Assessment of provisions in strategic documents concerning the implementation of electric rolling stock (own study) No.

City/area

Type of document (year of adoption)

Assessment of criterion 1

Assessment of criterion 2

Assessment of criterion 3

Assessment of entries concerning electric buses

1

Warsaw

SUTP (2014) SUMP (2016)

+

+

+

General

2

Upper Silesia Agglomeration*

SUTP (2013) SUMP (2016)

+

++

++

Complex

3

Kraków

SUTP (2013) SUMP (2016)

++

−

−

General

4

Łódź

SUTP (2018)

−

−

−

Not applicable

5

Gdańsk

SUTP (2014) SUMP (2018)

+

+

+

General

6

Gdynia

SUTP (2014) SUMP (2016)

+

+

−

General

7

Poznań

SUTP (2019) SUMP (2016)

+

++

++

Complex

8

Wrocław

SUTP (2016) SUMP (2018)

++

+

−

General

9

Bydgoszcz

SUTP (2013)

–

−

−

Not applicable

10

Toruń

SUTP (2013)

−

−

−

Not applicable

11

Szczecin

SUTP (2014) SUMP (2016)

+

+

−

Not applicable

12

Lublin

SUTP (2013) SUMP (2018)

++

++

++

Complex

*In the case of Upper Silesia Agglomeration, due to the conurbation system, the provisions of SUMP (for the Central Subregion of Silesian Province) and SUTP for KZK GOP (Municipal Transport Union of the Upper Silesian Industrial District) were used

The above legal conditions enforce municipalities to implement electric buses. According to the results of Barometr Nowej Mobilności (New Mobility Barometer) 2019/20 study conducted by the Polish Alternative Fuels Association (PSPA) on a group of 62 Polish self-governments (above 50,000 inhabitants), concerning public transport [57]: • 46% of municipalities are not planning to develop the electric bus fleet • 42% of municipalities are planning to develop the fleet within 3 years • 12% of municipalities are planning to develop the fleet within 1 year. In the same group of surveyed municipalities, 26% answered that they used electric buses in public transport, whereas the answers of the remaining 74% were

Determinants for the Effective Electromobility Development …

45

negative. There are currently 232 electric buses registered in Poland (data for February 2020) [58]. The main impulse for implementation of electromobility in public transport in Poland is legal regulations at the statutory level and involvement of EU funds in funding rolling stock investment. The following main barriers for implementation of e-buses should be mentioned: • High level of investment expenditure (in comparison with the combustion vehicles) • Uncertainty regarding long-term formation of electricity prices affecting the level of operating costs • Necessity to take into account the distribution of pantograph chargers and time required to charge batteries in selecting the line or planning the timetable. The level of investment expenditure seems to be the key factor, and at the same time, “deterrent” for the Polish public transport operators. In the case of a two-axis bus with the length of approximately 12 m, the cost of purchasing an electrically-powered vehicle is on the average 2.3 times higher—the price of diesel engine bus amounts to approximately EUR 232,000 (with the exchange rate of EUR 1 = PLN 4.30), whereas the price of electric bus with similar equipment standard amounts to EUR 533,600. The indicated values were estimated on the basis of the available data concerning purchases within EU funded programmes, as well as experiences of the author team, and are comparable with the values adopted by N. Hooftman’s team [59]. In the Polish conditions, an increase of electric rolling stock prices has been noticed in the recent years, which is the effect of the growing demand boosted by EU funds and low supply resulting from a small number of models that have obtained the domestic approval so far. One of the main concerns for the users of electric rolling stock is the battery life. Therefore, there is a clear tendency in Poland to purchase vehicles with a long warranty period for batteries, which proportionally increases the purchase price. According to various estimates, the battery life is 5–10 years, which depends on many factors, including the frequency and method of charging, as well as vehicle use conditions (land type, surrounding temperature). In Polish conditions, the average cost of a new battery set is approximately EUR 140,000 [60]. Within investment expenditure, the costs of charging infrastructure should be distinguished. The material scope of this investment depends on the character of the line intended for e-bus operation. The most popular model is to combine plug-in chargers and pantograph chargers. The first ones are most frequently used for charging vehicles at night, during their downtime in the depot. On the other hand, pantograph chargers allow for very quick battery recharging and are located across the network, most frequently at the final stops. The depot (plug-in) charger costs approximately EUR 18,000, whereas the pantograph charger costs approximately EUR 200,000 (including installation). The depot charger for night-time charging will be usually assigned to one vehicle, whereas the pantograph charger, installed on the public transport network, will serve a higher number of vehicles, depending

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on the line arrangement and timetable. In the comparative version, the costs of infrastructure for combustion rolling stock are hard to determine unambiguously. A majority of large municipal public transport operators have their own refuelling infrastructure, whereas smaller entities use the commercial offer of petrol stations. An important factor for implementing electromobility is the potentially lower cost of traction in comparison with the combustion engines, For example, a 12 m long bus with combustion engine Euro VI burns on the average 35 l/100 km, which with the fuel price of EUR 0.93 per litre results in the average 1 km driving cost at the level of EUR 0.33. On the other hand, an electric vehicle uses on the average approximately 1.1 kWk per 1 km of operation [61] (with the average energy price of EUR 0.09 per 1 kWh). In consequence, the average cost of 1 km of driving will amount to EUR 0.01. The details have been presented in Table 7. Operators also bear other direct costs, including e.g. the costs of tires, drivers’ remuneration, repairs, inspections, cleaning, insurance and indirect costs of activity. The above cost items can be classified as maintenance costs. Their total value, converted to 1 vehicle-kilometer, amounts to approximately EUR 1. It is assumed that the value is identical both for combustion and electric vehicles. Based on the above-specified assumptions, TCO (Total cost of ownership) comparative calculation was performed for two types of buses: combustion (Diesel Euro VI) and e-bus. The calculations presented in Table 8 include the total costs of vehicle use in 10 years, which corresponds to the period of standard bus depreciation. The average annual bus mileage at the level of 70,000 km was adopted for the calculation. The calculation is based on the version with one depot charger for one vehicle and one pantograph charger, whose cost is distributed proportionally between 4 buses that may use it. A comparative TCO analysis for the period of 10 years indicates that the cost of 1 vehicle-kilometer of electric bus is on the average approximately 18% higher than 1 vehicle-kilometer of combustion bus. The presented calculations are generalized and the adopted cost values are estimated for the Polish market, based on the data collected by the author team. The presented results will be different, depending on the parameters of a specific line and adopted strategy of e-bus implementation. The following can be distinguished among the factors affecting the cost reduction: • Performance of contracts for the one-time purchase of a higher number of vehicles, which should affect the reduction of a single vehicle cost

Table 7 Calculation of unit fuel cost (own study)

Cost category

Diesel Euro VI

EV (Electric Vehicle)

Fuel consumption Fuel unit price Cost per kilometer

35 l/100 km EUR 0.93/l EUR 0.33

1.1 kWh/km EUR 0.09/l EUR 0.01

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Table 8 TCO calculation for the period of 10 years (own study) Cost category

Diesel Euro VI

EV (Electric Vehicle)

Fuel price (EUR/km) 0.33 0.01 Maintenance costs (EUR/km) 1.00 1.00 Total operating costs (EUR/km) 1.33 1.01 Investment expenditure—bus (EUR) 232.00 533.600 Investment expenditure—charging infrastructure (EUR) (version – 18,000 with 1 depot charger) Investment expenditure—charging infrastructure (EUR) (version – 50,000 with 1 pantograph charger used by 4 vehicles) Investment expenditure—battery to be replaced in the 7th year of – 60,000 use* Total investment expenditure (EUR) 232,000 661,600 Average annual bus mileage (in vehicle-kilometers) 70,000 70,000 Mileage in 10 years (in vehicle-kilometers) 700,000 700,000 Investment expenditure converted to one kilometre of mileage in 0.33 0.95 10 years (EUR/km) TCO 10 year (EUR/km) 1.66 1.96 *Battery life (adopted for the period of 7 years) does not correspond to 10-year period of TCO analysis equal to vehicle depreciation period, so the cost of new battery proportional for the period of 3 years was adopted for the calculation (3/7*EUR 140.00)

• Reasonable distribution of expensive pantograph chargers, which will allow to operate a higher number of vehicles and reduce the share in unit cost (returns to scale) • Reduced costs of vehicle maintenance, which has been indicated in certain analyses, but it requires domestic operators to have proper knowledge and experience. The expenditure on rolling stock and batteries is a significant cost affecting the economic effectiveness of e-buses. These costs may diminish in the future due to the increase of market competition and technological progress influencing the decline of manufacturing costs and extension of battery life. Another important cost component is the price of fuel/electricity. The prices of petroleum are characterized by relative stability over the years. The companies have got used to some price fluctuations and are capable of forecasting certain trends in purchase prices. Additionally, in the case of having own refueling and fuel storage infrastructure, it is possible to wait through momentary price rises of diesel fuel. Less certain is the price of electricity, which is currently shaped in a very turbulent environment. The current energy mix in Poland is mostly based on hard coal, which will result in a rise in prices due to the increasingly restrictive CO2 emission standards.

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4 Summary Electromobility is regarded as an important instrument for sustaining mobility in the EU transport policy. However, it is necessary to solve many problems, in particular related to availability of supply and competitive price of electricity. A new challenge is to adjust mobility models to higher safety requirements, which will become effective in connection with the epidemiological threat. Mobility is one of the key areas that are subject to change in consequence of the experiences caused by COVID-19 epidemic. In the domestic conditions, electromobility is not regarded as a real alternative to combustion vehicles. Research conducted among representatives of the creative class in Polish metropolises has indicated a very small interest in electromobility, both in the context of electric car purchase and its use in the sharing system. Moreover, Polish metropolises do not regard electromobility (including e-bus implementation) as the key instrument in their strategic documents related to sustainable mobility and transport. The economic viability of e-bus implementation also raises doubts. Currently, there are two main factors promoting the development of electromobility in public transport that can be distinguished. The first of them is legal conditions at the statutory level that oblige public transport organizers to achieve a specific number of e-buses in their fleet. The second factor is the wide stream of EU funds intended for this type of products.

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Determinants for the Effective Development and Operation of the Charging Infrastructure Grzegorz Dydkowski and Anna Urbanek

Abstract The ensuring of appropriate charging infrastructure for battery electric vehicles (BEVs) is one of the electromobility development factors. The adopted legal regulations related to electromobility focus on the determination of the expected number of charging stations, on the offered power, and on ensuring information and safety during their use. Local governments and operators of power distribution systems are indicated as entities supposed to develop in the next years plans and to ensure an appropriate charging network. Apart from the accessibility, it is also important to adopt a model and to use tools, which will ensure the effective functioning of the charging infrastructure. This should result in the establishment of publicly accessible and efficient system, so as not to increase the costs of electromobility system implementation (which are already high enough). The latter results from the vehicle prices and the need for development battery charging infrastructure. The chapter aims at presenting rules that should be applied and solutions aimed at ensuring the effective development of BEVs charging infrastructure.










Keywords Electromobility Battery electric vehicles BEVs Charging infrastructure Charging networks Regulations Interoperability, business models










1 Introduction The issue of appropriate infrastructure for electric vehicle battery charging is one of the conditions for electromobility development, in particular for electric vehicles in road transport. Issues of battery charging infrastructure development are related both to the use of electric vehicles in cities and in longer-distance intercity traffic. G. Dydkowski
A. Urbanek (&) Department of Transport, University of Economics in Katowice, Katowice, Poland e-mail: [email protected] G. Dydkowski e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_4

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An assumption is made that the use of electric vehicles in cities is facilitated; in a part of cases, they can charge batteries during the overnight stop, or during the day, when the user is at work, and their daily mileage, e.g. related to commuting to work, is not high and the already achieved electric vehicles ranges allow to cover them. However, an electric vehicle user not always has and will have a possibility to charge the battery at the place of residence, and this forces the origination of a network of publicly accessible chargers. Such a network is also necessary in the case of using cars at longer distances so that there would be no concerns of covering the distance and of possible unplanned stops related to the battery discharging, and also to prevent significant extension of waiting for the charging itself when all positions are occupied by other vehicles. Hence, an issue of appropriate number and location of charging stations appears, to prevent the occurrence of a problem of their spatial distribution not in accordance with the existing demand. Therefore it is necessary to prepare network development plans and irrespective of that such a model of network service should be ensured, that it could comprise various entities, not only from the public sector and by ownership related to the power sector. As it results from the research, just multidimensional and based on the cooperation of various entities, including the public and private sector, strategies and projects for development and promotion of battery charging infrastructure were most successful [1]. The electromobility-related legislation adopted in Poland focuses on the determination of the expected number of electric vehicle battery charging stations, on the offered power, and on ensuring information and safety during their use. In the field of vehicle battery charging infrastructure establishment, the regulations predict in the first place an initiative of municipal local governments in cities for such point development, through the obligation to develop, agree, and approve network plans. Later on, after 31 December 2020, the operators of power distribution systems will be obliged to create a relevant number of charging points, in accordance with the adopted plans. Battery charging stations accessible to the public are being created and operate already now. They are established based on commercial rules by entities, whose business is this activity, entities managing shopping malls, office blocks, hotels, or big multifamily buildings, but also by electric vehicle manufacturers and sellers, fuel and power corporations or by the public administration. Premises and motives to create such points are different, from the expectations of profits on energy sales, making other services more attractive and increasing the volume of sales, up to the promotion of an entity and of zero-emission transport. Prices of the consumed energy differ—from free charging, up to relatively high prices. On the one hand, one can enjoy chargers, from which the electricity may be collected free of charge, on the other hand, longer-term this can limit the development of their network because it does not create motivation to enter this market by entities operating based on commercial rules as they encounter competition of entities offering free charging—including those costs into costs of another activity.

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Another issue that requires resolution is the unification of standards and the creation of access to chargers of various operators, also from the technical point of view, as well as ensuring in formal terms the possibility to use the services of various operators, with the user utilizing the authorization instruments of the parent operator. Unfortunately, so far, both in the world as well as in various countries or even cities, no uniform solutions exist [1]. No isolated points or solutions should be created. Mobility cannot be narrowed only to city boundaries. Visitors must have access to the charging infrastructure and be capable of using it as smoothly and easily as residents. The development of an interoperable infrastructure should be supported, i.e. such, which uses standardized solutions and enables cooperation with charging network operators and charging services providers operating within a country and on an international level [2]. Apart from the accessibility, it is also important to adopt solutions and to use tools, which will ensure the effective functioning of battery charging infrastructure. This should result in establishing accessible, but also efficient systems of battery charging, so as not to increase the already high costs of electromobility system implementation, resulting from vehicle prices and the necessity to create an appropriate infrastructure. The chapter presents the rules that should be applied and solutions aimed at ensuring the effective development of vehicle battery charging infrastructure, as well as the place of the public and private sector in these activities. Because it is necessary to emphasize that despite the performance of numerous research studies in the field of mobility, the preparation of reports and recommendations by various institutions, there are no broader results of research and publications in the field of the organizational ownership model for operation in the future of vehicle battery charging infrastructure. Solutions, which will ensure non-discriminatory involvement of various entities, including also the private sector, in the area of electric vehicles charging infrastructure accessible to the public. Reports and results of research related to European countries were analyzed, but also those of the United States, China, and Japan, that is states of relatively most developed electromobility [3–5]. The usage and increase in electric vehicles share in serving the cities is related to the question, to what extent such vehicles should be used by their owners, and to what extent they should dominate as vehicles shared by many users. The fact that users may be then relieved from time losses related to battery charging at points accessible to the public, as well as higher mileage may reduce the unit costs of those vehicles use, supports the sharing. The carried-out analyses show that because of higher electric vehicles prices and possible lower unit energy costs as against fuel costs, such vehicles are more effective in situations, when higher mileage is covered, hence this is an economic premise to use these vehicles in shared vehicles systems.

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2 The Role of the Public Sector in Charging Infrastructure Development The documents related to the policy on transport development, as well as the strategy adopted in recent years, show the necessity to ensure mobility to residents and accessibility of transport services, but also the necessity for environmental protection, including—in particular—city environments (e.g. by the reduction of CO2 emissions by vehicles). Attention is drawn to the limited fossil fuels resources and to the strong dependence of the transport sector on liquid fuels, part of which is extracted in world regions with pretty unstable political situations. The acquisition of electricity from renewable sources having low CO2 emission and supplying electric vehicles with energy allows them to accomplish these objectives [6–9]. However, attention should be drawn to the fact that on a European Union or a global scale, electricity acquisition sources are different—in countries having the power industry based on hard coal and lignite will not give such an effect, like in countries based on renewable sources and low CO2 emission. The public sector gets involved in ensuring conditions for electromobility development, and in particular—in the publicly available battery charging infrastructure. Various administration levels are involved in that—depending on the country, also the used intervention instruments are different [1, 10]. Significant benefits are sought in the public sector involvement, which should result in an acceleration of vehicle battery charging infrastructure development [10], albeit it is assessed that it is difficult to refer to an insufficient number of charging stations accessible to the public [11]. The European Commission recommends 10 stations per electric car—in many countries, this level is now obtained. It should be added that the assessment of station accessibility depends on their numbers, but to a large extent, it has a psychological dimension, the driver’s confidence that there will be no problem with battery charging when one needs it. Moreover, the truth of current readings of the range still achievable by the given vehicle is important. Regulations included in the Directive 2014/94/EU of the European Parliament and of the Council of 22 October 2014 on the deployment of alternative fuels infrastructure are to ensure the creation of conditions for alternative fuels use in the European Union countries [12]. In Poland, the Directive’s recommendations were implemented into the legislation, i.e. by provisions of the Act of 11 January 2018 on Electromobility and Alternative Fuels [13]. The Act determines the principles for the development and operation of the infrastructure intended to use key alternative fuels in transport, including infrastructure used in public transport, duties of public entities related to the electromobility market development, information obligations in the field of alternative fuels, conditions for zero-emission transport zones functioning, and the development and content rules of a national policy framework for alternative fuels infrastructure development. One of the most important decisions of the aforementioned act consists in the determination of charging station numbers in major cities and of the entities responsible for development planning, and later on—for construction of publicly accessible electric

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vehicle charging stations. An assumption was made that in the initial period electric vehicles charging points are and will be constructed based on market rules, e.g. by private entities. (Yet the interested parties can apply for financial support from the Low-Emission Transport Fund.) Hence the tasks of the municipalities consist initially in monitoring the infrastructure development. At the same time, they can carry out a more active policy in this field. Obviously, it is not necessary to adopt a new solution, in which a municipality creates publicly available vehicle charging stations, and using the public procurement formula (e.g. a public-private partnership formula may be used). The assumption is that funds are invested by a private sector entity and later on, for a specified period, it obtains revenue for the given project [14]. It is expected that the public-private partnership will not only allow to involve private funds in projects implementation, but also will ensure their effective completion and their management during current operations. Following regulations adopted in Poland, the head of the municipality or a mayor of a city shall by 15 January 2020 prepare a report on charging points within the municipality area, related to publicly available charging points, including information about the number and locations of existing publicly available charging stations, stations planned to build by 31 December 2020, and the number of charging points missing to achieve the minimum number indicated in the act, if any [13]. In the case, if only the above report shows that the minimum number of charging points were not achieved, the head of the municipality or mayor of municipality or city, which population is at least 100,000, and in which at least 60,000 motor vehicles are registered, and there are at least 400 cars per 1000 inhabitants, shall draw a plan of construction of such charging stations [13]. The plan shall be consulted with residents and agreed with power distribution systems’ operators, in the area of whose operation the arrangement of charging stations accessible to the public is planned. Then, after the arrangements, the head of the municipality or mayor of municipality or city passes the draft plan to the municipality council and after its adoption—inter alia to the distribution systems’ operators. Based on that, the operator develops a program of connecting the publicly available charging stations to the power distribution system. The power distribution system operator who is competent for the location of the charging station accessible to the public shown in the plan builds such a station. The construction costs of such charging stations incurred by power distribution system operators are classified as justified costs pursuant to Art. 3 para 21 of the Act of 10 April 1997—Power Law [15]. The last stage consists in the selection of the entity, operator-entity of a charging station. In this case, a procedure is adopted, in which to fulfil the role of a charging station accessible to the public operator and a charging services provider, a power entity is appointed, carrying out business in the field of electricity trading and selling electricity to the largest number of end customers connected to the power distribution network within the municipality area, in which a specific charging station is situated. However, the power distribution system operator, who constructed a publicly available charging station shall carry out a procedure to select an operator of charging station. In particular, the provision of Art. 12 of the Act on the Concession on Construction Works or Services [16] stipulates that the

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customer prepares and carries out the procedure to conclude a concession agreement in a way ensuring observation of fair competition rules and equal treatment of contractors as well as according to proportionality and transparency rules. At the same time, the procedure to conclude a concession agreement cannot be prepared and carried out in a way aiming at avoiding the provisions of the act application [16]. That means, that as a target an operator of publicly available charging stations shall be selected in an open competitive way, which may be positively assessed. However, this is a longer perspective, shorter-term this will be the expenditure of power sector entities—power distribution system operators, whose ownership structures are pretty complicated—frequently the State Treasury is the shareholder, but also individual and institutional investors, various funds, and private sector entities. Hoping to the network development based on market rules in the initial period, since the end of 2020, it is necessary to be aware of the fact that there is a short period of time to increase the number of charging stations, both for private entities, which are not obliged by the procedures stipulated e.g. in the Public Procurement Law or the Act on Public-Private Partnership, as well as for public entities. For the latter ones—in the case of active municipality policies—it would be necessary to carry out procedures in accordance with the Act of 24 January 2004—Public Procurement Law [17] or the Act of 19 December 2008 on Public-Private Partnership [18], or the Act the Concession on Construction Works or Services, as a result of which an entity would be selected, which would execute this procurement. In general, this is a construction of a charging station—albeit in this case the municipality may make the land property available, and the scope of construction works not necessarily must be significant, but the changing procedures to obtain an appropriate power of the service line and performance of all related requirements may be and frequently are a problem. Now the situation may be additionally complicated by the fact that power companies are also opening charging stations, which weakens their neutrality in this process. Irrespective of charging station locations in cities, there are also plans to place them along the TEN-T core road network. Hence, the General Directorate of National Roads and Motorways prepares location plans of publicly available charging stations along main roads under its management, which are published after the process of consultation and arrangements. Procedures are carried out at present, as a result of which entities are selected, which will be leasing sites for electric cars charging stations at selected motorways and express roads [19]. An assumption was made in the carried-out procedures that charging stations should not be at distances longer than 50 km, although this depends also on the Service Areas (MOPs) location, and obviously on the interest in a specific site of entities intending to establish and operate charging stations accessible to the public. Irrespective of that, an assumption was also made that the same operator should not offer its services in two adjacent MOPs. Procedures carried out in 2019 and at the beginning of 2020 showed interest of both entities from the power sector and entities concentrating on establishing and managing charging stations and provision of services in the field of electromobility infrastructure [19]. Until now it is difficult to determine to what

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extent such projects are profitable, and whether the charging infrastructure development is not ahead of electric vehicle use, but in the case of inter-city roads network the construction of vehicle battery charging infrastructure is carried out under open competitive and market procedures, which can be positively assessed. The infrastructure of publicly available charging stations in Poland is in its initial development phase, a part of publicly available charging stations at this stage of operation does not allow to obtain current money surpluses, or prognosticate a quick return of invested funds. With the expansion of the market and the increase in the electric vehicles number, the operators will have to balance the revenue and costs. The simplest solution would obviously consist in selling electricity at such prices that the sales revenue would cover the costs and allow to obtain profits. However, energy purchase prices from distribution networks may be an issue here, as well as liquid fuels prices. Because an electric car is not necessarily is and will be only in a given family (which would allow to make a choice from the incurred operating expenditures point of view), high electricity prices may weaken the motivation to buy such vehicles. In solutions aimed at improvement in charging point functioning effectiveness it is necessary to consider the possibility to obtain additional revenue, e.g. on advertisements placed on devices/device screens, or on sales from vending machines situated close to chargers. A benefit and thereby an additional source of financing may consist of increased sales in shops and restaurants situated nearby because the waiting time related to charging can be used for shopping or eating. One can refer to the synergy effect when considering the charging station location as a method to increase the revenue of shops, restaurants or other service points situated in its vicinity and benefiting from the free time of electric car drivers and passengers. The recommendation that private sector entities should be operators of publicly available charging stations, selected in open competitive procedures to establish stations at chosen locations, or independently deciding about the location and establishment of vehicle charging stations, results from the fact that basic differences exist between targets and methods of private and public sector functioning. The private sector, including in particular entities operating on competitive markets, is forced into innovation, implementation of strategies to be ahead of the competition, offering products and services of increasingly higher standards, and the application of modern technologies and novel organization rules. Moreover, private entities have their shareholders, for whom the company assets, the size of profits, or amount of paid dividends are important. Projects undertaken by private sector entities are subject to assessment from the point of view of benefits, primarily financial ones, which they can and will provide in a shorter or longer period of time. Private sector entities involve their funds, and in the case of engaging external funds, they pay specific amounts for their use. Hence, they must realistically assess the undertaken projects, to prevent a loss of ability to pay, and later on—bankruptcy. However, funds are not only the entities’ only resources and values, but also human resources, technology, know-how, experience, and effective work organization may be also mentioned, and all that is subordinated to competitiveness,

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development, expansion of the existing market share, or entering new markets. That means that a private sector entity must generate financial surpluses and profits because only in this way the entity will survive on the market and will be capable of developing. The fact that private entities operate for profit and this causes that products are expensive, is frequently emphasized, hence it is better when goods are provided by the public sector. However, private entities usually operate on competitive markets, hence they must offer products and services for which there is the demand on the market, and at prices, which the customers will be willing to pay. It is the consumer who, choosing this or another supplier of a good, decides which of them will sell it, will obtain the appropriate funds, that will guarantee the entity survival and development. The situation in the public sector is different. In this case, there is no market verification of provided service costs—provision of numerous services is reserved to this sector only. Moreover, public funds and assets of the taxpayers are being managed with limited influence on the assets’ management. The rationality of public funds spending is to be guaranteed by rules and procedures stipulated by the law, resulting inter alia from the Act of 27 August 2009 on Public Finance [20] and from the Act of 29 January 2004 on Public Procurement Law. Taxpayers, and at the same time voters, have a possibility to elect representatives presenting specific economic opinions participating in elections to various levels of public administration (but even that does not guarantee that actions described in the programs are carried out, and the public funds are effectively managed). Reservations are raised in the literature in the field of public funds spending rationality. The fact that decision-makers in the public sector spend others’ money primarily for other people’s needs is a factor resulting in lower rationality of funds spending in the public than in the private sector [21]. Public administration spends not their own money. Only human goodness, and not much stronger and reliable incentive in the form of own interest, may impel to spend money in a most favorable way from the aid receivers’ point of view [21]. In many cases, spectacular projects are pursued in the public sector, expecting that as a result of their implementation a positive image will be achieved, even if the projects will cause losses in all assessment categories. The public sector carries out projects, which frequently will not be carried out by entities based on market conditions—however, unfortunately, there are numerous examples, in which the project costs are too high, completion deadlines extended many times, and the social usability leaves a lot to be desired. Frequently, it is possible to have reservations about feasibility studies evaluating projects carried out based on public funds. There are also situations that feasibility studies are missing, and sometimes they are prepared only because of formal reasons, assuming the necessity and profitability of specific project implementation. There are rare cases, in which a public sector entity awarded a contract to perform a feasibility study of a planned project and the study outcome was that the project should not be implemented, i.e. the NPV (net present value) and the ENPV (economic net present value, i.e. considering costs and external benefits) are negative. As a rule, social benefits are evaluated high, in this way very large negative financial flows can be balanced.

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3 Charging Stations and Points with Limited Access: Residential Houses and Workplaces Irrespective of electric vehicle charging station development, the possibility of charging batteries during an overnight vehicle stop and/or during the stop close to the workplace—when the user is working—will be crucial for the increase in the number of electric cars possessed by individual owners. Charging overnight will be usually cheaper because it occurs during reduced demand for electricity. Moreover, the user—during charging overnight or during work—does not lose time for waiting during charging, as it is the case of publicly available charging stations. The vehicle battery charging overnight is not an issue for a person, who has a garage or a driveway within a specific property. Overnight charging is carried out most frequently in single-family houses, having a proper demand contracted. Although one can make reservations about situations in which an increase in power generates a procedure, like in the case of connecting a new facility to the network, while from the wiring system point of view this is only a formal consent. An issue appears in the case of vehicle users living in multifamily houses, up to now in underground car parks or in the vicinity of buildings which do not have parking places equipped with a possibility to charge batteries of electric vehicles. The population of multifamily development is estimated at almost half of society [22]. Public charging stations are a solution for them—but the charging costs there are much higher than in the case of a house or common private installation. Besides, that means another action during a day which takes time and limits the possibilities of car use [23]. Hence the creation of solutions, which facilitate vehicle battery charging at places of overnight vehicle stops—places of their users’ residence—is a condition for electromobility development. That requires such actions as [23]: • Introduction of changes to regulations, facilitating electric car owners to charge them in the area of housing communities and cooperatives • Preparation of technical solutions to handle and settle charges for the use of shared infrastructure • Introduction of solutions supporting the construction of public charging stations in streets or in places of overnight parking in neighborhoods, where new electric vehicles appear. Just the creation of charging points in places of residence and work may be considered a significant factor of electromobility development. The establishment of publicly available charging stations, on which the Directive 2014/94/EU of the European Parliament and of the Council of 22 October 2014 on the deployment of alternative fuels infrastructure focuses, obviously will facilitate the use of electric cars in moving on longer distances, but it will not substitute solutions facilitating the creation of individual and private infrastructure for vehicles charging. The Directive of the European Parliament and the Council concentrates on issues important from the Union point of view and does not impose on member states solutions of local dimension, and the elimination of unnecessary procedures and

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facilitation of individual infrastructure for electric vehicle battery charging should be the basis of actions, the more so that individual users or housing communities establishing their installations will incur the majority of costs and will not burden public sector budgets with them.

4 Charging Infrastructure of Public Transport The establishment of charging systems for battery electric buses is another issue. (Various charging solutions are discussed in chapter “Electric Buses: A Review of Selected Concepts Solutions and Challenges”.) There are different solutions adopted among cities. It should be noted, however, that the choice of technical solution and its effectiveness is affected by many factors, e.g. [24, 25]. As public transport operators are responsible for the operational work performance, the practice of building charging facilities develops. This is also the safest solution in the initial phase of electric bus systems implementation (there is no concern of incompatibility of charging systems and buses). In general, operators select an entity to deliver and install charging facilities in depots and across the network. If the purchase of electric vehicles by operators does not raise any discussion (albeit also, in this case, it is possible for the organizer to purchase vehicles), the issues related to the establishment of vehicle charging infrastructure outside those entities’ depots are to be considered. This, in particular, applies to big cities, conurbations, metropolitan areas, in which public transport is carried out by at least a few urban public transport operators (and also in the areas where regional bus transport lines stop). Utilizing charging facilities across the transport network (outside depot) may result in different charging systems in one metropolitan area, dedicated to individual operators due to technical reasons. The ex-post standard unification to ensure interoperability will be related to additional costs. Another issue concerning the operation of one charging facility by various operators is the need for the power increase of the power service line. An example of an individual approach to bus charging stations construction may be the central part of the Silesian Voivodeship (Poland), where the purchases of battery electric buses are made independently by various public transport operators: Przedsiębiorstwo Komunikacji Miejskiej Sp. z o.o. (Municipal Transport Enterprise, Ltd., PKM) in Sosnowiec, Przedsiębiorstwo Komunikacji Miejskiej Sp. z o.o. in Gliwice, and Przedsiębiorstwo Komunikacji Miejskiej Katowice sp. z o.o. Each of the above-mentioned entities independently develops the charging facilities across the transport network. Bus lines served by these operators have common areas, e.g. PKM in Gliwice or PKM in Sosnowiec serve lines terminating in Katowice. Another example is the bus line served by PKM Jaworzno running to Katowice; the latter carrier increases the number of operated electric buses and the need to charge batteries in Katowice will seem necessary soon. An intention to purchase a significant number of electric buses was expressed by the Metropolis of Upper Silesia and Zaglebie (the area in the central part of the Silesian Voivodship

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where these carriers operate), in cooperation with the National Centre for Research and Development. Also in this case the necessity to establish battery charging facilities will originate soon. In general, the establishment of infrastructure—including the infrastructure for transport needs—belongs to the public sector to a significant degree (including government and local government administration). Public funds are the majority of expenditures on the infrastructure, originating from the state budget or local government budgets, European Union aid funds, or the money of funds created for that purpose. Obviously, the infrastructure is also financed from charges paid by the users—both entities and individuals. Benefits resulting from appropriate infrastructure cannot be overvalued—benefits on a country, region, or city scale, obtained by the society as a whole or by various social groups or entities. Substantial benefits are also articulated in relation to electromobility development, especially in city centres—they include primarily emission reduction. Considering the fact that urban public transport to a substantial extent is financed from public funds [14, 26], the creation of electric bus battery charging infrastructure by the organizers (cities, or established for those purposes entities, e.g. city associations) could be indicated as a postulated solution. This may ensure establishing an optimal network of charging points for the entire area (not from one or a few operators point of view), as well as equal—non-discriminatory access to chargers by various operators. Furthermore, the unification of technical standards for charging will occur. Moreover, there will be one entity against the electricity supplier, which should reduce the purchase costs without additional agreements for common electricity purchase. It is also necessary to add, that the location of the bus battery charging infrastructure management has and will have an impact on the operation in the urban public transport market. The situation, where charging points across the network will be developed by operators, and—in legal terms—they will manage those points, may result in the fact that the single-source procurement will be the only mode of awarding a contract to serve a line by battery electric buses that require charging in those points and the contract award will depend on the charging points’ manager. After Poland’s accession to the European Union and the implementation of the EU legislation to Polish legal order, the open competitive procedures of awarding contracts on transport services provision are systematically being abandoned, entrusting provision of transport services to internal entities. The electromobility induction in the urban public transport, and in particular the lack of solutions creating access to battery charging infrastructure for various entities, will consolidate the mechanism of contracts signing with bus operators, without tender procedures, in a similar way as it was the case of tram or trolleybus operators. Separation of bus battery charging infrastructure across the network from the operators will also mean greater cost transparency, identification of infrastructure costs, and costs of vehicle operation. It is also necessary to consider, whether these chargers during the night periods could be used to provide energy for other electric vehicles, e.g. municipal ones. Electric bus prices are now significantly higher than those with conventional drives, i.e. Diesel engines [27]. One needs to add the cost of a battery charging

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facility creation and operation to the purchase price of the bus itself. Higher bus prices and the costs of additional infrastructure are not balanced by lower electricity costs. It is also difficult to assume that the expenditures on electricity purchase will be lower in the future (than those incurred for liquid fuels purchase in the situation of the operation of conventional vehicles). Already now the entities generating and distributing electricity in Poland expect an increase in electricity prices, and this increase is retarded due to social reasons, but this situation will not continue for a long period of time. Attention should also be drawn to fuels burdening with various taxes feeding the state budget and special-purpose funds. In the case of reduced fuels consumption, it is possible to presume, that to ensure relevant income such taxes will burden the electricity. And finally—the electromobility development will generate a significant increase in electricity demand. The latter, in turn, will generate the necessity to invest in the power industry and hence to incur additional costs, to ensure appropriate electricity supply. In this place, it is also necessary to draw attention to the cumulation of factors leading to an increased level of public financing of urban public transport. Battery electric buses operation means higher costs—as it was before due to their higher prices, the necessity to build infrastructure, and—what can still become realistic— higher rates for unit operational work, resulting from the lack of market, and open —competitive awarding contracts on transport services. Also, lower effectiveness of electric vehicles use can be added to that. Conventional buses fueling last a dozen or so minutes, hence such a vehicle could serve lines from early morning hours, till late night hours. For electric buses that will be not necessarily the case, because the time necessary to charge batteries will reduce the time of vehicle operability to serve bus lines. Another cost factor consists in the necessity to maintain two vehicle systems, conventional and electric buses. Hence assuming that the revenue on tickets sale will be constant, a higher cost related to electromobility implementation will translate into the necessity to assign more public funds by municipalities, or while maintaining the hitherto level of funds—to reduce the transport offer. The reduction of public transport offer due to electromobility implementation is not an unacceptable solution, because then the shift of passengers to individual transport will cancel the expected effects of emission reduction, being a result of electromobility introduction. The electromobility is and will be implemented with the support of various funds supporting public transport development and environmental protection. However, a more expensive bus and an additional cost of battery charging infrastructure, even if partly financed by non-repayable external funds, will anyhow mean higher depreciation charges, so that after the end of service life the replacement of vehicles and infrastructure would be possible based on own funds. These relationships can be changed only by making electric buses cheaper. Facing increasing expenses related to electric buses introduction it is necessary to carefully approach the introduction of tariff changes resulting in the reduction of revenue on tickets sale, e.g. expansion of groups entitled to free travelling or introduction of fare-free public transport. Also, the rules of battery charging infrastructure financing should be considered. Obviously, it is possible to charge for charging using a principle, that all the

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incurred costs will be covered. The fact of making them available also to neighbouring areas operators, or in the future operators providing transport services of regional reach, supports this solution. Another solution may be also adopted— electromobility support and implementation of the next electric buses will be achieved by preferential prices or free battery charging. Then in areas, where urban public transport is organized for many municipalities, the financing of battery charging infrastructure should not rest on the municipality, in which it is situated. It should be financed by the urban public transport organizer.

5 Conclusions The vehicle battery charging infrastructure is a significant factor affecting the purchase and use of electric vehicles. It is possible to mention both the charging points accessibility, conditions in which the driver and possibly passengers wait, a possibility to use additional services, and also the electricity price itself. In this context the target model of publicly available charging systems functioning is important—whether and to what extent the public administration interference is advisable, and to what extent the electricity sale is and should be carried out by private sector entities and financed from private funds. Documents of the European and national policy level indicate the need for electromobility development, especially in big cities, seeking in them the reduction of CO2 emission originating from motor vehicles and reduction of demand for liquid fossil fuels. That means involvement of the state—by means of legal and financial instruments—in the stimulation of development of charger’s system accessible to the public. However, the state involvement may be justified by the initial period of electromobility, when the number of operated electric vehicles is not large, and the charger’s network accessible to the public should ensure appropriate spatial accessibility of charging points. However, the state involvement should not be a permanent process, charging networks are already now created by private entities and this should be—as an assumption—the target model of functioning. Several entities having charging networks accessible to the public should operate on this market, then they will compete for customers—both by the attractiveness of tariff plans, and non-price features affecting a given network attractiveness. Obviously, cooperation is beneficial for the entire sector, as well as the unification of standards (not only technical but also offering attractive tariff plans to users, irrespective of with which network the user concluded a permanent contract). In the context of considerations about the involvement of the public and private sector in the creation of electric vehicles battery charging infrastructure, it is necessary to notice that electric vehicles charging at home or in workplaces has a dimension of private projects. In this case, the support would consist in facilitation and acceleration of procedures, e.g. related to the increase in the power consumption in the existing service lines or modification of the existing ones into such,

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which can supply electric vehicles. In situations, in which—especially in cities—the establishment of publicly available charging points is expected at places indicated by the public administration—and the land property is publicly owned, it is also not necessary that the city or a special city unit would manage that point. It is possible to use public procurement or public-private partnership procedures to find a private entity and thereby to ensure a given service of electric vehicles charging. The assumption of permanent financing from public funds of current operations of electric vehicle battery charging points is difficult to make. It is necessary to emphasize that electricity is burdened with value-added tax (VAT) and excise duty, and in this case, any exemptions or reductions of the burden can start a discussion about public financing and reducing the price of electricity used—instead of coal— for households heating. In the case of households electric heating dissemination, the environmental benefits would be significant, and also simpler and quicker to achieve. The replacement of coal-fired ovens with electric heaters does not require batteries and dedicated charging infrastructure. In the case of infrastructure used to charge public transport buses, the solutions may vary. If there is only one operator of urban public transport in the city, the system may be created and managed by that operator. In big conurbations served by a few or more than ten operators, the situation is different. The creation of charging infrastructure for electric buses by public transport organizers—cities or special-purpose entities established for that purpose, e.g. city associations, will be a postulated solution in this case. This will ensure establishing an optimal network of charging points for the entire area, but not from one or a few operators point of view, as well as equal—non-discriminatory access to chargers by various operators. Also, the unification of technical standards for charging will be beneficial. Irrespective of that, a solution will be created, in which the energy purchase will be concentrated in one entity, so it will be stronger with respect to the energy seller, which should reduce the purchase costs without additional purchase agreements.

References 1. Hall D, Lutsey N (2017) Emerging best practices for electric vehicle charging infrastructure, White Paper. In: The international Council on Clean Transportation, Beijing, Berlin, Brussels, San Francisco, Washington. https://theicct.org/sites/default/files/publications/EV-chargingbest-practices_ICCT-white-paper_04102017_vF.pdf 2. Fishbone A, Shahan Z, Badik P (2017) Electric vehicle charging infrastructure: guidelines for cities. Report, CleanTechnica, Greenway, Warsaw. https://cleantechnica.com/files/2018/04/ EV-Charging-Infrastructure-Guidelines-for-Cities.pdf 3. Schroeder A, Traber T (2012) The economics of fast charging infrastructure for electric vehicles. Energy Policy 43:136–144. https://doi.org/10.1016/j.enpol.2011.12.041 4. Gómez San Román T, Momber I, Rivier Abbad M, Sánchez Miralles Á (2011) Regulatory framework and business models for charging plug-in electric vehicles: infrastructure, agents, and commercial relationships. Energy Policy 39(10):6360–6375. https://doi.org/10.1016/j. enpol.2011.07.037

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5. Madina C, Zamora I, Zabala E (2016) Methodology for assessing electric vehicle charging infrastructure business models. Energy Policy 89:284–293. https://doi.org/10.1016/j.enpol. 2015.12.007 6. Europe 2020 A strategy for smart, sustainable and inclusive growth, COM (2010) 2020 final, European Commission, Brussels, 3 Mar 2010. https://eur-lex.europa.eu/legal-content/EN/ TXT/PDF/?uri=CELEX:52010DC2020&from=en 7. White Paper: Roadmap to a Single European Transport Area—Towards a competitive and resource efficient transport system, COM (2011) 144, European Commission, Brussels, 28 Mar 2011. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0144:FIN: EN:PDF 8. Clean Power for Transport: A European alternative fuels strategy, COM (2013) 17 final, European Commission, Brussels, 24 Jan 2013. eur-lex.europa.eu/LexUriServ/LexUriServ.do? uri=COM:2013:0017:FIN:EN:PDF 9. A European Strategy for Low-Emission Mobility, COM (2016) 501 final, European Commission, Brussels, 20 July 2016. https://ec.europa.eu/transparency/regdoc/rep/1/2016/ EN/1-2016-501-EN-F1-1.PDF 10. Utility investment in electric vehicle charging infrastructure: key regulatory considerations (2017). M.J. Bradley & Associates, Georgetown Climate Center. https://www. georgetownclimate.org/files/report/GCC-MJBA_Utility-Investment-in-EV-ChargingInfrastructure.pdf 11. Mathieu L (2018) Roll-out of public EV charging infrastructure in the EU. Is the chicken and egg dilemma resolved? A study by the Transport & Environment. https://www. transportenvironment.org/sites/te/files/Charging%20Infrastructure%20Report_September% 202018_FINAL.pdf 12. Directive 2014/94/EU of the European Parliament and of the Council of 22 October 2014 on the deployment of alternative fuels infrastructure. Official Journal of the European Union, 28 Oct 2014. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX: 32014L0094&from=EN 13. Ustawa z dnia 11 stycznia 2018 r. o elektromobilności i paliwach alternatywnych. tekst jednolity Dz. U. z 2019 roku, poz. 1124, 1495, 1527, 1716 (Act of 11 January 2018 on Electromobility and Alternative Fuels, Unified text: Dz. U. of 2019, item 1124, 1495, 1527, and 1716). http://prawo.sejm.gov.pl/isap.nsf/DocDetails.xsp?id=WDU20180000317 14. Dydkowski G, Urbanek A (2011) Partnerstwo publiczno-prywatne (Public-Private Partnership), Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach. University of Economics in Katowice Publishers, Katowice 15. Ustawa z dnia 10 kwietnia 1997 r. Prawo energetyczne, tekst jednolity Dz. U. z 2019, poz. 755, 730, 1435, 1495, 1517, 1520, 1524, 1556, 2166 (Act of 10 April 1997—Power Law, Unified text: Dz. U. of 2019, item 755, 730, 1435, 1495, 1517, 1520, 1524, 1556, and 2166). http://prawo.sejm.gov.pl/isap.nsf/download.xsp/WDU20190000755/U/D20190755Lj.pdf 16. Ustawa z dnia 21 października 2016 r. o umowie koncesji na roboty budowlane lub usługi, Dz. U. 2016, poz. 1920 (Act of 21 October 2016 on the Concession on Construction Works or Services, Dz. U. of 2016, item 1920). http://prawo.sejm.gov.pl/isap.nsf/download.xsp/ WDU20160001920/U/D20161920Lj.pdf 17. Ustawa z 24 stycznia 2004 r. Prawo zamówień publicznych, Tekst jednolity Dz. U. z 2019, poz. 1843 (Act of 24 January 2004—Public Procurement Law, unified text Dz. U of 2019, item 1843). http://prawo.sejm.gov.pl/isap.nsf/download.xsp/WDU20040190177/U/ D20040177Lj.pdf 18. Ustawa z 19 grudnia 2008 o partnerstwie publiczno-prywatnym, Tekst jednolity Dz. U. z 2019, poz. 1445, 1572, 2020 (Act of 19 December 2008 on Public-Private Partnership, unified text Dz. U. of 2019, item 1445, 1572, and 2020). http://prawo.sejm.gov.pl/isap.nsf/download. xsp/WDU20090190100/U/D20090100Lj.pdf 19. General Directorate of National Roads and Motorways (GDDKiA). https://www.gddkia.gov. pl/pl/3826/Aktualne-przetargi-MOP

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20. Ustawa z dnia 27 sierpnia 2009 roku o finansach publicznych, Tekst jednolity Dz. U. 2019, poz. 869, 1622, 1649, 2020 (Act of 27 August 2009 on Public Finance, Unified text: Dz. U. of 2019, item 869, 1622, 1649, and 2020). http://prawo.sejm.gov.pl/isap.nsf/download.xsp/ WDU20091571240/U/D20091240Lj.pdf 21. Friedman M, Friedman R (1980) Free to choose: a personal statement. Harcourt Brace Jovanovich, New York and London 22. Ponad połowa Polaków mieszka w domach jednorodzinnych (More than a half of Poles live in single family houses). https://www.bankier.pl/wiadomosc/Ponad-polowa-Polakowmieszka-w-domach-jednorodzinnych-7731472.html 23. Czyżewski R.: Ułatwienie dostępu do ładowania w nocy jest niezbędnym elementem rozwoju elektromobilności (Facilitation of access to charging overnight is a necessary element of electromobility development). https://www.linkedin.com/pulse/u%C5%82atwienie-dost% C4%99pu-do-%C5%82adowania-w-nocy-jest-elementem-rafal-czyzewski/ 24. Karlsson E (2016) Charging infrastructure for electric city buses: an analysis of grid impact and costs. Examensarbete Inom Teknikområdet Energi Och Miljö Och Huvudområdet Elektroteknik, Avancerad Nivå, 30 Hp, KTH Royal Institute of Technology, Stockholm. http://www.diva-portal.org/smash/get/diva2:967688/FULLTEXT01.pdf 25. Hooftman N, Messagie M, Coosemans T (2019) Analysis of the potential for electric buses: a study accomplished for the European Copper Institute, Vrije Universiteit Brussel. https:// leonardo-energy.pl/wp-content/uploads/2019/02/Analysis-of-the-potential-for-electric-buses. pdf 26. Dydkowski G, Gnap J (2019) Premises and limitations of free public transport implementation. Commun—Sci Lett Univ Zilina 21(4):13–18 27. Wyszomirski O, Wołek M, Jagiełło A, Koniak M, Bartłomiejczyk M, Grzelec K, Gromadzki M (2018) Elektromobilność w transporcie publicznym, Przewodnik dla jednostek samorządu terytorialnego, przedsiębiorstw użyteczności publicznej i prywatnych przewoźników. Praktyczne aspekty wdrażania, Raport Specjalny, Polski Fundusz Rozwoju, Polskie Stowarzyszenie Paliw Alternatywnych (Electromobility in the public transport, Guide for local governments, public enterprises and private carriers. Practical aspects of implementation, Polish Development Fund, Polish Alternative Fuels Association), Warsaw. http://pspa.com.pl/assets/uploads/2018/12/raport_PFR_elektromobilnosc_w_transporcie_S. pdf

Technical Issues Related to Electromobility in Public Transport

Electric Buses: A Review of Selected Concepts Solutions and Challenges Teresa Pamuła and Stanisław Krawiec

Abstract The chapter aims to review the most popular topics and most commonly implemented solutions related to the introduction of electric buses into the urban transportation fleet. Primary problems are costs and battery charging technologies. The costs and the choice of charging technology depend on the preliminary estimation of the energy demand and the choice of tracks of electric bus. Energy saving in public transport is also a significant problem. Applying innovative technologies for powering and charging electric buses in practice is very challenging. The chapter presents the most frequently discussed topics concerning the problems of introducing electric bus to the public transport system based on selected recent publications of the matter. Keywords Electric buses


Charging technologies
Public transport

1 Introduction The public transport is crucial for the functionality of urban systems. Environmental issues related to the consumption of fossil fuels and CO2 emissions force the transport sector to switch to sustainable energy sources with low impact on the environment. The public transport system, which is an integral part of the multimodal transport ecosystem, tries to meet such environmental demands by exploring the possibility of using electric vehicles. In recent years, the development of electric buses and technologies supporting their infrastructure has made them a viable replacement for diesel and compressed natural gas-powered buses. However, the challenge remains for the optimal configuration of the electric buses system due to their unique space-time characteristics [1]. T. Pamuła (&)
S. Krawiec Silesian University of Technology, Katowice, Poland e-mail: [email protected] S. Krawiec e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_5

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The development of electric buses accelerated rapidly over the past five years, the global fleet of electric buses estimated at 345,000 vehicles in 2016 (twice more than in 2015). The largest share (nearly 90%) has China [2]. Although Europe’s share in the global electric fleet is not very significant, European industries play an important role in researching and developing new solutions in the field of electrification of transport [3]. European Union (EU) promoted the development of electric vehicles [4]. EU policy in the field of sustainable transport is based on the European Commission’s White Paper entitled “Roadmap to a Single European Transport Area—Towards a competitive and resource-efficient transport system”, which aim is to reduce greenhouse gas emissions from transport by 60% by 2050 (compared to 1990). The European Union supports the implementation of strategies related to the introduction of electric buses into public transport networks financially and by means of legally binding acts. Member states are free to define their own specific national strategies. The EU subsidizes demonstration projects of electric buses under the FP7 (Seventh Framework Programme for Research and Technological Development) and Horizon 2020 programs. FP7 launched the ZeEUS (zero-emission urban bus system) with more than 40 participants in the consortium and a budget of 22 million euros [3, 4]. Many countries, including France, Germany, Italy and the United Kingdom, establish a national legal framework that will continue to encourage the deployment of electric buses for public transport [3]. In Sweden, it is planned that by 2030, 80% of urban buses will be electrically powered and it is estimated that by 2050 the share will reach 100%. The prepared study of popular concepts related to the introduction of electric buses into the urban public transport fleet is based on several dozen selected publications from the past few years. The paper discusses the current and emerging challenges in this research field. Papers within this study were selected so that the following research problems can be addressed: (1) What are the current requirements and the main factors affecting the costs of introducing electric buses for urban transport? (2) What types of charging stations and charging technologies are currently used and how will they evolve in the future? (3) How to estimate and minimize energy consumption in electric buses? (4) What are the problems when optimizing the charging schedules and charging infrastructure? The results of this study are presented and discussed in the following sections of this paper. Papers selected for presentation and discussion were published, in years 2015–2019, in peer-reviewed Journals indexed in the IEEE Explore, Web of Science and Google Scholar databases. Section 2 discusses the cost models of electric buses deployment. Section 3 describes the charging requirements of electric buses and the interrelations with the battery charging technology used. Section 4 presents the methods for estimating

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and optimizing energy demand. Section 5 provides answers related to the question on how to solve the problems of optimizing the charging schedules limited by costs, charging infrastructure and ranges of the electric buses. The last section contains a summary of the discussed problems of introducing electric buses into urban transport networks and presents possible development directions in this field.

2 Cost Analysis One of the most important elements influencing the decision to introduce electric buses is the cost analysis. The paper [5] presents an analysis of the costs of operating electric buses on various routes. The aim was to determine the requirements for charging power and battery type, as well as energy consumption and life cycle costs of electric buses. In order to comprehensively evaluate electric buses in various working conditions, a special simulation tool has been developed. Based on simulation results and predefined cost parameters, life cycle costs were calculated for various operational scenarios. Charging methods include overnight charging, end stations and fast opportunity charging (on the route). Simulation results for four operational routes have been presented. The results have been tested on existing bus lines in Finland and California (USA). The results showed that the high battery capacity of the battery system is essential for overnight charged buses to enable daily use. Moreover, the authors show that the size of the battery has a small impact on energy consumption and life-cycle costs of buses with fast charging. Figure 1 presents a model of the cost analysis of electric buses introduction to the urban transport network.

Fig. 1 The costs analysis of electric buses introduction (Source Own study based on [5])

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The costs of charging systems can vary greatly. The total costs are affected by the conditions for each specific case, for example space availability, local conditions of the electricity network and characteristics of selected fast charging infrastructure options. The cost of overnight charging is significantly lower for wired charging infrastructure. Inductive fast charging technology in case of overnight charging generates higher costs an order of magnitude [6]. Despite the higher costs, fast charging is worth considering, because it offers other benefits, such as the use of smaller batteries. The latter can significantly reduce vehicle costs and environmental impact throughout the bus life cycle. In addition, the simultaneous charging of large fleets of electric buses in the future would impose concentrated, high energy requirements in the energy network, the capacity of which may not be able to fulfil future charging stations demands. Therefore, it would be necessary to spatially and temporally distribute the charging points in order to increase the scale of use of electric buses (and other electric vehicles). The paper [7] presents the cost competitiveness of various types of charging infrastructure, including charging stations, charging routes (using charging technology while driving) and battery exchange stations. The aim of the study was to support the electric public transport system. For optimal placement of various charging devices along the transit line, determining the optimal size of the electric bus fleet, as well as their batteries and minimizing the costs of the fleet and infrastructure while guaranteeing the frequency of service and satisfying the needs of charge the transit system, mathematical models were proposed. The empirical analysis was carried out using available data from around the world. The obtained results suggest that: • The frequency of services, the length of routes and the operating speed of the transit system can have a major impact on the cost competitiveness of different charging systems. • Charging tracks equipped with currently available inductive wireless charging technology are competitively priced for the majority of existing high-speed bus corridors. • For high-speed and low-frequency transit systems, replacement stations can generate a lower total cost than charging tracks and charging stations. • Charging stations are competitively priced only for transit systems with very low frequency of services and short circulation. • The key to increasing the competitiveness of charging tracks for low frequency handling systems and high working speeds is to reduce their construction cost by an order of magnitude or increase their charging power. The life-cycle cost model from [7] is based on the model that the author has presented in his previous studies [8]. Three main cost areas have been included in the life-cycle cost model of electric urban buses: capital costs (CCAP), maintenance costs (COP) and technology replacement costs (CREP). Capital costs include the costs of buying a bus, the initial costs of charging devices and the costs of

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recovering (modifying) buses and charging devices. Technology replacement costs relate to battery replacements due to technological obsolescence and are not considered as maintenance costs. The analyzed bus fleet life cycle costs can be presented as: CLC ¼ CCAP þ COP þ CREP

ð1Þ

In terms of life-cycle costs, the electric drive is still more expensive than the use of internal combustion engines. However, the mass production of electric buses and fuel cell buses may potentially gradually decrease the costs. The rapid evolution of e-mobility opens up opportunities for both the environment and the market. Combined with the synergy between e-mobility and energy production, the concept of the intelligent transport network and the smart city was created. These concepts were formulated for the first time in urban literature and in urbanization trends [9]. The paper [10] analyzes the factors that impact demand charges of a fleet of electric buses, such as charging strategies and fleet size, using a model of electric energy consumption by electric buses in combination with the charging strategy model. It has been shown that simply adopting an appropriate charging strategy can lead to significant savings in demand-related charges. The proposed model shows that more frequent charging can lead to a reduction in demand charges, which is contrary to the popular practice where drivers maximize the distance between charges. This discovery may inspire public transport companies to modify the electric bus driver training programs to achieve the greatest savings. In cities where demand charges are more expensive or a shorter demand period (e.g. 15 min) is applied, savings can be even greater. Studies show that the cost per mile can be reduced when the fleet size and charging strategies are optimized.

3 Electric Buses Related Technology Issues The technical parameters analysis includes the charging method, driving route, work schedule, bus configuration and additional energy consumption, which depends on the weather conditions of the surroundings, as described in [8]. Figure 2 presents a diagram of the analysis of technical issues related to the introduction of electric buses into the urban transport network. The selection of the charging strategy is related to the propulsion technology used, as well as the size of the battery and the available budget for infrastructure investments. The slow charging strategy is also known as overnight charging. This technology allows to charge in an air-conditioned warehouse or in a car park for a long time. Fast charging, also known as charging opportunity, requires the installation of a charging station with high current charging capability. Charging technologies are one of the crucial factors which impact battery introduction of electric transport into urban network. Charging can be done in stationary mode (when the vehicle is stopped) or dynamically (when the vehicle is

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Fig. 2 Issues related to the introduction of electric buses into public transport network (Source Own study based on [8])

moving). Conductive charging technologies (stationary mode) can be further divided into overhead or ground/underground solutions. Induction charging is an underground solution and can be stationary or dynamic. A comparison between stationary conductive technologies and inductive charging can be found in [6]. The analysis excludes dynamic induction charging technology, because the technology is still at a very early stage of development and is not yet tested on buses in Scandinavia. In paper [7] the authors studied the cost-competitiveness of different types of charging infrastructure, including charging stations, charging route (using the charging technology while driving) and a battery exchange station. The research was aimed at supporting the electric public transport system. For optimal placement of various charging devices along the transit line, determining the optimal size of the electric bus fleet, as well as their batteries and minimizing the costs of the fleet and infrastructure while guaranteeing the frequency of service and satisfying the needs of loading the transit system, mathematical models were proposed. Then an empirical analysis was performed using available data from around the world. The advantage of conductive charging is proven technology and higher maximum charging current compared to currently available alternative induction charging. On the other hand, inductive technology entails other benefits, such as the lack of moving parts, which could reduce the infrastructure maintenance costs as well as the minimum visibility of the equipment. The main problems that may have an impact on increasing the use of electric buses are related to maturity, cost-effectiveness, compatibility and charging technology available. Dynamic wireless energy transfer technology, which provides bus operators with the ability to charge buses while driving, can effectively reduce some disadvantages of electric buses. The paper [11] addresses the problems of choosing the optimal location of dynamic energy transfer technology objects and designing the optimal size of electric bus batteries for the electric bus system. The results show that the proposed deterministic model can effectively determine the allocation of devices

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and sizes of electric batteries for the electric bus system dynamic energy transfer technology and a robust model can further provide optimal designs that are resistant to the uncertainty of energy consumption and travel time.

4 Estimation and Optimization of the Electric Buses Energy Demand for the Public Transport Network Many state-of-the-art approaches to determining the energy demand of electric buses use individual energy demand or based on standard driving cycles, but often do not take into account the characteristics of the local route. Others require high-resolution measurement of vehicle driving profiles, which is impractical for large bus fleets. The paper [12] presents a dynamic model for calculating the energy demand for electric buses. The model has been designed so that it can be easily used in large bus networks using real data sources that are widely available to bus operators. These can be low-resolution data collected from daily operations, where only the time of arrival and departure of buses at each stop is available. This approach is a practical alternative to modern methods and does not require any high-resolution speed profiles that are difficult to obtain while taking into account the operational details of the transport network under consideration. The application of the model is presented on the example of electrification of the public bus network in Singapore. The results show that a variety of driving conditions observed in a large network leads to a large variation in energy demand between different bus lines and at different times of the day. This confirms the need to take into account the characteristics of individual bus routes. In the case study, a fully electric public bus fleet would require around 1.4 GWh per day for transport services, which is about one percent of Singapore’s daily energy needs. It was found that half of the existing bus lines require less than 40 kWh for the journey to the end of the route in the worst case, and less than 31 kWh in the median. As fast charging is becoming did more popular common (only a few minutes of charging). Opportunity charging at the ends of the line during a stopover seems feasible for a large number of existing bus lines. The energy demand calculation model algorithm implements the following steps: 1. Acquisition of bus operation data (arrival and departure times from each stop). 2. Calculation of energy demand for each trip from the stop to the bus stop. 3. Calculation of the total energy demand for each vehicle for travel to the final station. 4. Calculation statistics for each bus line. The problem of saving electricity in public transport has become a key area of interest in recent years. By improving driving techniques and implementing

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eco-driving, you can save electricity. Systems that help reduce energy consumption and reduce emissions are popular in diesel-powered vehicles. Problems of optimising driving techniques of electric vehicles require the evaluation of individual parameters impacting energy consumption. In the paper [13] the performance indicators of electric bus driving technique were proposed. Selected parameters impact energy consumption are: • • • • • • •

Time of drive. Maximum speed of drive. Average value of speed. Average of speed during braking time. Average and maximum value of acceleration. Duration of coasting driving. The relative length of drive section.

The driver support system should be based on measuring and tracking the maximum speed value. The speed reference value may be indicated on the basis of vehicle movement tests carried out, for example, by the operator of the transport system. During driver training, emphasis should be placed on reducing speed in road traffic, and above all on avoiding unnecessary significant acceleration and immediate braking. If the bus is moving at a higher speed than the one specified in the schedule, its movement should first be corrected and the driving speed decreased and only then stop times at bus stops should be increased. Therefore, to further inform the driver about the recommended speed, the system that connects the IT system with the driver assistance system becomes a sensible and innovative solution. The on-line eco-driving strategy for plug-in hybrid electric buses has also been proposed in [14]. Optimal reduction of energy consumption within acceptable limits was obtained based on the driver’s behaviour and the condition of the vehicle. The strategy consists of two procedures: offline optimization and online implementation. First, offline, dynamic programming is used to optimize the power profile. Next, a neural network generates an online control strategy based on the offline optimization results. The neural network has four inputs: the position of the accelerator pedal, vehicle speed, battery charge status and vehicle acceleration. The network output is the power adjustment value. Simulation using driving cycle test data indicates a 2.72% reduction in fuel consumption compared to a strategy without eco driving. The features and computing capabilities of neural networks favour their wide application for solving transport problems [15, 16]. Paper [17] proposes a solution to the problem of planning the number of electric buses, in the fleet composition and optimization of charging infrastructure as part of a joint process. In the case study, two scenarios for European cities were analyzed showing that the type of bus (large and small buses) and its technical specification affected the cost structure and energy consumption considerably. However, the total cost of ownership for both types of buses is relatively close, due to the increased

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fleet size and driver expenses required for the light bus system. The case study also shows that a mixed fleet of different types of buses may be beneficial depending on the operational characteristics of the bus route. Light bus service allows energy savings of around 30%. Air conditioners usually consume the most electricity from all auxiliary components in an electric bus, which is over 30% of the maximum battery power [18]. Passengers transported by electric bus are important but they are accidental sources of heat, which are disruptions to temperature control and energy management in the air conditioning system. The aim of the research was to improve the energy efficiency of air conditioning by analyzing the variability of passenger numbers and the forecast within the predictive control model. A sample passenger database along a typical bus route in Beijing was used to analyze and forecast changes in passenger numbers. The results of the simulation show that energy consumption using the predictive control model was about 6% lower than in the rule-based control on the same bus route.

5 Optimization of the Charging Schedule for Urban Electric Buses In the literature for electric buses, various types of optimization studies have been presented, including: • Optimal selection and control of fast charging stations and energy storage system [19]. • Minimization of the number of charging stations and the average additional stop time at the station needed to charge electric buses [20]. • Optimization of the cost of building electric bus lines based on the number of e-buses, the battery level, the number of chargers and the cost of electricity [21]. • Electric mix bus (various number of small and large buses) fleet size and problem with optimization of charging infrastructure [17]. The authors of paper [22] developed a model for optimizing electric bus charging schedules, which defines both planning and operational decisions while minimizing total annual costs. The model has been tested in the urban public transport network in Davis, California. The results show that the charging problem can be solved by adopting specific charging strategies. The model can provide city public transport networks with comprehensive advice on the use of electric buses and the development of a fast charging system. Comparative analyzes have shown that the use of electric buses is more economical and environmentally friendly than diesel buses. Electric buses have a shorter range than buses with a diesel engine, so to ensure normal operation, several charging stations equipped with quick chargers should be strategically located in transit centers. Taking into account fixed driving schedules,

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the manager of the urban public transport network has to solve the following problems [22, 23]: • How many charging stations are needed in the transport network? • How many chargers are needed at each charging station? • How to plan the schedule of electric buses to ensure charging without any delays or overloading the charging station? An optimization model has been elaborated, taking into account both the configuration of the transit bus network and the technical characteristics of fast charging systems. The proposed optimization framework minimized the total operating costs of the electric bus charging system and determined location and capacity of charging stations and charging schedules. The optimization model was demonstrated using a transit network based in Davis, California. The results provide decision makers with the optimization of: local charging stations, the number of chargers needed, the electric bus charging schedule, and the corresponding costs. Sensitivity analyzes showed that the total number of recharging activities quickly decreased with the increase in the maximum range of the electric bus and charging time.

6 Conclusions The paper presents and discusses recent results on approaches to the general problem of introduction of electric buses into urban transport. The most significant achievements are collected, grouped and presented. Four main research problems are addressed. The implementation of electric buses into public transport networks requires from the management company an analysis of the number of vehicles, routes, type of power supply and the resulting investment costs. The first observation is that the commercially available options, as well as the variety of local conditions and service requirements, determine the choice of technology. The combination of charging technology and strategy involves more factors than just economic considerations. Modernization of old buses can be economically viable because if the bus is re-used, investment costs can be evaded as well as this reduces environmental pollution. Such a solution may be difficult to implement in some cases as not all buses are suitable for adapting to an electric drive. Choice of electric bus drive (conductive or inductive, or fast charging along the route) is related to previous guidelines and costs. It also depends on the scale of the use of electric buses. The choice of driving routes is also related to the type of power supply. As shown in Sect. 4, there are different methods for estimating energy consumption, but the method described in [12] deserves attention because it uses data that is widely available to bus transport operators, such as arrival and departure times of buses at bus stops. According to the authors, the method can be enhanced

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by using neural networks. Optimizing the energy consumption of an electric bus requires, above all, driver training because, as shown in [13], the differences in energy consumption for different driving habits, on the same route, can be as high as 50% of the total. When optimizing the charging schedule, it is important to choose the charging method and minimize the total costs of building charging stations by choosing appropriate locations in the road network. Conductive charging is advantageous as it is a proven technology, however, the technology requires high investments. The current challenge is the dynamic induction charging technology. It holds promise for the future but is still at a very early stage of development and has not yet been thoroughly tested in practice. The discussion proves that there exist numerous and important challenges emerging in this research field which will be further investigated soon. Acknowledgements The present research has been financed from the means of the National Centre for Research and Development as a part of the international project within the scope of ERANET EMEurope program ‘Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet.

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10. Qin N, Gusrialdi A, Brooker RP, T-Raissi A (2016) Numerical analysis of electric bus fast charging strategies for demand charge reduction. Transp Res Part A 94:386–396 11. Liu Z, Song Z (2017) Robust planning of dynamic wireless charging infrastructure for battery electric buses. Transp Res Part C 83:77–103 12. Galleta M, Massiera T, Hamacher T (2018) Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. Appl Energy 230:344–356 13. Bartłomiejczyk M (2019) Driving performance indicators of electric bus driving technique: naturalistic driving data multicriterial analysis. IEEE Trans Intell Transp Syst 20(4):1442– 1451 14. Liu T, Tian H, Tian G, Huang Y (2017) Neural network based online eco-driving strategy for plug-in hybrid electric bus. IEEE Veh Power Propul Conf (VPPC):1–5 15. Pamuła T (2016) Neural networks in transportation research—recent applications. Trans Prob 11(2):27–36 16. Pamuła T (2019) Impact of data loss for prediction of traffic flow on an urban road using neural networks. IEEE Trans Intell Transp Syst 20(3):1000–1009 17. Rogge M, van der Hurk E, Larsen A, Sauer DU (2018) Electric bus fleet size and mix problem with optimization of charging infrastructure. Appl Energy 211:282–295 18. He H, Yan M, Sun C, Peng J, Li M, Jia H (2018) Predictive air-conditioner control for electric buses with passenger amount variation forecast. Appl Energy 227:249–261 19. Ding H, Hu Z, Song Y (2015) Value of the energy storage system in an electric bus fast charging station. Appl Energy 157:630–639 20. Sebastiani MT, Lüders R, Fonseca KVO (2016) Evaluating electric bus operation for a real-world BRT public transportation using simulation optimization. IEEE Trans Intell Transp Syst 17(10):2777–2786 21. Ke BR, Chung CY, Chen YC (2016) Minimizing the costs of constructing an all plug-in electric bus transportation system: a case study in Penghu. Appl Energy 177:649–660 22. Wang Y, Huang Y, Xu J, Barclay N (2017) Optimal recharging scheduling for urban electric buses: a case study in Davis. Transp Res Part E 100:115–132 23. Wang X, Yuen C, Hassan NU, An N, Wu W (2017) Electric vehicle charging station placement for urban public bus systems. IEEE Trans Intell Transp Syst 18(1):128–139

Methodology for Probabilistic Assessment of Energy Consumption by Electric Buses on Routes Vladimir Algin, Arkadi Goman, Andrei Skorokhodov, Oleg Bytsko, Sergey Chistov, and Sviatlana Fedasenka

Abstract Approaches to the assessment of energy consumption of electric buses are analyzed and summarized. The results of experimental studies of the electric buses of the BKM Holding with various driving styles, loads, and interference on the route as well as operation data are presented. Energy consumption varies widely. The problem of choosing the calculated value for bus energy consumption is formulated. The solution is based on a probabilistic representation of space for numerous cases caused by the inevitable variation of operating factors on any bus route. Determining energy consumption for individual cases does not solve the problem; it is necessary to consider possible situations and justify the calculated (design) case. Typical probabilistic distributions of energy consumption are presented in the relative (dimensionless) form. A methodology is proposed for choosing a calculated value, which depends on the probability adopted by the interested party taking into account the risk of situations when the real value can exceed the accepted calculated value.




Keywords Electric bus Experimental studies Probabilistic approach Calculated value





Energy consumption


V. Algin (&)
A. Goman
A. Skorokhodov Joint Institute of Mechanical Engineering of NAS Belarus, Minsk, Belarus e-mail: [email protected] A. Goman e-mail: [email protected] A. Skorokhodov e-mail: [email protected] O. Bytsko
S. Chistov
S. Fedasenka BKM Holding, Minsk, Belarus e-mail: [email protected] S. Chistov e-mail: [email protected] S. Fedasenka e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_6

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1 Introduction Assessing energy consumption is one of the key problems associated with many aspects of the implementation of the electric bus fleet in various real operating conditions. The number of methods for evaluating the energy consumption of electric buses constantly increases. More sophisticated methods require more input data, which are not always available. Nowadays, real-time vehicle tracking is possible and prognosis energy consumption on this basis. (See, for example, https:// yandex.com/maps, the option “Public transport moving in real-time”.) But operational research is not comprehensive. They, like the data of modeling, are specific cases. Both calculated and operational estimates are faced with the problem of significant variation in the results due to the in-evitable action of factors such as driving style, loading, and interference on the route. There is a problem, how to evaluate energy consumption and use this value for decision making in different situations? This chapter proposes a radical solution that is based on two new provisions. Firstly, it is necessary to create a space of possible solutions in the form of a distribution of an indicator that describes the property under study (for example, energy consumption for the route cycle under consideration). Secondly, it is necessary to introduce an interested party (person) into the assessment procedure. This person must accept the probability with which the choice of the calculated (design) indicator of energy consumption is made. The chapter includes an analysis of assessment methods for energy consumption and factors determining it (Sect. 2), the results of experimental studies of the electric buses produced by BKM Holding (Sect. 3), the proposed probabilistic methodology for estimating energy consumption (Sect. 4) and conclusions.

2 Analysis of Energy Consumption 2.1

Typical Approaches for Assessment

Currently, there are many methods and tools for assessing the performance of vehicles, including their energy consumption. First of all, these are driving cycles that imply movement in accordance with a given speed profile and road condition. DieselNet [1] contains an extensive set of test programs (driving cycles) for vehicles and other equipment used in different countries. For the bus subject in question, the New York Bus (NYBus) cycle, developed by the US Environmental Protection Agency, is of particular interest. The NYBus is a cycle test for heavy-duty vehicles, representative of actual driving patterns of transit buses in New York City. The cycle consists of very rapid accelerations, followed by rapid decelerations to idle and long idling periods [2].

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In the context of bus topics, the UK Bus Cycle (UKBC) for certification under the Ultra-Low Emission Bus (ULEB) Scheme is also worth noting. The UKBC contains three test phases that simulate different areas of bus operation: Inner, Outer, and Rural [3]. The next known tool for testing buses is SORT methodology (“Standardised On-Road Test cycles”), which is an initiative of the UITP Bus Committee, first aimed at providing the bus sector with a standardized means of comparing the energy consumption of different buses when procuring at tender stage [4]. This methodology is close to driving cycles and uses a standardized set of trapezes to form the speed profiles. A criterion for the proximity of the SORT cycle and the real route is the key parameter: the same commercial speed Vc. The three SORT base cycles (modules) in the shape of trapezes are used to form an artificial SORT route. There are: (1) Heavy urban cycle (Vc = 12 km/h), (2) Easy urban (mixed) cycle (Vc = 18 km/h), (3) Easy suburban cycle (Vc = 25 km/h). Getting a mixed cycle based on the mentioned typical trapezes is the responsibility of the operator (interested party). The driving cycles and SORT methodology are designed for measurements of bus performance under standardized operating conditions. Such measurements cannot reflect the specific application of buses (vehicle configuration, topography, driver style, climate, loading conditions, etc.). Another approach is the use of data on the operation of similar diesel buses on specific routes. A typical example is presented in [5]. To reflect extensive real-world bus driving conditions, the Oak Ridge National Laboratory medium-duty conventional bus database (https://www.autonomie.net/) was used. The database covers one year of real-world second-by-second measurements from three Class-7 diesel buses operated by the Knoxville Area Transit in Knoxville, TN. The key measured data included fuel consumption, vehicle speed and acceleration, engine speed and torque, vehicle weight, and GPS spatial location information. The GPS altitude data were used to estimate road grade, which can have a significant impact on vehicle powertrain performance and energy consumption. Then, using these data (in particular, the speed profile and the road grade) as well as the electric bus data, the energy consumption was calculated. However, this approach is not suitable for cases when the route is being developed for the first time or its speed profile is unknown. Besides, such measurements are very laborious. A simpler method based on diesel bus operating data is presented in [6]. To assess the energy consumption of the electric bus, data on the fuel consumption of the diesel bus, taking into account its “Tank-to-Wheels” (TTW) losses, are used. The energy of diesel fuel is converted to the energy consumption of an electric bus, taking into account its TTW. The effect of energy recuperation for the electric is also taken into account. Some modern modeling techniques focus on creating a speed profile for a given route. As it is pointed in the paper [7], many state-of-the-art approaches do not consider local bus route characteristics. Others require high-resolution measurements of the vehicles’ driving profiles. The paper presents a method of building a speed profile based on trapezes and triangles which are combined to reach the

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average speed of the bus on the segment “from stop-to-stop”. In order to better reproduce the real driving conditions to which a bus is exposed, a simplified speed profile is derived dynamically for each trip between each pair of bus stops so that it matches the available real-world data (measured average speed). A key feature is the entry of intermediate stops to obtain a given average speed. But entering intermediate stops seems to be too artificial way. Intermediate stops and delays should be carried out during modeling following real transport interferences of the route (traffic lights, pedestrian crossings, traffic jams, etc.). An approach that typifies and takes into account these interferences when constructing the speed profile is presented in [8]. Many studies of vehicle driving cycles focus on creating one typical cycle by summarizing a large data set. An overview of the features of these works is presented in [9], where a new method for synthesizing driving cycles is proposed: generating varying cycles and the number of passengers for a selected bus route, based on several measured cycles. At the same time, the possibility of stops due to the passage of pedestrian crossings and traffic lights is not considered. In addition, as in other methods, the degree of “gravity” of the resulting cycles is not evaluated in terms of energy consumption and other indicators. As a result of the analysis of many works in [10], it is noted that most modern methods have one or several of the following limitations: (1) require high-resolution speed profiles which are difficult to obtain due to lack of operational data and high associated costs; (2) do not consider the complexity and variations of extensive real-world operational conditions; and (3) depend on typical driving cycles and do not consider randomness in speed and uncertainty in energy demand. In order to overcome the aforementioned issues, this chapter proposes a practical alternative to calculate Electric Bus Energy Consumption (EBEC) without the need for high-resolution speed profile data, while still considering the operational details of transportation networks. To that end, a probabilistic model is applied to generate synthetic speed profiles using basic information of the EB trip (i.e., trip time, distance, and stop locations). The proposed probabilistic model of EBEC considers route characteristics, traffic conditions, weather conditions, and the operation of HVAC systems. The authors believe that using this model, transit bus planners can accurately assess the energy consumption characteristics of EBs at different operating conditions. The following conclusion follows from a generalization of the above typical approaches. All approaches are aimed at obtaining one or more characteristic values of the energy consumption of the electric bus on the route. The task of accounting and representing the space of all energy consumption values is not formulated, and the method of choosing the calculated (design) case is not considered.

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Action of Typical Factors

As it is pointed in [11], uncertainty in operation factors, such as weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an IoT system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/ km between trips. Furthermore, the consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by ambient temperature, driving behavior, and route characteristics. Besides, it is noted that the high correlation between aggressiveness and bus energy consumption implies that particular attention must be paid to limiting high-speed accelerations of city buses [11]. The data below is used to evaluate in more detail the effects on vehicle energy consumption of factors such as passenger (Pas) loading and seasonal factors (temperature). The energy consumptions of electric buses on the same route with different passenger loadings, taking into account the season factor, are given in Table 1. These relations are based on the data from Table 2. For further research, most attention should be paid to the relationship between the average and minimum values, since they have a greater effect on the curve describing the distribution of energy consumption. The maximum values do not have much effect, since they are on the right side of this curve, where the probabilities are not large and not influential.

Table 1 Energy consumption versus passengers (Pas) loading (based on [12]) Season

No. Pas/average Pas

Max Pas/average Pas

Max Pas/no Pas

No. Pas/average Pas

Summer Winter

0.82 0.72

1.17 1.17

1.43 1.62

0.82 0.72

Table 2 Energy consumption (EC) versus Pas loading and season factor (based on [12]) Pas loading

Winter (kWh/km)

Summer (kWh/km)

Winter/summer EC

No Pas Average Pas Max Pas

1.19 1.64 1.93

1.07 1.31 1.53

1.11 1.26 1.26

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Table 3 Energy consumption (EC) versus season temperature (ST) Place

Extreme ST (°C)

EC (kWh/km)

Moderate ST (°C)

EC (kWh/km)

Extreme/ moderateST

Berlin [13] Ontario [14]

−17 +35

3.60 1.45

+18 +10

2.30 1.27

1.57 1.14

The action of the temperature factor is presented in Table 3. The difference in the relations max/min 1.57 (Berlin) and 1.14 (Ontario) can be explained by the fact that in Berlin, in addition to the HVAC system (heating, ventilation and air conditioning), there is also an increased rolling resistance in winter compared to in summer. In a popular and relevant work [15], two different driving styles (defensive and aggressive) were considered with the aim to compare fuel consumption and exhaust emission. 12 passenger cars of different categories were used for this work, of which 10 were petrol-powered and 2 were diesel-powered. The typical increase in fuel consumption due to aggressive driving compared to defensive driving occurs from 78.5 to 137.3% for petrol vehicles and from 116.3 to 128.3% for diesel vehicles. One of the features of modern electric buses is the ability to recuperate energy. As reported by Gao et al. [5], in modern electric buses, recuperation reaches 30%. This feature of electric vehicles led to the formation of eco-driving direction: coasting, instead of braking, with the return of energy spent on accelerating the vehicle [16]. However, for real assessments of all possible energy consumption space, it is necessary to consider a variety of driver behaviors. The effects of different driver behaviors as well as passenger loading and rolling resistance are theoretically studied in [8]. For analysis, the following driving styles are used: Light (calm) L (L1200), three aggressive styles A1, A2, A3, and super aggressive A!, as well as four typical segments with distances SD: 300, 600, 900, and 1200 m (see Fig. 1 for SD= 1200 m).

Fig. 1 Speed profiles of typical driving styles for segment 1200 m (based on [8])

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The aggressive A1 style includes a high acceleration movement to achieve maximum speed and a deceleration stage (coasting) at the planned time. Other aggressive style options are characterized by a different ratio of acceleration and deceleration. Super aggressive A! style includes high accelerations and decelerations as well as movement with maximum speed to overcome the segment in minimal time. Bus parameters used for calculation relates to 12-m E420 electric bus of BKM Holding (see below, next section); TTW = 0.88, and the recovery coefficient for costing is 0.6. The results of the calculation of energy consumption are given in Table 4 for the gross bus weight m = 15 t. These results follow that the aggressive style (A1) is the best for segments with long-distance SD and movement time tb. If the time of acceleration t1 is less than 0.04tb, then the aggressive style is preferable. This is explained by the fact that the overcoming of resistance to movement occurs due to the inertia (coasting) in the largest part of the segment. For long distances, the driving style A1 is close to energy-efficient bus driving consisting of a short intensive acceleration and subsequent coasting [17]. Thus, for long segments the driving style A1 is close to optimal. Table 5 shows the dependence of energy consumption on driving style, full bus weight, and the length of the segment “from-stop-to-stop”. These results show that an increase in passenger loading from 0 (gross bus weight = 12 t) to max (gross bus weight = 18 t) increases EC 1.4—1.5 times. Based on the average energy consumption, the deviation is ±20%. Table 4 Energy consumption (EC) versus driving style and distance Distance SD (m)

300

600

900

1200

Movement time tb (s) ECLight (kWh/km) ECA1 (kWh/km) ECA1/ECLight t1/tb ECA2 (kWh/km) ECA3 (kWh/km) ECA! (kWh/km)

37 1.06 1.51 1.42 0.15 1.60 1.69 1.80

74 0.63 0.85 1.34 0.07 0.96 1.07 1.19

111 0.55 0.62 1.14 0.05 0.74 0.86 0.99

148 0.51 0.51 1.00 0.04 0.64 0.76 0.89

Table 5 Energy consumption (kWh/km) versus driving style, full bus weight and distance (SD = 300, 600, 900, 1200 m) Driving style

Full bus weight (t)

300

600

900

1200

Light Light Light A1 A1 A1

12 15 18 12 15 18

0.854 1.059 1.264 1.214 1.508 1.803

0.517 0.630 0.743 0.683 0.845 1.008

0.451 0.549 0.647 0.506 0.624 0.743

0.423 0.514 0.605 0.417 0.514 0.610

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Table 6 Energy consumption (kWh/km) versus rolling resistance coefficient, driving style and distance Distance (m)

300

300

600

600

900

900

1200

1200

Driving style f = 0.008 f = 0.012

L 1.06 1.22

A1 1.51 1.60

L 0.63 0.81

A1 0.85 0.93

L 0.55 0.73

A1 0.62 0.71

L 0.51 0.70

A1 0.51 0.59

Assuming that the segment SD= 1200 m has 3 intermediate stops (after 300, 600, and 900 m), then three intermediate stops increase EC for this segment 2 times (in light style) and 3 times (in aggressive A1 style). Energy consumption data (kWh/km) depending on the rolling resistance coefficient f, driving style and distance SD are shown in Table 6. The bold font indicates the case when the aggressive style A1 gives less or equal energy consumption compared to the light style L. From these data, it follows that the rolling resistance coefficient is an influential factor. Its calculated value should be set for the conditions “summer” and “winter” separately. In addition, aggressive style A1 is preferred for conditions with high rolling resistance for long distances. The presented and other known data do not give a general picture of the action of the factors considered. More research is required.

3 Experimental Study of the Electric Buses Produced by BKM Holding The test objective was to determine the energy consumption depending on the electric bus loading, driving style, and route factors. The object of testing and its main technical parameters are in Fig. 2, and characteristics of the route are in Fig. 3. The test period is April 23–26, 2019. One of the most difficult segments for bus movement is depicted in Fig. 4, which also shows graphs of changes in speed, energy consumption, and recuperation. The test program included trials with three options for the total weight of the electric bus: 12 tons (no passengers), 15 tons (average load), and 18 tons (full load). For each loading option, rides were carried out with the three driving style options discussed above: calm (light) L, aggressive (A1) and super aggressive (A!). The driver sought to use these driving styles, but they were not implemented in their pure form due to the inevitable influence of the real traffic situation. During the tests, the processes of energy consumption and recuperation were recorded by two separate sensors: S1 (expended energy) and S2 (recovered energy). This is important for analyzing graphical data and estimating the total (resulting) energy consumption. Typical control, speed and energetic processes are presented in Fig. 5.

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Fig. 2 Electric bus E420 of BKM Holding and its main parameters

Fig. 3 Characteristics of the route for testing

Table 7 contains the main results of testing: energy consumption depending on the driving style and gross bus weight. From the data obtained conclusions 1–5 follow, which are presented below.

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Fig. 4 One of the segments “from-stop-to-stop” and graphs of bus working processes

1. The indicator “Driver factor” is introduced as the ratio of total energy consumption per km for the driving style in question to this value for a calm driving style. A comparison of driver factors for a 12-ton bus (1–1.09–1.38) and an 18-ton bus (1–1.03–1.13) allows to conclude that a heavier bus is less sensitive to changes in driving style. 2. Using as an example the aggressive driving style and total energy consumption (0.96–0.99–1.14 kWh/km) for the bus of different cargo weight (12–15–18 t), it can be concluded that driving style has a greater influence on energy consumption than passenger loading. This is also due to the fact that the passenger load is 1.5 or more times less than the curb weight of an electric bus.

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Fig. 5 Typical control, speed and energetic processes: expended energy (top) and recovered energy (bottom)

3. The ratio of recovered energy to expended energy (energy consumption without recuperation), depending on driving style and electric bus loading, ranges from 31 to 41%. Note: the above conclusions may be violated during rush hours. 4. Transport cost indicator confirmed the fact that the higher the passenger load, the lower the costs in the form of energy consumption per ton-km. 5. To calculate the energy consumption, it is necessary to know the rolling resistance coefficient. The experimental studies of the electric bus run-out gave the following values of the coefficients: 0.011 and 0.013, an average of 0.012.

9.58

9.58

9.18

9.58

9.44

9.18

9.58

9.61

12

12

15

15

15

18

18

18

a

TPI =Transport costs Rush hour

9.58

12

Driving style

Calm (Light) Aggressive (A1) Super aggressive Calm (Light) Aggressive (A1) Super aggressive Calm (Light) Aggressive (A1) Super aggressive indicator

Route length L (km)

Gross bus weight m (t)

20,217

18,431 8,168

7,512

5,150

7,455a

18,378a 15,306

6,757

4,289a

13,853a 16,219

6,686

5,225

3,860

Recovered energy S2 (kWh)

18,270

14,377

12,282

Expended energy S1 (kWh)

Table 7 Energy consumption versus driving style and gross bus weight

1.25

1.14

1.11

1.16

0.99

1.04

1.21

0.96

0.88

Energy consumption per km (S1–S2)/ L (kWh/km)

70

63

61

77

66

69

101

79

73

TPI= Transport costs indicator = (S1–S2)/ (mL) (kWh/(t km))

1.13

1.03

1

1.12

0.95

1

1.38

1.09

1

Driver factor (for the same m)

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Fig. 6 Characteristics of electric buses E433 of BKM Holding (above) and average power consumption of 15 electric buses on the same route, as well as the average temperature in Minsk (below) depending on the month

Additional data on the energy consumption of BKM Holding electric buses, related to seasonal factors, are shown in Fig. 6. For their formation, data were used for 15 electric buses that run along one of the routes in Minsk. The maximum average value is 2.03 kWh/km (February), and the minimum average value is 1.37 kWh/km (June). Their ratio 2.03/1.37 = 1.49. These data can be taken as typical for assessing the impact of the seasonal factor on buses of a similar type. It should be noted that in this electric bus type: (a) the battery is used for: – heating the driver’s cab – managing ventilation,

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(b) the battery is not used: – for heating the passenger compartment of the bus (Webasto device is used for this) – for opening and closing doors (a pneumatic system is used for this). Increasing energy consumption during winter months can be explained by using a bus battery for heating and ventilating operation, as well as additional factors: • • • •

Increasing rolling resistance because of winter tires Road coverage (snow is often) More complicated traffic condition Harder battery work under minus temperatures.

4 Methodology for Assessing Energy Consumption 4.1

General

Figure 7 shows the energy consumption structure, which is based on published data and our own theoretical and experimental studies. A significant scatter in energy consumption data leads to the need for a probabilistic approach and consider the energy consumption indicator as a random variable.

Fig. 7 Structure of energy consumption (12-m electric bus) on the route

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Fig. 8 The probabilistic approach to energy consumption evaluation

Fig. 9 Typical distributions LN03 and LN05 for estimating relative energy consumption

The initial idea of the approach is described in [6] and depicted in Fig. 8. For electric buses, the distribution of energy consumption in the relative (dimensionless) form is proposed. A parameter P is relative energy consumption P = E/E0, where E0 is a certain base value, for example, modal value. (Modal value is the value that occurs most often or the most probable energy consumption value). The distribution is shown in Fig. 8 has the form of a truncated normal distribution f(P) with modal value P0 = 1 and minimum Pmin = 0.8 (empty bus). In fact, the values of the curve in the region of its left edge should be small. This condition corresponds to a lognormal distribution having a shift along the abscissa. In addition, the analysis of operation data and simulation of the electric bus operation, as well as common sense lead to the conclusion that for a more accurate (differentiated) presentation of the data it is advisable to use not one, but two characteristics distributions. These distributions are presented in Fig. 9. The modal value of every mentioned lognormal distribution (LN03 and LN05) is P0 = 1.0. To ensure this condition, each initial normal distribution ln(x) with a

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Table 8 Parameters of initial normal distributions for ln(x) and transfer to the lognormal distributions LN03 and LN05 Distribution

l = average for ln(x)

r = standard deviation for ln(x)

x0 = exp (l − r2) = modal value for f(x)

P0 = modal value for f (P)

D = P0 − x0

LN03 LN05

−1.00 −1.00

0.300 0.500

0.336 0.287

1.00 1.00

0.664 0.713

modal value x0 is shifted along the argument axis by the values D = P0 − x0 (see Table 8). The resulting lognormal distributions have the following features. The LN05 is a close analog of the truncated normal distribution depicted in Fig. 8 [6]. The LN05 is designed for cases with a wide variation of all factors (seasons and operation conditions): driving style, passenger load, HVAC and other auxiliaries, season, snow, route congestion. The LN03 is designed for cases when several acting factors are known and taken into account (season, snow, HVAC and other auxiliaries) and at the same time, other factors vary and introduce uncertainty (driving style, passenger load, road congestion). The parameters of distributions are in Table 9. Their variation coefficients (0.20 for LN05, and 0.11 for LN03) are typical for similar situations in different technical areas. They relate to cases with a wide and narrow variation of parameters depending on operation conditions. The methodology under consideration is implemented by the ECPro procedure in Excel. This procedure can be applied to various objects, situations and indicators. In the context of considering thematic, ECPro is used to select a design case for the energy consumption of an electric bus. The user (interested party) must perform the following actions: (1) to determine a typical indicator of energy consumption Ex, for example, the average or modal value for the route in question, based on operational, simulation and online data, (2) to select the type of distribution (LN03 or LN05) suitable for the situation in question (narrow or wide range of factors that corresponds to the data used), (3) to accept the probability FC for choosing the calculated value of energy consumption EC,

Table 9 Parameters of the lognormal distributions LN03 and LN05 Distribution

Modal value

Average

Standard deviation

Variation coefficient

Min/ average

LN03 LN05

1.00 1.00

1.05 1.13

0.12 0.22

0.11 0.20

0.76 0.71

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(4) to run the ECPro procedure and determine the calculated value of energy consumption EC that relates to the energy consumption Ex, the selected distribution type and the probability FC.

4.2

Application Example

After starting ECPro in Excel, the user fills in the following input data: – – – –

the value of the typical indicator of energy consumption: Ex= 1.64 kWh/km distribution type: LN05 Ex status (ID) in distribution: average value IAv2 = 1 the desired probability FC that is associated with the calculated value of the parameter in question: FC = 0.9.

For the solution, auxiliary data on the digitization of distributions LN03 and LN05 are used, which are presented in the Excel file in the form of Tables 10 and 11. In the open Excel file, the user generates a local table (see Table 12) based on the data from Tables 10 and 11. This table contains the values F1(P1) and F2(P2) that are closest to the selected probability (FC = 0.9) for the selected distribution (LN05). After generating this local table, subsequent calculations are automatically performed by Excel. They are as follows. As an intermediate result, the calculated value of energy consumption PC in relative (dimensionless) form is determined by the formula PC ¼ P1 þ ðP2  P1 ÞðFC  F1 Þ=ðF2  F1 Þ

ð1Þ

For this example, PC = 1.41. The Ex/Px ratio is then determined to convert the relative value to absolute, taking into account the Ex status specified in the input data. In this case, Ex = 1.64 kWh/km and Px = 1.13 (the average value for LN05 from Table 9). Final result (calculated energy consumption) EC = PC Ex/Px = 2.04 kWh/km.

4.3

Using the Approach

Determinative routes cycle. When using the proposed approach, it is necessary to take into account the route, understood as a routes cycle, and the charging configuration of the electric bus. The routes cycle of the electric bus and its charging configuration are closely related and condition each other. In particular cases, the routes cycle may correspond to – the vehicle cycle = bus operation through the course of a day for transit service (charging configuration Slow Depot)

0.434

1.01

FLN03(P)

P

1.04

0.525

Table 10 Probabilities F(P) for LN03

1.06

0.610 1.09

0.685 1.11

0.749 1.14

0.803 1.16

0.847 1.19

0.882

1.21

0.910

1.24

0.932

1.26

0.949

1.14

0.984

100 V. Algin et al.

0.460

1.29

FLN05(P)

P

1.31

0.515

1.41

0.566

Table 11 Probabilities F(P) for LN05 1.51

0.614 1.61

0.657 1.71

0.695 1.21

0.730 1.24

0.762 1.26

0.789

1.29

0.814

1.31

0.836

1.41

0.901

1.51

0.940

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Table 12 Local table for calculating PC using interpolation

F1 = 0.836

F2 = 0.901

P1 = 1.31

P2 = 1.41

– several trips followed by recharging (Slow Depot + Fast Terminal) – separate route (Fast Terminal at route termini) – part of the urban route (Fast Terminal + Fast Bus Stops), etc. In [18], based on data [19], 116 cases of the implementation of charging electric buses in Europe were analyzed. As a result, a set of simple basic charging configurations was selected (Table 13). Based on these simple configurations, 20 widespread combined configurations that are used in the actual operation of electric buses are given. The most spread of them are: Slow Depot = 49 cases (42.24%); (Slow Depot + Fast Terminal) = 21 cases (18.10%), and (Slow Depot + Slow Terminal) = 10 cases (8.62%). Each configuration has peculiarities that must be considered when choosing a “determinative” route cycle that forms the estimated energy consumption. For example, the most popular Slow Depot configuration generally relates to the routes cycle shown in Fig. 10. Table 13 Simple basic configuration ID

Charging configuration (features)

Charging time

1 2 3 4 5 6 7

Flash Fast Bus Stops Fast Terminal (pantograph, induction, connecting poles, plug) Slow Terminal Fast Depot/Selected Bus Stops Slow Depot In-Motion

5–20 s 1–5 min 5–15 min 0.5–2 h 0.5–3 h 2–8 h During motion

Fig. 10 Routes cycle corresponding to the transport cycle and Slow Depot configuration

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The following symbols are used for trips in this route cycle: 1 = pull-out from depot D, 2 = routes between termini A and B, 3 = routes between termini B and A, 4 = dead-head from bus-line AB to bus-line CE, 5 = routes between termini C and E, 6 = routes between termini E and C, 7 = pull-into depot D. In this cycle of routes, the electric bus first runs along one route and then moves along another route. At the same time, it is necessary to take into account his movement from the depot to the first route and his return to the depot for overnight charging. For the similar routes cycles, input data and simulation of each trip can be very cumbersome. Therefore, the following simplification of the task is recommended. 1. Divide all trips into typical trips by time periods. For instance: (1) Morning (N1 of trips); (2) Middle of a day (N2 of trips); (3) Evening (N3 of trips); (4) Late evening and night (N4 of trips). 2. Form input data and calculate energy consumptions in typical trips for mentioned time periods. 3. Calculate energy consumption for the routes cycle using the number of trips (Ni) for selected typical trips. After comparing results for different variants, the user selects the “determinative” case for the task in question. In various cases, attention can be paid to assessing energy consumption for a route, part of an urban route, daily time (vehicle cycle), monthly and annual consumption. The determinative routes cycle is used for evaluating energy consumption and choosing calculated value based on the above ECPro procedure. Adjustment of calculated energy consumption in some cases. In some cases, the user can independently evaluate the variation coefficient VCi of energy consumption distribution for the routes cycle in question. If this value is not close to VC1 for LN03 distribution or to VC2 for LN05 distribution (see Table 9), the user can design his own distribution in the manner presented in Table 8 and determine its parameters, as shown in Table 9. But another is also possible a method based on setting parameters associated with the distributions LN03 and LM05. The following operations are proposed to adjust the calculated energy consumption: 1. Taking into account the typical indicator of energy consumption Ex (for example, average value), it is determined the calculated energy consumption (EC1) for the LN03 distribution. 2. This operation is repeated for the LN05 distribution and it is determined EC2. 3. Then, the adjusted value of the calculated energy consumption ECi is determined by interpolation (or extrapolation) according to a formula similar to the above formula (1): ECi ¼ EC1 þ ðVCi  VC1 ÞðVC2  VC1 Þ=ðEC2  EC1 Þ

ð2Þ

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Table 14 Adjustment of calculated energy consumption for cases Int1, Int2 and Int3 Distribution

LN03

LN05

Int1

Int2

Int3

Variation coefficient VC 0.11 0.20 0.08 0.15 0.30 1.88 2.04 1.83 1.95 2.22 Calculated energy consumption EC (kWh/km) Note If EC is determined using the shifted lognormal distribution constructed by the method shown above in Tables 8 and 9, then for the case with the variation coefficient VC = 0.3 (Int3) it has r = 0.70 and average value PAv2 = 1,24. In this case, the calculated energy consumption is 2.21 kWh/km. The EC value of 2.22 kWh/km obtained by adjustment (Table 13) is very close to this value

Table 14 shows the results of the EC adjustment for the three values of the variation coefficient VCi. These cases are Int1, Int2 and Int3 (the typical indicator of energy consumption is Ex= 1.64 kWh/h as in the example above). Presented data confirm that in many cases it is possible to operate only with the two proposed distributions LN03 and LN05. They cover most of the practical cases. For example, an increase in the coefficient VC 1.5 times from 0.20 (the case of LN05) to 0.30 (the case of Int3) gives an increase in the calculated energy consumption EC by about 10%.

5 Conclusions Analysis of the data on the operation of electric buses shows that the energy consumption even on the same route has significant dispersion. The main factors leading to energy dissipation are: driving style, passenger loading, auxiliary systems operation, traffic interferences at different times of the day, seasonal factors (temperature, snow). As a result of experimental studies and analysis of seasonal operational data, the action of these factors was clarified and a typical structure of energy consumption was formed with an indication of dispersion of the structural components. Based on the previously expressed idea [6] and the obtained data, an approach was formulated and realized for describing the space of possible energy consumption values on the route. It is based on the use of two typical distributions with large (wide) and small (narrow) dispersion of data, which correspond to the characteristic cases of the operation of technical objects. The developed ECPro procedure provides the opportunity to select the calculated (design) energy consumption value of an electric bus on the route, taking into account the probability accepted by the user (interested party) that reflects the risk of not exceeding this value in operation. Acknowledgements This chapter was prepared as part of the PLATON project (Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet, runtime: 01.2018–06.2020), which received funding from the ERANET COFUND Electric Mobility Europe.

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References 1. DieselNet. Emission Test Cycles. https://www.dieselnet.com/standards/cycles/index.php 2. DieselNet. New York Bus. https://dieselnet.com/standards/cycles/nybus.php 3. Ultra-Low Emission Bus Scheme. https://www.lowcvp.org.uk/Hubs/leb/ultra-low-emissionbus.htm#UKBC 4. SORT—Standardised on-road test cycles. New ed. UITP (2014) 5. Gao Z, Lin Z, LaClair TJ, Liu C, Li J-M, Birky AK, Ward J (2017) Battery capacity and recharging needs for electric buses in city transit service. J Energy 122:588–600 6. Algin V (2019) Calculated modes for assessing operation properties and dependability of vehicles. In: Advances in mechanism and machine science, pp 3749–3758. Springer, Cham 7. Gallet M, Massier T, Hamacher T (2018) Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks. J Appl Energy 230: 344–356 8. Algin V, Goman A, Skorokhodov A (2019) Main operational factors determining the energy consumption of the urban electric bus: schematization and modelling. In: Topical issues of mechanical engineering: collection of scientific papers, pp 185–194. Minsk 9. Kivekäs K, Vepsalainen J, Tammi K (2018) Stochastic driving cycle synthesis for analyzing the energy consumption of a battery electric bus. IEEE Access 6:55586–55598 10. El-Taweel NA, Zidan A, Farag HEZ (2020) Novel electric bus energy consumption model based on probabilistic synthetic speed profile integrated with HVAC. IEEE Trans Intell Trans Syst 1–15 11. Kivekäs K, Lajunen A, Vepsäläinen J, Tammi K (2018) City bus powertrain comparison: driving cycle variation and passenger load sensitivity analysis. J Energies 11(1755):1–26 12. Hanlin J. Battery Electric Buses Smart Deployment. http://www.cte.tv/wp-content/uploads/ 2016/12/5_Hanlin.pdf 13. Göhlich D, Kunith A, Ly T (2014) Technology assessment of an electric urban bus system for Berlin. J Urban Transport XX:137–149 14. Fast Charge Battery Electric Transit Bus In-Use Fleet Evaluation (NREL/CP-5400-66098, May 2016). https://www.nrel.gov/docs/fy16osti/66098.pdf 15. Tzirakis E, Zannikos F, Stournas S (2007) Impact of driving style on fuel consumption and exhaust emissions: defensive and aggressive driving style. In: Proceedings of the 10th international conference on environmental science and technology, pp 1497–1504. Kos island, Greece 16. Gao Z, LaClair T, Ou S, Huff S, Wu G, Hao P, Boriboonsomsin K, Barth MJ (2019) Evaluation of electric vehicle component performance over eco-driving cycles. J Energy 172:823–839 17. Butarovich DO, Kositsyn BB, Kotiev GO (2017) The method of developing an energyefficient law for controlling an electric bus when driving on an urban route. J Trudy NAMI 2 (269):16–27 (in Russian) 18. Algin V, Czogalla O, Kovalyov M, Krawiec K, Chistov S (2018) Essential functionalities of ERA-NET Electric Mobility Europe Platon project. Mech Mach Mech Mater 4(45):24–35 19. ZeEUS eBus Report #2: an updated overview of electric buses in Europe (2018). http://zeeus. eu/uploads/publications/documents/zeeus-report2017-2018-final.pdf

Scheduling and Balancing of Electric Buses’ Charging Operations in Public Transportation Sebastian Naumann and Christian Hübner

Abstract Current battery electric buses in public transportation need to be recharged during their daily run. Delays in operation may cause unintended power peaks leading to a higher power providing fee and may additionally cause problems by more electric buses demanding for charging than available charging points. Therefore, the charging operations need to be scheduled before operation in order to make the charging operations schedule more robust against delays. Furthermore, balancing is needed during operation in order to handle critical delays appropriately. This chapter presents objective function based and rule-based algorithms for scheduling the charging operations before operation as well as balancing them during operation. These algorithms exploit the optimization potential that is offered by a fast charging infrastructure as well as an Intermodal Transportation Control System (ITCS) that collects and distributes the relevant real-time data.




Keywords Electric bus Public transportation Scheduling Balancing power grid





Charging operation


1 Introduction Battery electric buses (BEBs) are a mean for clean and silent public transportation. Several cities have introduced the first couple of electric buses on single bus routes. A problem of currently available batteries is that the capacity is not sufficient for powering such a bus with a mass of 10–20 t for the daily shift of 250–300 km. Although the improvements of lithium batteries continue, the total breakthrough is not expected within the next years. Therefore, intermediate recharging of the buses’ batteries is required in the medium term. S. Naumann (&)
C. Hübner Institut f. Automation und Kommunikation e.V., Magdeburg, Germany e-mail: [email protected] C. Hübner e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_7

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Charging electric buses or electric vehicles, in general, is strongly related to the required charging power and power grid restrictions. Whereas a simple-minded charging operations schedule might lead to power peaks, a sophisticated charging operations schedule protects the power grid and its components as well as reduces the costs for the public transport company caused by a high power providing fee. Vehicle schedules with integrated charging operations schedules may offer some scope for moving the single charging operations within certain borders. We aim to exploit these scopes for improving the charging operations schedule before the operation as well as during operation. Within this chapter, we distinguish between two parts. The first part describes the scheduling of electric buses’ charging operations before operation. Compared to this, the second part deals with balancing the charging operations during operation. All work bases on data of the public transport operator of Brunswick, Germany, which currently operates five battery electric buses and five inductive charging stations at a power of 200 kW each. For our methods of scheduling and balancing, we assume a pure battery electric bus fleet. For scheduling the charging operations before operation, we define an objective function and try to optimize it. For balancing the charging operations during operation, we use a rule-based algorithm.

1.1

Related Work

Scheduling and balancing charging operations within an electric bus system in public transportation is an unattended area of research so far. However, there are several papers dealing with managing charging operations of passenger cars [1–4] with similar aims as avoiding a grid components overload, minimizing power losses and improving the voltage profile. Compared to the field of public transportation with determined arrival and departure times as well as a pretty accurate knowledge on the future energy demand, managing charging operations of passenger cars is challenged by much more uncertainties. Approaches based on Markov processes or based on minimizing an objective function are commonly used in order to solve this stochastic optimization problem. Moreover, He et al. [5] considers charging operations scheduling within smart grids that not only includes charging but discharging too (vehicle-to-grid). They also try to minimize the total cost of the EVs formulated as a local scheduling optimization problem. Other contributions with relation to the much more restricted area of public transportation deal with designing routes for electric buses [6] and with scheduling electric buses on a given timetable and given charging stations [7]. However, both papers do not consider managing the charging operations. Wang et al. designed a real-time charging scheduling system called bCharge and tested it on a fleet of 16,359 buses in Shenzhen [8]. The bCharge system is based on Markov Decision Process with the overall objective of reducing the charging and

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operation costs. The optimization is based on electricity prices and its objective is to minimize the charging costs, not to minimize the peak demand.

2 Definitions and Prerequisites 2.1

Charging Infrastructure

In order to operate electric buses in public transportation, charging infrastructure is required. The charging infrastructure in general is a set of charging points. A transformer is necessary to transform the voltage of the medium voltage power grid to the low voltage required for the charging points. Figure 1 shows an example of such an infrastructure. Charging Point. A charging point is a device where an electric bus can stop in order to recharge his battery. We assume that only one bus can be recharged at a charging point at the same time. There are different possibilities of how the bus can connect to the charging point. Cable with plug, pantograph and inductive are some of them. However, we do not consider details on the type of charging here. The charging power ranges up to several hundred kW. At the examples within this chapter, we always assume a charging power of 200 kW. Power Grid Access Point. A power grid access point as the name says is the connection to the electric grid. It corresponds to the according transformer. Each charging point is connected to a power grid access point. Usually in Germany, a power grid access point is designed to 600 kW at maximum. Therefore, the sum of all charging points connected to a power grid access point should not exceed 600 kW in sum. In Brunswick, e.g. the charging points have a power of 200 kW, such that three charging points are connected to one power grid access point at maximum. There could be further charging points connected to it, however, in order Fig. 1 Charging infrastructure at a bus stop

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to avoid damages on the power grid and transformer, it has to be ensured that no more than three charging points are operated simultaneously.

2.2

Power Providing Fee

Users of the power grid going beyond standard household access, do have to pay in Germany not only for the energy received from the energy provider measured in kWh but additionally for getting provided the required power measured in kW. This power providing fee has to be paid to the power grid operator and not to the energy provider. In Germany, energy providers and power grid operators all usually are separated independent companies. Currently, we do not know how this is handled in other countries. However, in the research project PLATON corresponding work is ongoing. The real fee depends on the power grid operator. In Germany, the fee is calculated by the Quarter (15 min) with the highest requested power of the last year. It has to be paid for a year per kW. In cities like Brunswick, the power providing fee is related to a power grid access point. However, the power grid operator may relate the power providing fee to all power grid access points together of a public transportation company or any other customer. Within this paper, we concentrate on single power grid access points only.

2.3

Charging Operation

A charging operation has different attributes we use for scheduling later. Start time. This value denotes the beginning of the charging operation measured in seconds. Duration. The duration indicates the period of time from the beginning of the charging operation to the end of the charging operation measured in seconds. Time window. The time window defines the period of time in which charging is possible. Its start time twindow_start and end time twindow_end is limited by the trips that have to be solved before and after. The duration of charging is given by Δtcharging which is considered to be fixed here. To lengthen or shorten is possible in principle, however, this influences all follow up charging operations which becomes rather complex and therefore might be considered in future research. In Fig. 2 the time window and its parameters are sketched. The start time of the charging operation can be varied. The end time of charging always is:

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Fig. 2 Time window of a charging operation

tch arg ing

end

¼ tch arg ing

start

þ Dtch arg ing

ð1Þ

The latest start time of charging is: tcharging

start latest

¼ twindow

end

 Dtcharging

ð2Þ

The charging duration is set by the scheduling process. When a vehicle has the possibility to charge, it does until the battery state reaches a defined maximum. When the charging power is low, the defined maximum battery state of charge will never be reached in most cases, however, if the charging power is high (e.g. 200 kW), the desired maximum battery state of charge is reached before. Therefore, the charging duration is often shorter than the time window, such that, we have some scope to change the beginning of the charging operation.

2.4

Vehicle Schedule

The vehicle schedule is a set of blocks for a certain day. Each block exactly corresponds to one bus. A block describes the run of a bus out of the depot via several trips of the timetable (with passengers) finally back to the depot. Between the trips, there are deadhead trips without passengers, charging operations and waiting times. An example of a vehicle schedule provides Fig. 3.

Fig. 3 Example of a vehicle schedule

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Scheduling buses (VSP—Vehicle Scheduling Problem) is a problem for its own and scheduling electric buses (E-VSP) even more. Many researchers have been dealing with this problem in general and a lot of variants for more than 50 years. Although we do not want to consider this topic in detail here, we would like to refer to some literature. Firstly, [9] constructed a bipartite graph where each node represents a trip from the timetable. The minimum number of required vehicles results from the number of trips minus the maximum flow in the network. [10] presented a faster algorithm for calculating the maximum flow within a network. [11, 12] provides a method for scheduling electric vehicles by a column generation approach. Currently, we are working on the E-VSP by improving the column generation approach. The results will be published soon. The vehicle schedule used in the later example results from the ongoing work. Our further work deals with the determination of a minimum charging infrastructure [13, 14]. The developed method receives a vehicle schedule and puts out the required charging infrastructure by analyzing the waiting periods at different stops. Greedy Charging Operation Operations Scheduling. As a result of vehicle scheduling, we let start all charging operations as early as possible and call this the greedy way. This is a very trivial algorithm, which is not described in detail here. The consequence might be a higher demand for charging points and higher costs for providing power due to peaks. Because this is not satisfying, we try to exploit the scope of the time windows in order to distribute the charging operations in a more intelligent way.

3 Charging Operations Scheduling Although we have a valid vehicle and charging operations schedule, we would like to improve the charging operations schedule in order to make it more robust against vehicle delays resulting from traffic congestions or unexpected events.

3.1

Metrics

Let’s have a look at Fig. 4. It shows some charging operations at three charging points which all belong to the same transformer. N indicates the number of simultaneous charging operations at a certain time. Dtgap indicates the period of time between the end time of a charging operation and the start time of the subsequent charging operation. If such a subsequent charging operation starts before the previous charging operation has ended, this value is negative. From these basic values, we easily can derive the parameters total time

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Fig. 4 Metrics of a charging plan

gap Dtgap_total (sum of all time gaps), an average time gap Dtgap , standard deviation s and variance r2, if we would like.

3.2

Approach

The aim of the improvement is (a) to distribute simultaneous charging operations such that the number of them N is minimal at any time and (b) to stretch the charging operations such that there is always a maximum time gap between them. N can be easily calculated by stepping through the sorted charging operations schedule and Dtgap_total as well by summing up all single time gaps. The total time gap is only be influenced by changing the start time of the first and the last charging operation of the schedule. So, we move the first charging operation to the earliest time within its window, and we move the last charging operation to the most right time of its window. The question for the optimal distribution such that there is always a maximum time gap between neighbored, is more complicated. Unfortunately, simply distribute all time gaps equally among the charging operations is not possible due to the restricting time windows of each charging operation. Instead, we look at the square of Dtgap. Assuming three charging operations with two time gaps Dt1 and Dt2 and assuming that—when we move the middle charging operation, the sum of Dt1 and Dt2 keeps unchanged. So, if we increase Dt1, then Dt2 is decreased by the same value and vice versa. For easier writing, we set x = Dt1 and y = Dt2 for the following equations. At first, we assume x = y: x2 þ y2 ¼ x2 þ x2 ¼ 2x2

ð3Þ

Then, we assume x = x − 1 and y = x + 1, which represents a movement of the start time of the middle charging operation. That total time gap keeps unchanged by this movement as stated above:

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ðx  1Þ2 þ ðx þ 1Þ2 ¼ ðx  1Þðx  1Þ þ ðx þ 1Þðx þ 1Þ

ð4Þ

¼ x2  x  x þ 1 þ x2 þ x þ x þ 1

ð5Þ

¼ x2  2x þ 1 þ x2 þ 2x þ 1

ð6Þ

¼ 2x2 þ 2

ð7Þ

2x2 \2x2 þ 2

ð8Þ

From Eq. (3) and (7) follows:

Such that we conclude, that the sum of all Dtgap should be minimized: X

2 Dtgap ! min

ð9Þ

Comparing the Solutions. In order to determine whether a new solution s2 is better than the best solution found so far s1, we use the following algorithm: Solution compare (Solution s1, Solution s2) if (s1.N < s2.N) return s1; if (s1.totalTimeGap < s2.totalTimeGap) return s1; if (s1.sumOfSquareTimeGaps < s2.sumOfSquareTimeGaps return s1; return s2;

The algorithm firstly compares the numbers of simultaneous charging operations, secondly the total time gaps and finally the sums of the square time gaps. Optimization. Optimizing the entire charging schedule is not so easy. The number of possible combinations increases with every charging operation by up to three tens potencies if we focus on accuracy of seconds, such that the computation time for trying all possible combinations becomes exponentially very fast, and is not possible to check all possible combinations with current computers if there are more than a dozen of charging operations. We have been looking for efficient algorithms solving similar problems, however, we could not found some so far. It reminds a little bit to process scheduling on operation systems, however, there are major differences between them. Therefore, we decided to build chronological groups of some charging operations each, such we can find the optimum for each group separately by trying all possible combinations. This probably does not result in the overall optimal solution but delivers a good approximation.

Scheduling and Balancing of Electric Buses … Table 1 Comparison of charging operations scheduling methods

Metric

Greedy

Improved

N

40 times 2 = 400 kW 61.256 s 34.668.884 s

19 times 2 = 400 kW 61.438 s 26.472.121 s

Dtgap_total Sum of Dt2gap

3.3

115

Results

We have an investigated example in Brunswick. On a day with 872 trips, we calculated a vehicle schedule with 41 electric buses. The charging operations schedule at the transformer with the highest number of charging operations has 2 charging points and includes 237 charging operations during the whole day. The charging operations schedule achieved by the greedy method revealed 40 times two simultaneous charging operations with 400 kW in total. There are more than 1087 possible combinations for the charging operations schedule. That is why we had to apply our grouping method as described in the previous section. Even if we could not eliminate all situations with two simultaneous charging operations we could significantly reduce the number of occurrences. Table 1 compares the results of both methods. Further work is necessary to achieve the objective of reducing the number of occurrence from—in this case—two simultaneous charging operations down to one. Changing the duration of single charging operations might be a key here, however, increases the complexity of the problem.

4 Charging Operations Balancing 4.1

Motivation

When operating a bus fleet that contains a significant number of electric buses for public transportation, there is a need for supervision and control of the actual charging operations performed by the individual electric buses, i.e. a real-time load balancing management must be established. The load balancing management is required even if dedicated scheduling of the charging operations has been performed as part of the offline charging scheduling. This is because during the course of the day the scheduled charging operations can not always be carried out as planned due to delays and possible routing changes. Without any operational management of the actual charging operations, the peak power demand may exceed the targeted threshold because of overlapping charging operations. Limiting the peak power demand is important for minimizing the total cost of the charging operations.

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Another motivation for the management of the charging operations is to avoid lowering the state of charge of the batteries below a certain threshold in order to maximize battery life and therefore optimize overall costs. In extreme cases, it may even be possible that an electric bus will not be able to complete its scheduled trip and thereby disrupting operations. The third and final reason for the management of the charging operations results from the fact that the existing power grid infrastructure is not designed for the high power demand that electric bus charging (typically around 200 kW) requires. In order to use the existing power grid infrastructure efficiently, it would be necessary to consider the available power supply at any given time at particular charging locations. For example, a charging point with a nominal power demand of 200 kW can be supplied by an existing power substation (medium voltage to low voltage with a nominal power of about 600 kW), if the previously installed loads usually need less than 400 kW of total power. In rare cases, the actual power demand of these loads may exceed the 400 kW level, therefore limiting the available power for the bus charging. If the dynamically changing power supply at certain charging points can be communicated to a central load balancing system, this information can be used to optimize the bus charging operations. In this case, existing power grid infrastructure can be used instead of having to build a new power substation at every charging point.

4.2

Concept and System Architecture

The system for load balancing of charging operations can be considered as an extension of an existing Intermodal Transport Control System (ITCS) that tracks the position of the buses in real time with low latency. In addition to the current position, the ITCS also knows the schedule and routes of all involved vehicles and is able to estimate the arrival time at specific stop points along the route in order to show the expected arrival time to passengers at the stop points. One has to keep in mind that the load balancing system should interfere as little as possible with the normal operations of the ITCS and the scheduling of the electric buses to avoid introducing delays. The operation of the load balancing system requires that in addition to the current position of the electric buses the current state of charge needs to be communicated from the buses to the ITCS and then provided to the load balancing system. Additional information like the current level of passengers or the mileage may be collected too, in order to better estimate the current energy demand. To facilitate the integration of the load balancing system with the ITCS, the communication between these two systems should be based on existing standards

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that are established in the ITCS domain. In the German research project MENDEL, the standards VDV 452 and VDV 453 are used for this purpose, which are widely used in Germany for the exchange of plan data as well as real time actual data. In addition to the communication between load balancing system and ITCS for traffic data, there needs to be a link to a system that provides the information about the available load power at individual charging points at specific points in time. Such a system is only needed if charging points need to be considered that can not guarantee the maximum available power supply all the time. This system would have to be managed by the distribution grid operator that is responsible for the power grid infrastructure. For this purpose, the power substations feeding the bus charging points have to be equipped with automation technology including power measurements in order to perform a state estimation of the respective power grid [15]. The increasing need for automation of the medium and low voltage levels of the distribution grid results not only form increased penetration of e-mobility but also decentralized energy resources, e.g. photovoltaic systems, which lead to dynamically changing load profiles [16]. The overall structure of the system together with the relevant information flows is shown in Fig. 5. Note that the load balancing system is tightly coupled to the ITCS and is likely to be integrated directly into future ITCS.

Fig. 5 System structure for load balancing of charging operations

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4.3

Objectives of Load Balancing

There are three main objectives of the load balancing system that need to be honored simultaneously: 1. Consider possible limitations to the available charging power at certain charging points as indicated by the “available charging power” system of the distribution grid operator. – In case that a charging point can not provide the full nominal power due to temporary grid congestions, the charging power needs to be reduced or no charging can take place at all. – This type of event should happen only very rarely but the resulting limitations must be strictly respected in order to avoid overloading power grid infrastructure. The second objective will address the loss of energy as compared to the scheduled charging. 2. Detect and mitigate the insufficient battery state of charge for completing the block of the bus. – Actions only need to be taken in the case that the energy required for completing the current block of any bus exceeds the sum of the energy in the battery and the scheduled amount of energy to be consumed in the remaining charging operations. – If actions are necessary, some of the remaining charging stops need to be extended in order to increase the state of charge to a level that allows for completion of the current block. 3. Ensure that the sum of all concurrent charging operations of the entire bus fleet does not exceed a certain predetermined threshold. – The value of the threshold may be derived from the scheduling of the charging operations, i.e. during the operation day the scheduled maximum power demand may not be exceeded. – The maximum power is calculated on a per 15 min average, i.e. for every quarter of an hour, the total amount of energy consumed is multiplied by four to get the power in kW for this 15-min interval. – In the future, instead of fixed value, this threshold may be dynamically agreed between the public transport operator and the distribution grid operator based on the actual congestion/capacity of the power grid. – An example of an unscheduled peak power demand is shown in Fig. 6. Here the unexpected delay at the end of trip 2020 caused three concurrent charging operations that each require 200 kW of active power resulting in total power demand of 600 kW. Without the delay, only two concurrent charging operations would have happened on that day.

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Fig. 6 Example of an increased peak load by an unexpected delay after trip 2020

4.4

Load Balancing Algorithm

For any point in time, the load balancing system knows the set of all currently active blocks of electric buses by reading the corresponding plan data. In this context, we can equate one block with one electric bus and one associated battery with a specific energy capacity and state of charge. This battery moves through the block during the operating day and must never be drained below a preconfigured level of state of charge in order to sustain a reasonable lifetime. In order to calculate whether the current state of charge of a battery will be sufficient to carry the electric bus through the rest of the block given the remaining charging operations scheduled, there needs to be an estimation of how much energy will be needed. This estimation needs the following input information: • • • • •

Remaining distance until the end of the block Remaining duration until the end of the block Passenger occupancy level Ambient temperature Vehicle characteristics (weight, energy demand for heating, etc.)

The estimation of the energy demand does not need to be explicitly calculated when using the plan data from the scheduling of the charging operations. That is because the scheduling already performs such energy demand calculations and provides the most relevant results through the plan data in form of the expected state of charge at any given charging point. This information can be used to calculate the energy demand between successive charging points of a block with the option for calibration given the ambient temperature and passenger occupancy level. To facilitate the efficient operation of the load-balancing algorithm, the plan data of all relevant blocks that are received in form of VDV 452 relational data, needs to be imported and transformed into a form that is more suitable for the subsequent calculations. During this transformation, the consistency of the plan data is verified.

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The relevant information of a block can be seen as an ordered sequence of stops. For each stop, the following information is stored: • • • • • • • • •

Trip identifier—to identify the trip corresponding to the stop Stop point identifier Planned arrival time Planned departure time Travel duration until next stop Travel distance until next stop Planned charging duration (if there is a charging point at this stop) Planned charging energy Planned state of charge of the battery (only if this is a charging stop)

The load-balancing algorithm works by performing a continuous comparison between the plan data just described and the actual state of the electric busses, most importantly the estimated arrival time at the next stop and the current state of charge of the battery. In the research project MENDEL, the real-time data about the current state of the electric buses is received from the ITCS via the protocol VDV 453 using the DFI (dynamic passenger information) as well as the AND (generic messaging) services. The VDV 453 protocol is based on HTTP transport using XML payloads. The load-balancing algorithm can be described by the following activities that are continuously executed on the total set of blocks: • Check whether any of the charging points that are scheduled to be used for charging within the next time frame (about 10 min) will not be available for charging (or only with reduced maximum power) as communicated by the distribution grid provider based on the current power grid load – If a charging point will not be available for a scheduled charging stop, the system will inform the bus driver via ITCS to omit the charging stop. – Otherwise, the charging points will be assumed available for charging. • Check whether there is any bus that needs additional or extended charging in order to complete its block without draining the battery below the configured minimum state of charge – In order to perform this check, the current state of charge needs to be available. Given the energy demand for the remaining part of the current block and the time and location of the remaining scheduled charging stops (subject to restrictions from the first check) the minimal state of charge is calculated. – If the estimated minimum state of charge of any bus is lower than the preconfigured level, then additional charging needs to happen, i.e. charging stops on the remaining part of the current block need to take longer than scheduled which may lead to delays.

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– If there is more than one remaining charging stop, the required additional energy (to prevent a drop of the state of charge below the minimum level) will be distributed over all remaining charging stops to minimize the resulting delays in comparison to the scheduled stops. • Check whether within the next time frame the total sum of all concurrent charging operations is lower or equal to a predefined limit – If the sum is below the limit, no further adjustments to the scheduled charging operations are necessary. – Otherwise, one or several charging operations need to be omitted or shortened. The selection of those charging operations considers the importance of a charging operation for the corresponding bus, i.e. what effect it would have for the lowest state of charge on the remaining part of the block. In Fig. 6 we see an example of the third case: Because of a delay during trip 2020, three concurrent charging operations would happen (instead of two). With an active load-balancing system as described in this chapter, this situation would be avoided by directing either the delayed bus (row 4) or the bus of row 1 to omit its charging operation. The decision depends on which bus could most easily afford to have its charging operation dropped. For this, we estimate the lowest state of charge (LSOC) that would result (anywhere on the remaining part of the block) from omitting the charging operation for both buses under consideration. We then drop the charging operation for the bus with the highest LSOC. In this example, it may well be that the charging operation of bus 4 is dropped because the length of its remaining block is shorter compared to bus 1.

4.5

Evaluation

In order to evaluate the performance and correctness of the load-balancing algorithm for charging operations, scenarios need to be defined and simulated. For more complex scenarios, it is necessary to define and evaluate metrics, which describe specific characteristics of the resulting outcome. These results can then be compared with the same scenario but without having the load-balancing algorithm in place. In the research project MENDEL, the evaluation is done by using the traffic simulator SUMO (Simulation of Urban Mobility: https://sumo.dlr.de) that simulates the traffic of the city of Brunswick including electric buses. With the help of the Traffic Control Interface (TraCI) of SUMO, the data exchange to the ITCS is organized. The ITCS in turn connects to the load balancing system using the VDV 453 interface. The simulation runs need to be performed in real-time, i.e. without speed-up, because the ITCS software used is not currently able to support an accelerated run time mode useful for simulations.

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An obvious approach for defining test scenarios is to look at the objectives of the algorithm and create scenarios that target a specific objective each. Because the load balancing system has three major objectives that have been described in the previous subchapter, three corresponding scenarios can be defined: • Scenario 1: Respect limited power supply due to congestions in the power grid – A specific charging point used in one block of an electric bus is made unavailable due to distribution grid congestion. – The load-balancing system needs to pick up this information and adjust the recommended charging power/duration for the corresponding charging stop to zero. • Scenario 2: Avoid draining the battery state of charge below a minimum value – The state of charge of the battery of a specific electric bus is reduced to 50% at the beginning of the block (the planned state of charge would be 100%). – The load-balancing system must detect that the bus will not be able to complete the block with only the charging stops/durations scheduled, i.e. the minimum level of state of charge of the battery would be exceeded. – The charging duration given to the bus must be extended to compensate for the lower state of charge at the beginning of the block. • Scenario 3: Avoid exceeding the maximum level of total power consumption – A delay of one of the electric buses is introduced so that one of the charging stops will happen at the same time as another charging stop of a second block. The limit of the maximum power demand is set to 200 kW (so that only one bus may charge at once). – The charging stop of the first bus will be omitted to avoid exceeding the power limit. For the evaluation of a realistic, large-scale scenario of an entire city like Brunswick containing many electric buses at the same time, it is not possible to evaluate the performance of the load-balancing system just by looking at the results. Therefore, some metrics need to be defined and calculated based on the result of the simulation. One such metric is the ratio between the actual maximum total power demand and the scheduled maximum power demand. This metric should be lower or equal to one, i.e. the maximum power limit was not exceeded. Another metric can be defined based on the possible delays of individual buses that may be introduced because of increased charging durations. If these delays exceed a certain acceptable value, this indicates potential issues that need further inspection. Additional verification can be done by directly comparing the simulation results of the system with and without an active load-balancing system. Any change of the charging duration compared to the originally scheduled duration may only happen, when some of the constraints of the overall system are violated with no load-balancing system in place, e.g. minimum state of charge violation.

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5 Conclusion Within this chapter, we introduced problems and opportunities coming with battery electric buses in public transportation. We presented approaches for scheduling the charging operations before operation and balancing them during operation. The results are relevant particularly for charging systems with a high charging power of about 200 kW and more. Compared to his, charging systems with much less power does not offer sufficient potential for limiting and delaying the charging operations. Beside this, our approaches are universal and can be applied to any electric bus system. During operation, we are challenged by an almost infinite amount of situations and solutions. Therefore, the investigation of a machine learning approach compared to the rule-based algorithm might be beneficial. Acknowledgements The work described in this chapter has been done within the research project MENDEL (www.mendel-projekt.de). The authors would like to thank the German Federal Ministry for Economic Affairs and Energy (BMWi) for the financial support.

References 1. Zhang T, Chen W, Han Z, Cao Z (2014) Charging scheduling of electric vehicles with local renewable energy under uncertain electric vehicle arrival and grid power price. IEEE Trans Veh Technol 63(6):2600–2612 2. Sortomme E, El-Sharkawi MA (2012) Optimal scheduling of vehicle-to-grid energy and ancillary services. IEEE Trans Smart Grid 3(1):351–359 3. Deilami S, Masoum AS, Moses PS, Masoum MA (2011) Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans Smart Grid 2(3):456–467 4. Wu D, Aliprantis DC, Ying L (2012) Load scheduling and dispatch for aggregators of plug-in electric vehicles. IEEE Trans Smart Grid 3(1):368–376 5. He Y, Venkatesh B, Guan L (2012) Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans Smart Grid 3(3):1095–1105 6. Iliopoulou C, Tassopoulos I, Kepaptsoglou K, Beligiannis G (2019) Electric transit route network design problem: model and application. Transp Res Rec 2673(8):263–274 7. Wen M, Linde E, Ropke S, Mirchandani P, Larsen A (2016) An adaptive large neighborhood search heuristic for the electric vehicle scheduling problem. Comput & Oper Res 76:73–83 8. Wang G, Xie X, Zhang F, Liu Y, Zhang D (2018) bCharge: data-driven real-time charging scheduling for large-scale electric bus fleets. In: Proceedings of the IEEE Real-Time Systems Symposium (RTSS), Nashville, TN, USA, 11–14 December 9. Saha JL (1970) An algorithm for bus scheduling problems. Oper Res Q, 463–474 10. Edmonds J, Karp RM (1972) Theoretical improvements in algorithmic efficiency for network flow problems. J ACM 19(2):248–264 11. Posthoorn C (2016) Vehicle scheduling of electric city buses—a column generation approach, Master’s thesis, Delft University of Technology 12. van Kooten Niekerk ME, van den Akker JM, Hoogeveen JA (2017) Scheduling electric vehicles. Public. Transport 9(1–2):155–176

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13. Büchter H, Naumann S (2017) Charging electrically driven buses considering load balancing in the power grid. In: Proceedings of the 14th Scientific and Technical Conference “Transport Systems. Theory & Practice 2017”, September 18–20, Katowice, Poland. http://www. springer.com/gp/book/9783319439907, http://tstp.syskonf.pl/?lang=en(2017) 14. Büchter H, Naumann S (2018) Integrated electric vehicle scheduling and charging infrastructure planning for public transport, European Simulation and Modelling Conference (ESM), In: Claeys D, Modelling and Simulation 2018. European Simulation and Modelling Conference, pp. 147–153, ESM 2018, October 24–26, Ghent, Belgium 15. Hübner C, Fedorov A, Huth C, Diedrich C (2015) Extensible distribution grid automation using IEC 61131 in simulation and operation, ETG Congress 2015—Die Energiewende, 17– 18 November, VDE Verlag 16. VDE ETG/ITG (2016) Schutz- und Automatisierungstechnik in aktiven Verteilnetzen Herausforderungen, Lösungskonzepte, Empfehlungen, VDE study, 04

Evaluation of Alternatives for Realization of Opportunity Charging at Transit Stops by Analyzing the Power Grid Olaf Czogalla and Sebastian Naumann

Abstract Battery electric buses in public transportation usually need to be recharged during their daily run. In this context, the question for the most suitable charging points comes up. An important factor here is the distance of potential charging points to the nearest power grid. Any charging point needs to be connected to a power grid access point which is provided by a transformer station, whereas several charging points can be connected to one transformer station. This chapter presents a method of how to estimate the costs for creating a charging point by constructing a new transformer station at the medium voltage power grid compared as well as by connecting the charging point to an existing transformer station. This particularly includes the costs for the civil works for laying the connection cable. Comparing both values might be helpful during the decision making process. Keywords Power grid


Opportunity charging
Public transport
Electric bus

1 Introduction The erection of opportunity charging facilities at transit stops along an electrified bus route is subject to decisions on the location of bus service stops, the availability of energy supply, the type of charging standard and operations. An important precondition for the construction of charging infrastructure is the spatial proximity to power lines of medium voltage (1 kV up to 30 kV) to supply sufficient energy for charging stations. Therefore, the location of charging stations is influenced by both the location of bus stops and by the proximity to energy supply. The availability of transformer stations from medium voltage to low voltage is essential for O. Czogalla (&)
S. Naumann Institut f. Automation und Kommunikation e.V., Magdeburg, Germany e-mail: [email protected] S. Naumann e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_8

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the cost of charging infrastructure which is a significant share of the total cost of ownership (TCO). Eventually, new transformer stations and additional cabling to charging stations have to be built adding up to anyway-cost-figures. In the following sections a method is described to support the decision process by means of geographical information about the power grid including locations of transformer stations, road network, transportation network including transit stops. The attributed geographical information is administered within a spatial database system with geographic processing functionality. The approach is implemented by algorithms in a standardized structured query language (SQL) of the applied spatial database system with geographic functional extensions.

2 Variants of Location Finding for Opportunity Charging Facilities One of the basic decisions in the planning process of bus fleet electrification is about the configuration of charging infrastructure in relationship with the required battery size of the deployed electric buses. Considering an average daily mileage of about 250 km at an average energy consumption rate (ECR) of 1.5 kWh/km (including auxiliary electrical loads, such as heating, ventilation, air conditioning and powered steering) the required theoretical battery capacity would amount to 375 kWh. For practical transportation-technological reasons, a maximum depth of discharge (DOD) of 60% should be accepted to avoid premature battery ageing as well as to allow enough reserved capacity and range. In this realistic case, the theoretically required battery capacity amounts to 937 kWh. It is obvious that a full day vehicle cycle without intermediate recharging of the battery pack is not feasible, given the present technological state of the art. Typical battery sizes of battery electric buses (BEB) that are presently (2019) on the market are 160 kWh for 12-meter-long BEBs and 240 kWh for articulated 18-meter long BEBs. Provided the deployed BEB’s are equipped with above-sized battery packs, the charging configuration requires opportunity charging at one or more stops along the route of revenue service. The bus route is determined by the stops to be serviced. The location of stops is determined by the given transit demand of the residential districts or business districts, respectively. The recharging of batteries can take place at terminal stops when the timetable allows for enough charging time between arrival and departure for the next duty cycle. Ideally, the recharging time can be combined with the legal resting time of the driver. Alternatively, opportunity charging facilities are established at regular transit stop locations if timetable requirements allow for short trip interruptions or short connections to near electric power transformers offer favorable conditions. In the following, it is described as a systematic approach to identify potentially suitable locations for the establishment of charging infrastructure under consideration of spatial relationships between electrical power grid, road network and transportation network.

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3 Initial Data Sets for Location Analysis for Charging Facilities Electrical grid. The basis for the supply of electrical power for fast charging stations are power lines of medium voltage (1 kV up to 30 kV) which are laid as earth cables in general alongside public roads. Only medium voltage power lines are dimensioned such that the energy demand of fast charging processes of 200 kW and above are met. The medium voltage grid is provided as a geographical line feature shapefile, which is imported into a geographical database system with geographical processing functionality (PostgreSQL/PostGIS) [1, 2]. Power transformer stations. The establishment of fast charging facilities requires either their connection to an existing power transformer station or a new built direct connection to the medium power line, which includes also the construction of a new transformer. Transformer stations are located at distributed locations to provide the connection to the low voltage grid (400 V) for the energy supply of households. The transformer locations of the medium power line are provided as a geographical point feature shapefile. Road network. The road network is required for calculation of distances from transit stops locations to existing or planned power transformer stations by route searches within the road network. The required cable lengths correspond to the calculate distances of found road network routes. The road network was obtained from OpenStreetMap (and contributors) over free access sources and was processed in various steps to transform the given raw network data into a node/link topology. Transit stops. The local public transport operator provided transit stops including terminal stop locations including their unique identifier, names and geographical coordinates. Each stop represents an exact location for passenger boarding and alighting the public transport vehicle. In many cases, single stops belong to a larger common logical stop with the same name, e.g. in the simplest case for opposite travel directions. A combined view of the initial data sets is shown in Fig. 1 and illustrates the principle of geographical superposition that is essential for the developed evaluation method. The electrical medium power grid is displayed in light blue over the light grey road network. Dark blue squares mark the location of transformer stations; orange squares mark the location of transit stops.

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Fig. 1 Layers of initial data sets used in the cost evaluation of alternative opportunity charging locations

4 Problem Solution to the Minimization of Overall Connection Costs The problem of connection cost minimization is solved by calculating the costs of the following alternatives: A. Connection of a charging facility (i.e. the transit stop) to an existing transformer station that exists already in the power line. B. Connection of the charging facility (i.e. the transit stop) to a new (to be) constructed transformer station in a minimal road distance to the medium voltage line including. The connection costs from existing transit stops to existing or potentially to be newly built transformer stations have been analyzed on the basis of the following algorithm and cost figures provided by the utility supply firm: 1. Input data are the distance lengths of all transit stops to nearest existing transformer stations determined by route search in the GIS spatial database (PostgreSQL/PostGIS). As stated above, the road map base used for route search is OpenStreetMap. 2. For each transit stop search for the nearest transformer station and determine the route length for this stop/station pair. The result for each stop/station pair is saved into a separate field and denoted as alternative A.

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3. The distance length from a transit stop to the nearest point in the medium voltage grid is determined by nearest neighbor search from a transit stop to medium voltage grid to find the nearest point and route search from the transit stop to nearest grid point. 4. The result for each stop/nearest-grid-point pair is saved into a separate field and denoted as alternative B. 5. Use the cable line costs LC, according to information of the supply firm provided in Table 1. 6. The cost TS for a medium voltage transformer station is 40.000 EUR plus incidental costs of 2.200 EUR = 42.200 EUR 7. To account for both alternatives A and B the total costs TC are determined as follows: TC ð AÞ ¼ LC
distancestop=stationð AÞ

ð1Þ

TC ðBÞ ¼ LC
distancestop=nearestpointðBÞ þ TS

ð2Þ

For decision making, a comprehensive table was created to calculate the total costs on the basis of distance lengths for both alternatives A and B. As such, the management decision (to build new/use existing) is supported by substantial calculations for each individual case to identify suitable stop locations for charging infrastructure investment. The processing steps of cost determination for the described connection alternatives are shown exemplarily in Fig. 2. The basis of calculations are path searches in the conditionally prepared road network between geographical locations of transit stops and either existing transformer stations or new-to-be-built transformer stations as the fundamental precondition for the establishment of the charging facility. The conditionally prepared road network (A) must have the precondition of a routable network, i.e. to possess the property of a node-/link topology of a directed graph. It is necessary to emphasize this requirement since the basic road network being used for the described method has a priori, not this required property. However, this requirement can be achieved in a series of spatial database processing

Table 1 Cable line costs at different surfaces No.

Surface

Costs

Comment

a. b. c. d. e. f. g.

Stone pavement Asphalt Green surface Cable costs medium voltage Cable costs low voltage Average costs medium voltage Average costs low voltage

200 EUR/m 300 EUR/m 100 EUR/m 30 EUR/m 50 EUR/m 280 EUR/m 300 EUR/m

at an estimated share of 30% at an estimated share of 60% at an estimated share of 10%

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steps of splitting and noding of the network, which are not described further here. The routeability is indicated in Fig. 2, part A by distinguished markings for the beginning and endings of edges. Part B displays the geographic overlay of the medium power electric grid which is in general laid along with the road network but constitutes a separate network, shown in turquoise color. The overlay is essential to enable the search for relationships by using geographical functionality of the relational spatial database extension (PostGIS) [2]. In Fig. 2, part C additional point locations layers of transit stops (emphasized in purple color) and existing transformer stations (emphasized in green color) are shown. Both layers are input information for the algorithm to determine the shortest distances from the transit stop to existing the nearest transformer station or to the nearest point in the electric power grid. In the first step, the algorithm searches for the shortest path in the road network. Therefore, the start node is found as the nearest node of the road network to the given transit stop position. For nearest neighbor search it was applied a generic solution, developed by Boston GIS Consulting [3] licensed for open source usage in PostGIS. Secondly, the nearest existing transformer station to the given transit stop

Fig. 2 Processing steps of cost determination for connection alternatives of transit stops to existing or new transformer stations

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is found. In the third step, the nearest node of the electric power grid is searched which has the shortest distance to the given transit stop. Thirdly, the end node, as the nearest node in the road network to the nearest node in the power line, is found which itself is nearest to an existing station. Finally, the nearest node in the power line to the position of the transit stop is searched. The route costs in units of distance are aggregated by applying the K shortest path routing algorithm based on Yen’s algorithm [4] from the determined start node to end node and saved in a variable representing the route cost to the nearest existing transformer station (alternative A). For alternative B the start node is assigned as the nearest node of the road network to the transit stop position and the end node is assigned to the nearest node of the road network to the nearest node of the power line. The route costs in units of distance are aggregated by applying the K shortest path routing algorithm from the latter start node to latter end node and saved in a variable representing the route cost of the nearest road network node to the nearest node of the power line as a potential location of a new to-be-built transformer station (alternative B). The algorithm, described in Fig. 3 ends after for all transit stop locations the route costs for alternatives A and B were determined.

Fig. 3 Algorithm of Spatial cost calculation

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5 Evaluation Method of Resulting Cost Figures In the result of the spatial database supported cost analysis, a comprehensive table of cost figures has been created that is subject to further evaluation by the following steps. Both distance lengths A and B of Eqs. (1) and (2) are compared under consideration of the potential costs for a new transformer station in comparison of the cost for longer cable costs: • If the cost for alternative A is greater than for alternative B the decision is recommended to build a new transformer station and to connect it to the nearest point in the medium power line. • If the cost for alternative B is greater than for alternative A, the decision is recommended to connect to the nearest transformer station by newly laid cable using the shortest road path. Table 2 shows a small excerpt from the extensive decision table of the cost evaluation that has been an output of the spatial database supported cost analysis. Beside the columns of transit stop ID and the calculated distances A and B, it is shown the evaluated costs for each alternative and the recommended decision for new construction. Special cases were taken into account when the transit stop is located outside the area where the medium voltage power line shape is not available and the transit stop is located directly on the medium voltage power line (B = 0). In the latter case, the new construction decision is obviously more economical.

Table 2 Excerpt from decision table of cost analysis ID

A in m

B in m

TC(A) in EUR

TC(B) in EUR

Recommendation

1511 1512 1611 1612 1711 1712 1811 1812 1911 1912 2011 2012 2111 2112 2213 2214

216 N/A N/A N/A N/A N/A 284 284 168 200 228 237 1009 1091 487 587

209 N/A N/A N/A N/A N/A 324 324 0 23 24 10 3 0 56 123

64.800 78.900 N/A N/A N/A N/A 85.200 85.200 50.400 60.000 68.400 71.100 302.700 327.300 146.100 176.100

104.900 47.300 N/A N/A N/A N/A 139.400 139.400 42.200 49.100 49.400 45.200 43.100 42.200 59.000 79100

Use existing New construction

Use existing Use existing New construction New construction New construction New construction New construction New construction New construction New construction

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Fig. 4 Alternative variants of transformer station allocation for subsequent transit stops “Dreisch” and “Wendener Weg”

Evaluation of Table 2 directs to many cases in which the geographical relations of transit stops locations and transformer station locations have to be considered as well as incorporated into the final decision making. In Fig. 4 such typical case is illustrated where the distance to a transit stop (“Dreisch”) to both transformers (“13” and “403”) is disproportionally long in geometrical relation to subsequent transit stop (“Wendener Weg”). In this case, the reasonable recommendation is given to use transit stop (“Wendener Weg”) for construction of the opportunity charging facility. Furtherly, transit stops should be excluded for construction of opportunity charging facilities, if construction costs exceed a predetermined cost rate. Regarding the required capacity reserves of the BEB vehicles, it is also evident that a significant subset of transit stops is being shortlisted for opportunity charging (Fig. 4). By complete evaluation of Table 2, the decision on new station construction or connection to the existing station can be made based on substantial analysis and reasoning of geographical relations. Acknowledgements The work described in this chapter has been done within the research project MENDEL (www.mendelprojekt.de). The authors would like to thank the German Federal Ministry for Economic Affairs and Energy (BMWi) for the financial support.

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References 1. PostgreSQL, Open source object-relational database system, https://www.postgresql.org/docs/ 11/index.html 2. PostGIS, Spatial database extender for PostgreSQL, https://postgis.net/stuff/postgis-2.5.pdf 3. Obe R, Hsu L (2015) PostGIS in action, 2nd edn, Manning Publications, Shelter Island, NY, May 3, 2015. ISBN: 978-1617291395 4. Yen JY (1970) An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Q Appl Math 27(4):526–530

Application of Lithium-Ion Battery Thermal Management System in Electric Vehicle Simulation Feng Xie, Olaf Czogalla, and Huaiwei Shi

Abstract Lithium-ion batteries are becoming increasingly a popular energy storage form in electric vehicles (EVs) industry. However, the performance of EVs depends largely on the properties of batteries. If the cell temperature is too high or too low, it will result in some irreversible damage to the battery, e.g. the capacity, cycle life and energy efficiency would decrease significantly. The thermal runaway phenomenon will cause fire and even explosion. Although varieties of battery thermal management systems (BTMSs) have been researched, most were evaluated under experimental condition with a constant current rate. Thus, in this research, a liquid cooling methodology and the first-order Thevenin equivalent circuit model are firstly adopted to develop a BTMS. Then the practicability is validated with the current pulses generated by a battery electric bus driving model. Finally, the BTMS is able to control the temperature in the range of 20–40 °C for the whole day operation.










Keywords Lithium-ion battery BTMS Electric vehicle Thermal management

1 Introduction Nowadays, developing electric vehicles (EVs) is an attractive topic for many countries, as a result of zero emission, less noise, high tank-to-wheels efficiency, and so on [1]. The performance of a battery pack determines directly the perforF. Xie (&)
O. Czogalla Institute of Automation and Communication, Magdeburg, Germany e-mail: [email protected] O. Czogalla e-mail: [email protected] H. Shi Faculty of Process and Systems Engineering, Otto-von-Guericke University, Magdeburg, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_9

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mance of an EV [2]. Therefore, battery technologies play a great role in EV industry. As one of the largest EV markets, especially taking the leadership in the battery electric bus (BEB) markets worldwide, Chinese BEB market is well worthwhile to make a reference. According to the evaluation of the technologies in Chinese BEB market [3], over 99% is shared by lithium-ion batteries. For most types of batteries, the temperature has a vital influence on safety, efficiency, cycle life, and so on [4]. In order to guarantee the maximum energy capacity and efficiency of lithium-ion batteries, the optimum operating temperature range should be 20–40 °C [5]. If the temperature is below 0 °C or over 40 °C, the cycle life and energy capacity will decrease significantly [4]. If the temperature is extremely high, it might melt the separator inside a battery cell, leading to the internal short circuit, which will probably cause thermal runaway [6]. The first stage occurs at around 80 °C, when the solid electrolyte interface (SEI) is dissolved. Then at approximately 110 °C, hydrocarbon gases would be released to break the pressure. Furthermore, the short circuit effect will generate a large amount of heat at around 135 °C. At last, the released oxygen by the metal-oxide cathode will burn the hydrogen gases. Obviously, in this case, the battery pack has a high risk to catch fire and explode. Therefore, it is really necessary to implement an appropriate battery thermal management system (BTMS) on an EV [7]. However, many current BTMSs for EVs [8–10] have to be validated under the experimental conditions. Normally, constant charging or discharging rates of 1C– 5C are settled manually, which do not correspond with the practical energy requirements of a real EV running. A majority of thermal models are electrochemical models, focusing more on the internal microscopic behaviors of the battery cell [11]. Though electrochemical models are more accurate, they are still too complex to be embedded in microprocessors or at a system level to cooperate with other modules, e.g. an EV driving module [12]. In order to establish an electrochemical model with high accuracy, a lot of efforts have to be put into the research of the internal structures and reactions of a specific battery cell. On this aspect, they are not universal models for other types of lithium-ion batteries. Therefore, this work will develop a simple equivalent circuit battery model with thermal effect, implemented with BTMS to cooperate with the required energy plan of a real BEB. Cooling methods widely used in BTMSs are air cooling, liquid cooling, phase change material (PCM) and liquid-vapor phase changing cooling or any combination form of them [13]. The simplest way is to implement a fan in the vehicle cabin for air cooling, which requires large space and will cause the uneven cooling of cells in the same pack [14]. Liquid cooling method has a much higher efficiency of heat transfer and less noise compared with conventional air cooling [14, 15]. For direct-contact liquid cooling, the medium must be dielectric, such as mineral or silicon-based oils [4]. While for indirect-contact cooling, jackets with discrete tubes are normally used to cover the battery cells, with flowing water or other refrigerants to transfer heat [4]. However, PCM cooling is a kind of passive cooling method. With a switch of liquid and solid states at a fixed point, PCM can absorb a large amount of heat [1]. But if the EV runs in an environment with a constant high

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temperature (e.g. hot summers), or the battery pack is charging or discharging continuously, the completely melted PCM will have a relatively low thermal conductivity [15]. Considering the price and functionality, this research will adopt the liquid cooling method based BTMS to analyze and control the thermal changes of a BEB running. The remainder of this paper is structured as follows: Sect. 2 introduces the construction of equivalent circuit model (ECM) with thermal effect; Sect. 3 presents an adopted BTMS structure and operating algorithm; Sect. 4 validates the practicability of the BTMS by cooperating with a BEB driving model; Sect. 5 concludes the work in this research.

2 Modeling and Formulation 2.1

Equivalent Circuit Model

ECM is one of the most widely used modeling approaches to describe characteristics of battery cells. Chen et al. [16] found that for frequently used batteries, the self-discharge could be neglected without significant errors. And compared with other types of batteries, the parasitic branch of the equivalent circuit can be ignored due to the high coulombic efficiency of lithium-ion batteries [17]. After simplification, the most basic ECM is shown in Fig. 1. As described by Huria et al. [17], the Thevenin equivalent circuit with only one RC branch is sufficient to simulate the dynamic behaviors of one lithium-ion battery cell. In Fig. 1, the ECM is constructed with one ideal voltage source Uoc, one parallel RC branch, which is used to describe the hysteresis response of the cell, and one internal resistance R0, which is related to the instantaneous response. The input variate is represented by Iinput, with positive pulses meaning charging currents. otherwise, it means the cell is discharging. All these circuit elements are determined by temperature and state of charge (SOC), with values represented by two-dimensional look up tables. The output voltage Uoutput can be expressed by Eq. 1, where I is Iinput, U1 is the voltage of the RC branch. Uoutput ¼ Uoc þ IR0 þ U1 Fig. 1 First-order Thevenin equivalent circuit

ð1Þ

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In order to create look up tables for circuit elements, the measurement of Uoutput and charging or discharging current pulses Iinput have to be controlled under certain conditions. Several groups of data between SOC = 0% and SOC = 100% under three temperatures of 5, 20 and 40 °C are extracted respectively from the experiment of Huria et al. [17]. Then bilinear interpolation algorithm is adopted in this work to estimate the value of any point of (SOC, T) based on the known data.

2.2

Battery Selection

A common battery package installed on an EV is constructed with many battery packs connected in parallel or in series. As shown in Fig. 2, this research aims to study the thermal effect for one battery pack, which consists of eight battery cells connected in series. Heat transfer exists not only between two neighboring cells but also between side-cells and ambient air. A Lithium Nickel Manganese Cobalt Oxide (NMC) battery cell is selected for the ECM development, with specification listed in Table 1.

Fig. 2 Construction of one battery package

Table 1 NMC cell specification

Manufacturer

KokamTM

Typical Capacity Nominal Voltage Charge Condition

31.0 Ah 3.7 V 62.0 A 4.2 ± 0.03 V 155.0 A

Discharge Condition

Operating Temperature Dimension

Max. Current Voltage Continuous Current Peak Current Cut-off Voltage Charge Discharge Thickness Width Length

310.0 A 2.7 V 0–40 °C −20–60 °C 8.4 ± 0.5 mm 215 ± 2.0 mm 220 ± 2.0 mm

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Thermal Effect

Heat exchange of the cell in this EMC comes from two following sources: i. Internal generation heat results from the flowing current through the battery pack. ii. External heat convection between side-cells and ambient air, and by means of the conduction between neighboring battery cells. Thus, these two heat sources are respectively described in the model by Thermal Generation Module (TGM) and Heat Convection Module (HCM). Thermal Generation Module When the battery cell is charging or discharging, the circuit elements in ECM will generate heat due to the flowing current. Instead of describing the internal chemical reaction at electrodes, power is calculated as the main factor to cause macroscopically the thermal effect. As shown in Fig. 3, the ECM installed TGM is developed in SIMBA# [18]. As the whole input variate for ECM, Iinput flows firstly through each circuit component from I_in to I_out, then to I_L. Finally, it will flow out from I_out of the module of voltage source to the next battery cell or other modules. Via the integration of current and time R idt, a difference of energy quantity can be calculated to update SOC. By means of the input of internal power P_in and external exchanged heat power H, thus the gained heat Q can be calculated by the following equation: Z

Q ¼ ðP in þ H Þdt

ð2Þ

The battery cell is viewed as a homogenous body, with the heat distributed uniformly. Thus, the temperature change DT can be obtained by Eq. 3.

Fig. 3 Thermal generation module in ECM

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Fig. 4 Heat convection module in ECM

DT ¼

Q C
m

ð3Þ

where: C (Jkg−1K−1): is the specific heat capacity of the battery cell, which equals 810.5328 Jkg−1K−1 in this research; m (kg): is the weight of the cell, which is set to be 1 kg. With the initial temperature of the battery cell added by DT, the new temperature of TGM is computed as the new input to the lookup tables of other circuit elements to update in real time. Heat Convection Module As shown in Fig. 4, HCM is used to describe the transferred heat between two contacted objects, which have different body temperatures. With input of two temperatures, it will output the transferred heat caused by the temperature difference. The heat flow between Object A and B is computed by Eq. 4. H ¼ h
S
ðT A  T BÞ

ð4Þ

where: H (W): is the convection heat flow, which is one of the heat sources to TGM; h (Wm−2K−1): is the convective heat transfer coefficient. The cell-to-cell transfer coefficient is defined as 5 Wm−2K−1, while for the side-cell to ambient convection, it is set to be 10 Wm−2K−1; S (m2): is the area of contacted surface, where heat exchange happens; T_A, T_B (K): are the temperatures of contacted subjects.

3 BTMS Design The BTMS is designed as a thin plate, inserting between cells as displayed in Fig. 5. According to the comparison of different structures of the thermal management plates by Samba [13], no matter the channels inside the management plates are straight or tortuous, they will perform with little difference in removing heat. Therefore, one of the possible designs is selected for this research. The adopted plan is a thin aluminum plate with five straight cooling channels distributed uniformly.

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Fig. 5 Battery cells covered with thermal management plates

In order to keep the batteries in the optimum range of 20–40 °C, an effective volume flow rate of water at the inlet is qv = 30 Lmin−1. Then the whole management plate is viewed as a homogenous body with a heat transfer coefficient of 100 Wm−2K−1. Thus, heat removed by this management plate can be computed by Eq. 5. H ¼ h
S
ðToutlet  Tinlet Þ

ð5Þ

where: Tinlet, Toutlet (K): are respectively the temperatures of inlet and outlet of the cooling channel. The working mechanism of this BTMS (i.e. control of Tinlet) is explained further in Algorithm 1. Because the side-cells can convect heat directly to the ambient air, they have the minimum temperature Tmin. In the similar way, the medium cells have to collect heat of neighboring cells and can not convect the heat effectively, so they have the maximum temperature Tmax. If the maximum temperature of the battery pack is higher than 40 °C (i.e. 313.15 K), the cooling mode will be activated with water of 0 °C pumped to the inlet of the cooling channel. In another case, if Tmin is lower than 20 °C, the heating mode will be activated with Tinlet of 40 °C, until the minimum temperature reaches 25 °C. After the installation of BTMS to the battery pack in SIMBA# [18], the heat transfer between battery cells is shown in Fig. 6. It presents the connection of two neighboring modules of cells. Thereinto, Cell1 is a side-cell, Cell2 is an inner cell. Firstly, with output of body temperatures being input to the HCM, a gained or lost heat will be input to the cell module. For the Cell1, there is also a convection heat from the ambient air. For Cell2, it has to exchange heat with both neighboring cells. The body temperature can also be the input to the BTMS. Because if BTMS is activated, there will be a heat exchange between the cell and the cooling plate. All these heat exchange should be considered to update the body temperature.

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Fig. 6 Connection of battery cells

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4 Validation in BEB Running In order to observe the temperature changes of the battery pack during a real BEB running, this research adopts the electric bus vehicle model developed by Czogalla et al. in the former work [19]. The technical data of the selected BEB is listed in Table 2. The EV driving model is presented in Fig. 7, trip driving cycles are firstly input to the driving model, which are generated by a GPS tracker on a bus. Then a comparison between the tracked velocity and simulation velocity is proceeded to reach the driving plan. Then a PI-controller is used to control the acceleration or deceleration of the BEB. After processing the velocity, motor torque T_mot multiplied by rotation speed x is viewed as the required power for driving. Together with the auxiliary power P_aux and regenerative braking power, the required current flows from batteries are therefore obtained, which can be the input to the battery model. Finally, the inertia force F_i can be computed by Eq. 6 to calculate the acceleration a, which can update velocity v for the next input. F tr ¼ F aero þ F rr þ F grade þ F i q ¼ Crr
m
g þ Cd
Af

v2 þ m
g
sinð/Þ þ m
a 2 where: F_tr (N): is traction force; F_aero (N): is aerodynamic force; F_rr (N): is rolling resistance; F_grade (N): is grade force; F_i (N): is inertia force; Crr: is rolling resistance coefficient; m (kg): is vehicle mass; g (m/s2): is gravitational acceleration; Cd: is drag coefficient;

Table 2 Specification of BEB for simulation

Model

Solaris Urbino 12 Electric

Max. Power Max. Torque Battery Capacity Uoc Mass/Load Frontal Area Final Gear Drive Ratio Accessory Load

250 kW 973 Nm 240 kWh 600 V 13,790 kg/5100 kg 8.66 m2 22.6 5 kW

ð6Þ

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Fig. 7 EV driving model

Af (m2): is vehicle frontal area; q (kg/m3): is air density; v (m/s): is vehicle velocity; Ф: is road angle in radians; a (m/s2): is vehicle acceleration. With the input parameter of BEB to the driving model, a series of required energy plan for one specific bus route is generated in the form of current pulses. Then a processed current plan could be further input to the battery stack model, which is shown in Fig. 8. The ambient temperature is set to be 25 °C, which exchanges heat directly with the both side-cells. On the outside, the package is covered with BTMS plates to manage the temperatures of side-cells. The heats and temperatures of the side-cells are represented respectively by H1, T1 and H8, T8. Current pulses of one duty cycle is the whole input to the battery pack to simulate the temperature changes during operation. Then all simulation results for each duty cycle are overlapped repeatedly, making a reference to a practical schedule for one bus route. Simultaneously, SOC can be obtained to estimate the residual energy stored in the battery pack. Finally, 18 duty cycles in total are simulated in this model, with the thermal changes shown in Fig. 9. The initial temperature of battery stack is set to be the same as the cabin environment temperature of 25 °C. One duty cycle contains one return trip from Terminal 1 to Terminal 2 then again back to the starting point Terminal 1. As displayed in Fig. 9, the red line represents the temperature of the middle cell (i.e. Tmax), the blue line means the temperature of a side-cell (i.e. Tmin). At the end of each cycle, there will be an opportunity charging for 5 min, which causes the temperature to climb on a new thermal step. The graph only presents the last 12 duty cycles after 18,000 s for details. On the whole, the

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temperatures rise up while the BEB is operated. When Tmax reaches 40 °C, the BTMS is activated with a sharp decline performed on the temperature curves. Then after several cycles, the temperature of middle cell will rise up more quickly compared with the slight thermal change on side-cell, since the heat is more difficult to be dissipated effectively. For the whole day operation of more than 15 h, this BTMS can meet the basic demand to keep the battery operated in the optimal temperature range.

Fig. 8 Battery pack model for validation

Fig. 9 Thermal changes for one day BEB operation

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5 Conclusion In this research, an effective BTMS, based on liquid cooling method, is applied on a BEB running simulation. From an engineering perspective, this BTMS is validated to be able to perform sufficiently on heat management of battery cells for a long-term of BEB operation. Even though some drawbacks arise on the final results, such as the gradient temperature DTg ¼ Tmax  Tmin is larger than 5 °C, which can be further solved by adjustments on BTMS. To narrow down the temperature difference, the heat removing efficiency of middle cell should be improved, or if it is necessary, the efficiency of side-cell could be reduced properly. This goal can be accomplished by means of controlling the Tinlet for different cooling plates, or adjusting the volume flow rate of water in the cooling channels. For example, the flow rate for side cooling plates is 30 Lmin−1, the flow rates for the inner plates could be improved respectively to be 40 Lmin−1 and 50 Lmin−1. Acknowledgement Research in this paper is based on the project “PLATON-Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet”, which is funded by German Federal Ministry of Transport & Digital Infrastructure (BMVI) under grant number 03EMEN17 and co-funded by the European Commission under the framework of “Electric Mobility Europe”.

References 1. Li J, Zhu Z (2014) Battery thermal management systems of electric vehicles. Master’s thesis, Chalmers University of Technology 2. Karimi G, Li X (2013) Thermal management of Lithium-Ion batteries for electric vehicles. Int J Energy Res 37(1):13–24 3. Du J, Li F, Li J, Wu X, Song Z, Zou Y, Ouyang M (2019) Evaluating the technological evolution of battery electric buses: China as a case. Energy 176:309–319 4. Saw LH, Tay AAO, Zhang LW (2015) Thermal management of Lithium-Ion battery pack with liquid cooling, 31st Thermal Measurement, Modeling & Management Symposium (SEMI-THERM). IEEE, pp 298–302 5. An Z, Jia L, Ding Y, Dang C, Li X (2017) A review on Lithium-Ion power battery thermal management technologies and thermal safety. J Therm Sci 26(5):391–412 6. Saw LH, Tay AAO (2013) Thermal modeling and management of Li-ion batteries for electric vehicles. In: Proceedings of the ASME 2013 international technical conference and exhibition on packaging and integration of electronic and phonic microsystems 7. Electropaedia. Cell chemistries—how batteries work. https://www.mpoweruk.com/ chemistries.htm 8. Hekmat S, Molaeimanesh GR (2020) Hybrid thermal management of a Li-ion battery module with phase change material and cooling water pipes: an experimental investigation. Appl Therm Eng 166:114759 9. Oh S, Lee J, Lee H, Shin D, Thalluri T, Shin K (2019) Design of battery thermal management unit with PCM for electrical vehicle: part I: modelling and analysis of pouch type battery cell. In: 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), pp 82–85

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10. Xu X, Tong G, Li R (2020) Numerical study and optimizing on cold plate splitter for Lithium battery thermal management system. Appl Therm Eng 167:114787 11. Fotouhi A, Auger DJ, Propp K, Longo S (2018) Accuracy versus simplicity in online battery model identification. IEEE Trans Syst, Man, Cybern: Syst 48(2):195–206 12. Pattipati B, Sankavaram C, Pattipati K (2011) System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Trans Syst, Man, Cybern, Part C (Appl Rev) 41(6): 869–884 13. Samba A (2015) Battery electrical vehicles-analysis of thermal modelling and thermal management. Ph.D. dissertation. LUSAC (Laboratoire Universitaire des Sciences Appliquées de Cherbourg), Université de caen Basse Normandie; MOBI (the Mobility, Logistics and Automotive Technology Research Centre), Vrije Universiteit Brussel 14. Pesaran AA (2001) Battery thermal management in EV and HEVs: issues and solutions. Battery Man 43(5):34–49 15. Xia G, Cao L, Bi G (2017) A review on battery thermal management in electric vehicle application. J Power Sources 367:90–105 16. Chen M, Rincon-Mora GA (2006) Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans Energy Convers 21(2): 504–511 17. Huria T, Ceraolo M, Gazzarri J, Jackey R (2012) High fidelity electrical model with thermal dependence for characterization and simulation of high power Lithium battery cells. In: IEEE International Electric Vehicle Conference, pp 1–8 18. ifak, Simba# 3.0, ifak e.V. Magdeburg. https://simba.ifak.eu 19. Czogalla O (2019) Xie, F.: methods and tools for public bus fleet electrification in the area of sustainable city transportation. 26th world congress on intelligent transport systems, Singapore

Case Study and Cost Analysis of a Bus Fleet Electrification Agnieszka Tubis, Sylwia Werbińska-Wojciechowska, Maria Skrętowicz, Zbigniew Sroka, and Joanna Świeściak

Abstract In 2018, the Act on electromobility and alternative fuels took effect in Poland. The law imposes an obligation on municipalities servicing the public transport to exchange at least 30% of bus fleet for electric buses by 2028. In the chapter, the impact of the fleet replacement on the emission of air pollutants rate in global and local scale has been analyzed. For comparison, the option of exchanging the fleet for buses meeting the Euro VI standard was also assumed. For this purpose, it was necessary to analyze the current structure of the bus fleet of MPK Wroclaw (manufacture year, Euro standards, type of buses, etc.). It was indicated what impact on the air quality will have the fleet electrification on the local scale (in Wroclaw) and on the global scale (emission from the power plant during the production of electricity needed to charge electric buses). Keywords Electromobility


Public transport
Gas emission
Fleet replacement

M. Skrętowicz
Z. Sroka
J. Świeściak Department of Vehicle Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland e-mail: [email protected] Z. Sroka e-mail: [email protected] J. Świeściak e-mail: [email protected] A. Tubis
S. Werbińska-Wojciechowska (&) Department of Operation and Maintenance of Technical Systems, Wroclaw University of Science and Technology, Wroclaw, Poland e-mail: [email protected] A. Tubis e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_10

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1 Introduction The growing number of inhabitants in large agglomerations in Poland, as well as their changing behavior and transport preferences, mean that the number of individual transport journeys increases significantly. An increasing number of vehicles on city streets causes congestions and emissions of more and more pollution into the atmosphere. Congestion on the main streets of large cities means that even public transport vehicles are forced to wait in traffic jams. In the case of older buses that are used for public transport services performance, there can be observed an increase of gas emissions to the atmosphere connected with their long waiting among crowded city streets. For this reason, there can be observed a constantly deteriorating air condition, mainly in large agglomerations, which significantly reduces the quality of life of the inhabitants of these cities. Deteriorating air quality in large Polish cities, as well as the current trends in the field of transport policy of the European Union, led to the adoption by the Polish Government on January 11, 2018, of the Act on electromobility and alternative fuels (Journal of Laws of 2018 item 317). This Act imposes on local government units of the cities (where the number of inhabitants exceeds 50,000 people) systematic replacement of the fleet of vehicles providing public transport services for electric buses. These requirements should be fulfilled, starting from 01/01/2021. The share of zero-emission vehicles is specified in the Act in the form of minimum percentage shares, which the local government unit is obliged to achieve in subsequent years. The required shares are presented in Table 1. The introduced Act on electromobility caused an increase in demand for zero-emission buses reported not only by the largest municipalities in the country, but also those places that are not subject to the above requirements (e.g. in the Polish province of Lower Silesia: Swidnica, Boleslawiec). However, due to the high costs associated with their purchase and operation, all types of funds and subsidies that support municipalities and carriers in the purchase of zero-emission vehicles have gained in importance. Electrification of bus lines in large cities in Poland is currently in the first phase of implementation, in accordance with the guidelines of the above mentioned Act. However, rising electricity prices, with the traditional energy production system being forced in Poland, meaning that more and more people are asking about the legitimacy of such a quick and wide replacement of combustion buses for zero-emission vehicles [21]. Table 1 The share of zero-emission vehicles in the fleet serving public transport in the city Year of achievement

Percentage of zero-emission vehicles (%)

2021 2023 2025 2028

5 10 20 30

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Therefore, the purpose of the chapter is to present the benefits of the introduction of zero-emission vehicles for public transport services in large urban agglomerations and the costs associated with their purchase. To achieve this goal, the literature review on electromobility in the public transport system is presented first. Then, on the example of a Polish agglomeration, a plan for replacing buses for zero-emission vehicles with a selected public transport carrier is to be discussed and analyzed. Moreover, an indication of the expected environmental benefits and purchase costs is to be developed.

2 Electromobility for Public Systems—Literature Review The common understanding of ‘e-mobility’ is in terms of shorthand for ‘electrified automobility’ [16]. However, we can also define electromobility as an application of electric propulsion in the daily transport sector [10]. E-mobility is an idea that is designed to respond to the needs of the concept of sustainable development. According to the sustainable development assumptions, the environmental burden caused by transport emissions should be significantly reduced. This applies in particular to urban areas. The pressure exerted by increasingly stringent environmental regulations should lead to changes in the transport preferences of residents, but also to the actions taken by the government for sustainable development-oriented changes [2]. Typically, this kind of change can be achieved through technological innovation [11]. However, for e-mobility to become such an innovation, it must meet the three basic features of system innovations identified by Geels et al. [9]. It must be associated with: (1) the spread of new technology; (2) by the emergence of new functionalities as the result of socio-technical co-evolution; (3) meeting certain sustainability criteria. Since 2009 e-mobility has become very much in vogue. In the public debate, e-mobility is presented as the ultimate solution to nearly all transport problems [17]. Proponents of the development of the e-mobility sector have discussed a variety of environmental and economic advantages. One of the central arguments is that it will contribute to sustainable transport development [17], and will allow better usage of energy resources [7]. However, researchers note that e-mobility is also connected with a large number of challenges and obstacles in three basic areas related to the development in these fields: 1. Technology—the fundamental technological change for the automotive industry [14]. The main problem today is also battery life and the short distance traveled by the vehicle between recharges, 2. Economics—the e-mobility sector requires extensive funding [17], 3. Environment—the main concern is the environmental impact of the high volume of electricity that needs to be generated [20]. This mainly applies to countries such as Poland, where energy is generated from fossil fuels.

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Road transport based on vehicles with internal combustion engines is one of the main sources of air emissions in European cities [1]. Road transport contributes primarily to increasing the concentration of such pollutants in the atmosphere as nitrogen dioxide and particulate matter, including soot. Both of these pollutants occur primarily in urban areas, for example in large urban agglomerations, where there is increased traffic from road transport vehicles, and thus also public transport vehicles. Compounds that are harmful to the health of living organisms and adversely affect the environment, emitted in high concentrations from the exhaust system of internal combustion engines, are such compounds as: • Carbon oxide CO • Hydrocarbons HC including volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) • Nitrogen oxides NOx (a mixture of nitric oxide NO and nitrogen dioxide NO2) • Particular matter PM. Exhaust gas emitted to the atmosphere from diesel engines used in buses causes various complaints. Health effects are associated with both, short (several days or several weeks) and long-term (several months or even several years) exposure to air pollution. In case of electric buses, additionally, the sulfur dioxide is emitted due to the combustion of carbon necessary to generate electricity. Carbon monoxide, which is a product of incomplete combustion of carbon-containing substances, is normally a colorless, odorless gas. It is highly toxic. In the human body, it most quickly damages tissues and organs most susceptible to hypoxia: central nervous system, heart muscle, lungs [8]. The pathomechanism of carbon monoxide interaction on the body is based on the greater affinity of hemoglobin for carbon monoxide than for oxygen (hemoglobin combines with carbon monoxide up to 200–250 times faster than with oxygen). As a result, a hemoglobin-carbon monoxide complex is formed, the so-called carbon monoxide hemoglobin (HbCO) that is not able to attach and transport oxygen. Sulfur dioxide is a very toxic, colorless, non-flammable gas with a pungent odor. It is classified as a very irritating gas. It enters the human body by inhalation and is absorbed through mucous membranes. Sulfur dioxide can dissolve in the secretions from the respiratory tract, forming sulfuric acid, which enhances the irritating effect on tissues. The olfactory palpability threshold starts at 0.008 mg/dm3, while concentrations that may cause sore tongue, mouth and throat, conjunctiva and cough are in the range of 0.02–0.05 mg/dm3. Immediate death can occur with sulfur dioxide concentrations in the range of 1.4–1.5 mg/dm3 [5]. Oxide and nitrogen dioxide are gases that are the source of fuel combustion processes. These are substances which in sufficiently high concentrations can cause sudden death. The major danger is the depressing effect of nitrogen oxides on the respiratory and blood circulatory systems, but its effects on the body are more complex. In contact with skin, mucous membranes and conjunctiva of the eye, nitrogen oxides have an irritating effect, causing coughing, shortness of breath,

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redness of the eyes. With prolonged exposure, it can convert nitric oxide to nitric acid, which may cause inflammation and local tissue necrosis [22]. The presence of suspended dust in the air carries a lot of health-threatening complications. PM 10 particulate matter (particles smaller than 10 lm) is able to reach the upper respiratory tract and lungs. In turn, PM 2.5 dust (dust particles smaller than 2.5 lm) are small enough that they can enter into the bloodstream from the alveoli pulmonis. Moreover, the human body is not able to excrete such small particles, therefore, starting from PM10, dust particles accumulate in the lungs and accumulate. The impact of particulate matter on the human body is multidimensional. Dust particles may contain on their surface substances harmful to health, e.g. benzo(a)pyrene or heavy metals. Inhalation of air contaminated with PM 2.5 dust leads to the penetration of toxic substances directly into the bloodstream. The result of this process can be respiratory, cardiovascular and cancer diseases [19]. Dust settling directly into the lung tissue may be the cause of its focal fibrosis. Hydrocarbons affect human organisms primarily mutagenic and carcinogenic. More than 200 different hydrocarbons can be identified in the exhaust gas. The most common are benzene (belonging to the VOCs group) and benzo(a)pyrene from the PAHs group. Benzene is able to enter the body primarily through the respiratory system, but is also absorbed through the skin and from the digestive tract. The direct action of benzene on the skin and conjunctiva causes irritation, pain, redness, itching and eye lacrimation. Benzo(a)pyrene tends to accumulate in tissues, especially those rich in lipids. After entering into the body, benzo(a)pyrene undergoes a number of metabolic transformations, which result in the formation of active metabolites with carcinogenic and mutagenic effects [15, 18]. Research shows that electric mobility has a low carbon foot-print (provided that the electricity is not generated from fossil fuels), and can employ renewable energy. It has less local impact on noise levels and air quality. As shown by studies carried out by Laurent and Windisch [12] in 11 countries, the main goal of introducing e-mobility is to reduce the impact of pollution on the environment and improve local quality of life. In addition, depending on the country, the aspect of e-mobility is also combined with energy security and industrial policy. Laurent and Windisch also indicate that most governments address simultaneously the development of demand and that of a charging infrastructure, plus R&D objectives if there is a stake of industrial policy. Comprehensive policy packages are devised, which include framework design and equipment standardization, demand stimulation using fiscal incentives, cooperative procurement of large fleets of vehicles, the implementation of charging spots and the deployment of pilot projects in selected areas. The subject of numerous studies in the area of e-mobility are challenges related to the construction and implementation of an efficiently performing e-mobility system (e.g. [3, 10]). One of the main challenges is currently growing technical heterogeneity, changing requirements, human factors and multidisciplinary aspects [10]. Many researchers, therefore, point out that two key aspects should be the subject of research in the e-mobility system [6, 13]: (1) technical or technological complexity; (2) organizational complexity. For this reason, the first critical issue in the engineering process of systems for e-mobility is the definition of requirements,

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which typically reflect complex organizational problems and their transfer to a solution system [3]. Further challenges arise from the complexity of the different steps within the systems engineering process: design and modeling of the system architecture and its development [10].

3 Case Study of Fleet Exchange Plan for Public Transport Service Provider Operating in the Given City The research was conducted in a selected Polish city, which in 2019 had approximately 640,000 inhabitants, and therefore fell under the provisions of the Act on electromobility and alternative fuels discussed above. The basis for calculating the required number of electric buses is the total number of vehicles used to handle public transport at the end of 2020. Public transport in the city is currently provided by 3 carriers—one municipal company and two private carriers. However, private carriers provide transport services as subcontractors of a municipal company. The size of the fleet currently used by all three transport companies is 429 buses. None of the carriers currently operates a zero-emission vehicle. The required percentage and quantity of electric buses in subsequent years, according to law regulations, is presented in Table 2. When replacing combustion buses for electric vehicles, the oldest and the ones with the lowest combustion standard should be replaced first. Only such targeted actions will reduce the level of air pollution in the city through a greener fleet investment. Thanks to the smaller amount of exhaust gases generated by new buses, not only the quality of passenger service but also the general quality of life of residents will be improved. Private carriers, signing in recent years a contract for the provision of public transport services, have committed to using a modern vehicle fleet that meets the latest requirements in the field of exhaust emissions (vehicles with the EURO 6 standard). Therefore, only the municipal transport company has the oldest vehicles that should first be replaced by zero-emission buses. It currently has vehicles in its fleet whose combustion standard is at the level of EURO 3, EURO 4 and EURO 5. A detailed analysis of the vehicle fleet is presented in Subsect. 3.1.

Table 2 The share of zero-emission vehicles in the fleet serving public transport in the analyzed city Year of achievement

Percentage of zero-emission vehicles (%)

Number of electric vehicles that are obligatory for the analyzed city

2021 2023 2025 2028

5 10 20 30

22 vehicles 43 vehicles 86 vehicles 129 vehicles

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Bus Fleet Characteristic

In the presented case, public transport services are provided by the municipal transport company and the subcontractor. The city’s communication network consists of 115 basic communication lines, including 22 tram lines and 93 bus lines. Depending on the adopted criterion, these lines can be divided, among others, into lines: • urban, suburban and zonal; • daytime and nighttime; • normal, periodic, suburban, fast, peak and night. Generally, operational work is about 24,000,000 vehicle kilometers annually and during the year over 200,000 passengers are transported. The implementation of the set transport performance includes the use of a specific fleet of bus vehicles. On working days, 348 buses are required for ongoing communication network service, while on days off this number drops to 188 buses. Zonal lines served by other carriers (private companies) and night lines were not included in further analyzes. Depending on the line, the commitment of a given number of vehicles is different. An important aspect in the analysis of the communication network is its operational parameters. To this end, the city’s communication network and its communication lines are analyzed in terms of: • number of vehicle kilometers divided into lines by type of day, • communication velocities (Vcom) and operating (Vop) for communication lines by type of day. Based on the available data, it was calculated that the bus rolling stock carries out operational work at the level of: • 74,994.94 vehicle kilometers during the school working day; • 49,945.81 vehicle kilometers during the free school day. However, analyzing the speeds implemented on individual communication lines, it can be seen that: • for the school working day, communication speeds are between 16.6 and 33.1 km/h, and operating speeds are between 11.7 and 26.7 km/h; • for a school day off, communication speeds are between 18.0 and 28.5 km/h and operating speeds are between 10.9 and 23.4 km/h. As it was written, there are currently 429 buses operating in the analyzed city, including 329 buses belonging to the municipal company and 77 buses belonging to the subcontractors. The municipal company has 1 MIDI bus, 154 Solo buses and 174 articulated buses. In Fig. 1 the age structure of the fleet is presented, while Fig. 2 shows the structure of the fleet according to the emission standards met (as of

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Fig. 1 Structure of vehicles according to age

Euro 3 18%

Euro 4 0,30%

EEV 1%

Euro 5 30%

Euro 6 51%

Fig. 2 Structure of vehicles according to combustion standards

EEV; 2 Euro 3; 59 Euro 4; 1 Euro 6; 167

Euro 5; 100

2020). The charts include only the municipal company fleet. All vehicles belonging to the subcontractors (77 units) meet the Euro 6 emissions standard. Based on information on the fuel consumption of vehicles meeting individual emission standards and information about the annual mileage of vehicles, the emission rate from the current fleet of the municipal company and the subcontractor was calculated. For this purpose, emission factors recommended by the Polish Center For EU Transport Projects have been used [4]. Table 3 presents the annual emission of carbon dioxide (greenhouse gas) and tropospheric pollutants in 2017. The above analyzes were the basis for further actions in the field of fleet replacement, and the calculated emission was a reference to the effects of fleet replacement on atmospheric air.

Case Study and Cost Analysis of a Bus Fleet Electrification Table 3 Annual emission of carbon dioxide (greenhouse gas) and other air pollutants in 2017, in Mg (CO2—carbon dioxide; VOCs—volatile organic compounds; NOx— mixture of nitric oxide and nitric dioxide; PM—particle matter)

3.2

Standard

CO2

Urban transport company Euro II 521.29 Euro III 11,645.81 Euro IV 5.37 Euro V 9617.4 Euro VI 4989.6 Sum 26,779.6 Subcontractors Euro VI 5896

157 VOCs

NOx

PM

2.14 28.68 0.01 16.51 2.42 49.76

13.62 217.27 0.07 71.77 7.45 310.18

0.29 4.35 0.0004 0.72 0.19 5.54

2.86

8.8

0.22

Fleet Exchange Variants

The conducted analysis compared two options for replacing an obsolete fleet of vehicles. Option W1 concerned the replacement of all vehicles with conventional buses with the current highest Euro 6 combustion standard. Option W2 concerned the replacement of all vehicles with electric buses, which will be powered in two options: (1) slow-charging plug-in chargers (during long stops at the bus depot) and (2) fast-charging pantographs (during short stops at bus loops). The correctness of the adopted research variants is confirmed by the implementations carried out in other cities in Poland. When implementing a solution based on electric buses in large cities, the option of recharging the vehicle on bus loops is adopted, and not every time it goes to the depot when the power supply battery runs out. The requirement to replace traditional buses with zero-emission ones should primarily apply to the oldest vehicles, which have the lowest combustion standard. Therefore, based on the characteristics of the vehicle fleet, a replacement schedule has been prepared, starting from EURO 3 vehicles (2021–2023) and ending with EURO 5 vehicles. This schedule is presented in Table 4.

Table 4 Vehicle replacement schedule in subsequent statutory periods Replacement data

Standard combustion

Lenght (m)

Age

Number of replaced vehicles

2021 2023

EURO EURO EURO EURO EURO EURO EURO EURO

12 12 18 18 12 12 18 12

15 15 15 15 12 11 11 11

22 8 13 16 1 26 22 21

2025

2028

3 3 3 3 4 5 5 5

Total number of electric vehicles in the fleet 22 43 86

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In order to determine the bus routes that should be electrified, a detailed analysis of current timetables was carried out. The analysis was carried out for timetables valid on working (school) days, due to higher demand for bus rolling stock and a shorter interval between courses occurring on a daily basis. The selection of bus lines for electrification took into account the following guidelines: • the electrification process covered only bus lines that are currently served by the municipal company (due to the arguments presented above), • for electrification, lines were indicated that occur in the very center of the city, which in the future may be included in the low-emission transport zone, • lines that run through the city area with high traffic congestion are electrified, which allows minimizing the effects of ineffective emissions from the entire transport system at road congestion, • the order of selected lines has been correlated with the length of buses to be replaced in accordance with the schedule presented in Table 4. Finally, 16 daytime bus lines, currently operated by the municipal company, were taken over for electrification.

4 Analysis of Air Quality for the Defined Fleet Exchange Plan Air quality has a significant impact on the health and lives of people and their comfort of living and staying in the city. Therefore, one of the key aspects of making investments is the reduction of emissions of pollutants in the lower atmosphere. For the variants selected in the financial and economic analysis, the amount of air pollutant emissions in individual stages of the investment was calculated. It was necessary to make the following assumptions: • A fleet of a municipal company (329 buses) and subcontractors (77 buses) were analyzed in total—as of 2020, • The current timetable was analyzed, based on which the annual mileage of the entire fleet (429 buses) was calculated, which was 22,195,290 km—it was assumed that it will not change in subsequent years, • Total number of vehicles in the fleet will not change in subsequent years; • In the W1 variant, each bus performs the same mileage annually, which is 67,463 km, • The mileage of a given bus type in terms of compliance with exhaust emissions standards was calculated as the product of the number of these buses in the fleet and the calculated annual mileage of one bus, • In both W1 and W2 variants, fleet replacement begins with the removal of the oldest vehicles from service, • In variant W2, the annual mileage of electric buses was calculated on the basis of proposals for the electrification of bus lines in individual years,

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• Remaining annual mileage for combustion buses in the W2 variant was divided by the number of conventional vehicles remaining in the fleet—each combustion bus will have the same mileage in a given period, • In the case of internal combustion buses, fuel consumption per 100 km was calculated for each type of buses in terms of exhaust emission standards being met as the average fuel consumption based on the fuel report provided by municipal company, • For electric buses, energy consumption of 250 kWh/100 km has been assumed. Emission factors for internal combustion and electric buses were adopted in accordance with the guidelines of the Center For EU Transport Projects [4]. In Table 5 the volume of annual emissions calculated at the next stages of the fleet exchange investment is presented. Based on the above, the costs of air pollution were calculated over the entire investment period, i.e. in the years 2020–2045. To this end, it was necessary to calculate the sum of air emissions over the entire investment period in both variants and to take into account the unit cost. Unit costs, like emission factors, were adopted on the basis of the guidelines of the Center For EU Transport Projects [4]. Table 6 presents the results of the above analyzes. The volume of emissions in 2020–2045 (before the first stage of implementation) was adopted at the level of emissions calculated for 2019. Table 6 shows that the reduction of emission and emission costs when using electric vehicles will cover only volatile organic compounds, their emission throughout the entire investment plan. In addition to these compounds, there is a clear increase in carbon dioxide emissions and a slight increase in particulate emissions. Additionally, when using electric buses, there are costs associated with the emission of sulfur dioxide, which is not presented in the W1 variant. However, it should be taken into account that this state of affairs is caused by the fact that the professional energy sector in Poland is based on coal combustion. With the increase in the share of renewable sources in the electricity generation process or with the implementation of nuclear energy, the costs associated with emissions from the use of electric buses will decrease. For this moment, the implementation of the W2 variant will increase the cost of exhausts emission by over PLN 173 million.

5 Financial Analysis for the Planned Vehicle Replacement Process The schedule for replacing buses on selected lines is the same for both compared variants (W1—replacement with conventional vehicles and W2—replacement with electric vehicles). Detailed quantitative data on the planned replacement on individual lines is presented in Table 7. The number of replaced vehicles complies with the requirements of the Act and the numbers presented in Table 4. If the number of

W2

29,464 31.95 165.91 2.87 2.96

2021 W1

28,324 32.67 164.95 2.73 0

Substance

CO2 VOCs NOx PM SO2

27,754 27.89 125.00 1.95 0

2023 W1 30,117 26.20 125.86 2.21 6.16

W2

Table 5 Annual emission rate from the fleet at stages of the investment, in Mg

27,178 21.30 79.94 1.26 0

2025 W1 33,123 16.26 78.46 1.90 15.60

W2

26,946 16.59 57.31 1.11 0

2028 W1

38,590 6.75 54.58 2.36 30.57

W2

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Table 6 Summary of emissions and costs of exhaust emissions in 2020–2045 Substance CO2 VOCs NOx PM SO2 Sum

Emission, Mg W1 W1

Difference (W2 − W1)

Cost, PLN W1

W2

Difference (W2 − W1)

707,617 522 2148 37 0 710,325

234,428 −197 −50 25 615 234,822

170,779,215 6,718,959 213,758,477 61,968,638 0 453,225,289

230,815,951 3,916,975 207,913,799 109,359,068 75,144,252 627,150,046

60,036,736 −2,801,984 −5,844,677 47,390,430 75,144,252 173,924,757

942,046 325 2098 62 615 945,147

Table 7 Schedule of vehicle replacement in subsequent years on individual lines

Line number

Vehicle length (m)

Line 1 12 Line 2 12 Line 3 12 Line 4 18 Line 5 18 Line 6 12 Line 7 18 Line 8 12 Line 8 18 Line 9 12 Line 10 18 Line 11 12 Line 12 12 Line 13 12 Line 14 18 Line 14 12 Line 15 12 Line 16 18 Buses 12 m Buses 18 m Sum

2021 10 10 2

22 – 22

2023

6 6 7 2

8 13 21

2025

4 7 6 5 4 6 2 4 5

27 16 43

2028

6 2 8 8 2 9 8 21 22 43

vehicles operating a given line is greater than the quantities assumed in Table 4, then the electrification process of this line is spread over two consecutive statutory periods. In the W2 option—replacing the existing fleet with electric buses—for the analysis, it is also necessary to adopt guidelines regarding the deployment of fast-charging infrastructure for the efficient operation of electrified lines. The operational model applicable in most major cities in Poland was adopted for the

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analysis, namely that each line operated by electric buses will have a minimum of one dedicated pantograph on each bus loop. The operational model adopted in this way provides access to vehicle charging stations in fast mode, required especially during rush hours traffic. The analysis of demand indicated that it is necessary to prepare the infrastructure and install 32 pantographs in the entire analyzed investment period, including 6 pantograph positions in 2021, 6 pantograph positions in 2023, 8 pantograph positions in 2025 and three pantograph positions in 2028. Prices of traditional buses for the W1 option were determined on the basis of selected tenders carried out in 2018 in large cities in Poland for the purchase of internal combustion vehicles with the EURO 6 emission standard. For 18 m buses, a net unit price of 1,567,130.00 PLN was adopted, while for 12 m buses a net unit price was adopted at PLN 1,159,065.00. Table 8 shows the costs for each of the four periods analyzed, along with the type of vehicle replaced. The total value of the entire investment in Option W1 is PLN 170,330,700.00. This investment only covers the replacement of buses with low emissions standards for vehicles with currently maximum emissions standards (EURO 6). In Option W2, the scope of investment works concerns the replacement of buses for electric vehicles and the purchase of associated infrastructure in the form of pantographs. The W2 option assumes that the operator will use Plug-in chargers as a source of battery top-up during the night operation of buses in the bus depot. The Plug-in chargers are purchased together with the bus, which means that the adopted unit price of the vehicle also includes the cost of the Plug-in charger (electric vehicle pricing model in most tenders announced by Polish cities in 2019). During the daily operation, the vehicles will be charged on the indicated bus loops at intervals between the routes in progress, using pantographs. Thanks to the battery recharging system defined in this way (based on Plug-in chargers and pantographs),

Table 8 Costs of replacing vehicles with traditional buses with the EURO 6 standard in option W1 Replacement data

Combustion standard

Length (m)

Number of vehicles

Purchasing cost of vehicles [PLN]

Total purchase cost for each period [PLN]

2021 2023

EURO EURO EURO EURO EURO EURO EURO EURO

12 12 18 18 12 12 18 12

22 8 13 16 1 26 22 21

25,499,430.00 9,272,520.00 20,372,690.00 25,074,080.00 1,159,065.00 30,135,690.00 34,476,860.00 24,340,365.00 170,330,700.00

25,499,430.00 29,645,210.00

2025

2028 In Total

3 3 3 3 4 5 5 5

56,368,835.00

58,817,225.00

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the vehicle fleet can be replaced in a 1:1 ratio. There is no need for electric buses to travel faster to the depot to recharge the battery. Based on tenders for the purchase of 12 and 18 m electric buses with charging infrastructure (the price of the bus also included the cost of the plug-in charger) that were implemented in 2019, the net unit price for the 12 m electric bus was estimated at 2,434,100.00 PLN, while for the 18 m bus—PLN 2,628,331.00. The electric vehicle pricing model in most tenders announced by Polish cities in 2019 assumed that the bus price offered includes the cost of the vehicle and a slow-charging plug-in charger. This assumption was also adopted in the analysis of the W2 option. The unit net price of the pantograph was set at PLN 550,000.00, but it should be emphasized that it does not include costs related to the lease/purchase of land for the device, fees (e.g. building permits), or any elements of the connection infrastructure (e.g. transformer station, connecting cable lines). Table 9 shows the costs for each of the four periods analyzed, along with the type of vehicle replaced and the number of pantographs. The total value of the entire investment in the W2 option is PLN 341,548,681.00, including the cost of replacing buses for electric vehicles—it is PLN 323,904,681.00, while the cost of purchasing pantographs for driving vehicles on bus loops is PLN 17,600,000.00.

6 Summary The Act on electromobility and alternative fuels has forced Polish cities to replace their existing combustion vehicles with zero-emission buses. Such activities are dictated by concern for the quality of life of residents (especially in large cities) and environmental cleanliness. In their assumptions, zero-emission buses should emit less pollution into the atmosphere and reduce noise levels in the city center. However, the assumption of a low carbon footprint is only possible in countries, where energy supply is not generated from fossil fuels. This situation, however, does not apply to Poland. For this reason, as the basic social benefit, as it results from the use of electric vehicles in public transport, can be considered a reduction in noise level. At the same time, it should be noted that electric buses, as a product, are at an early stage of market growth. For this reason, the current market price is high and is twice the price of combustion vehicles with the highest EURO 6 standard. In addition, the operation of electric vehicles requires purchases and expansion of the accompanying infrastructure in the form of fast and slow charging devices. Recently, we have also observed an increase in electricity prices in Poland, which means that also in the process of using electric vehicles, the costs associated with their use become comparable to traditional vehicles. All these cost-creating elements mean that without additional funding from the government and European funds, it is difficult for cities to decide to buy electric buses. Meanwhile, the next

In Total

2028

2025

EURO EURO EURO EURO EURO EURO EURO EURO

2021 2023

3 3 3 3 4 5 5 5

Combustion standard

Replacement data

12 12 18 18 12 12 18 12

Length (m) 22 8 13 16 1 26 22 21

Number of vehicles 53,550,200.00 19,472,800.00 34,168,303.00 42,053,296.00 2,434,100.00 63,286,600.00 57,823,282.00 51,116,100.00 323,904,681.00

Purchasing cost of vehicle [PLN]

8

12

6 6

Number of pantographs

Table 9 Costs of replacing vehicles with electric buses along with charging infrastructure in the W2 option

17,600,000.00

4,400,000.00

6,600,000.00

3,300,000.00 3,300,000.00

Total purchase cost for each period [PLN]

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financial perspective envisages a lower level of co-financing than the one implemented in recent years (currently some programs provide co-financing at the level of 85%, in future periods there is a maximum of 55%, e.g. the Low-Emission Transport Fund). The following years will show whether Polish cities will be able to cover the high costs of purchasing zero-emission vehicles in accordance with the requirements of the Act in force.

References 1. Air quality in Europe (2018) 2018 Report, EEA Report no. 12/2018 2. Augenstein K (2015) Analysing the potential for sustainable e-mobility—the case of Germany. Environ Innov Soc Transit 14:101–115 3. Benbya H, Mckelvey B (2006) Toward a complexity theory of information systems development. Inf Technol People 19(1):12–34 4. Center For EU Transport Projects. https://www.cupt.gov.pl/en/ 5. Central Institute for Labour Protection, Safety Data Sheet (SDS) of sulfur dioxide. Available at: https://www.ciop.pl/CIOPPortalWAR/appmanager/ciop/pl?_nfpb=true&_pageLabel= P27600224401410431343241&id_czynn_chem=207 (Accessed on: 02.01.2021) 6. Courtney J, Merali Y, Paradice D, Wynn E (2008) On the study of complexity in information systems. Int J Inform Technol Syst Approach 1(1):37–48 7. Döppers F, Iwanowski S (2012) E-mobility fleet management using ant algorithms. Procedia Soc Behav Sci 54:1058–1067 8. Farell S, Lee D (2008) Carbon monoxide (in Polish). In: Plantz S (ed) Medycyna ratunkowa, p 752 9. Geels FW, Elzen B, Geels FW, Green K (eds) (2004) System innovation and the transition to sustainability. Theory, evidence and policy. Edward Elgar, Cheltenham, pp 19–31 10. Kirpes B, Danner P, Basmadjian R, de Meer H, Becker CH (2019) E-mobility systems architecture: a model-based framework for managing complexity and interoperability. Energy Inform 2(15):1–31. https://doi.org/10.1186/s42162-019-0072-4 11. Köhler J, Whitmarsh L, Nykvist B, Schilperoord M, Bergman N, Haxeltine A (2009) A transitions model for sustainable mobility. Ecol Econ 68:2985–2995 12. Laurent F, Windisch E (2011) Triggering the development of electric mobility: a review of public policies. Eur Transp Res Rev 3:221–235. https://doi.org/10.1007/s12544-011-0064-3 13. Lemberger PP, Morel M (2012) Managing complexity of information systems: the value of simplicity. Wiley, Hoboken 14. Managing the Change to e-Mobility (2012) Capgemini report. Available at https:// www.capgemini.com/wp-content/uploads/2017/07/Managing_the_Change_to_e-Mobility___ Capgemini_Automotive_Study_2012.pdf 15. Manahan S (2006) Environmental toxicology. Chemical and biochemical aspects (in Polish). Warsaw 16. Sauter-Servaes T (2011) Technikgeneseleitbilder der Elektromobilität. In: Rammler S, Weider M (eds) Das Elektroauto—Bilder für eine zukünftige Mobilität. LIT Verlag, Münster, pp 25–55 17. Schwedes O, Kettner S, Tiedtke B (2013) E-mobility in Germany: white hope for a sustainable development or Fig leaf for particular interests? Environ Sci Policy 30:72–80 18. Seńczuk W (ed) (2005) Contemporary toxicology (in Polish). Warsaw 19. Szczeklik A, Gajewski P (eds) (2009) Internal diseases (in Polish). Cracow

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20. van Deventer AP, van Steen MA, de Bruijn JA, van Twist MJW, ten Heuvelhof EF, Haynes KE (2011) Governing the transition to e-mobility: small steps towards a giant leap. Netherlands School of Public Administration, Den Haag 21. Woźniak A (2020) Elektroklapa. Electricity price rises are overshooting electromobility (in Polish). Rzeczpospolita. https://www.rp.pl/Motobiznes 27.01.2020 22. Wyrębkowski P (2001) Toxic effect of nitrogen oxides on the human body (in Polish). Plock, pp 13–18

Look into the Future of Public Transport—Selected Issues

Cybersecurity in Electric Bus Public Transport Systems Sylwester Markusik and Aleksander Bułkowski

Abstract The chapter presents threats related to broadly understood cybersecurity, encountered in modern public transport, especially in cases of increasingly introduced Intelligent Transport Systems (ITS). Experience to date has shown strong links between the technical sphere and the IT system of public transport, ensuring its protection against unwanted external interference from persons (or organizations) trying to disrupt its functioning. Of key importance are, inter alia, the existing bus network system, as well as the organization of buses on routes (timetable) and the related IT infrastructure, monitoring and protecting the system against external interference. The main goal of cybersecurity in public transport is to increase the level of cybersecurity in agglomerations through a greater availability of hardware and IT tools, forming part of intelligent transport systems, thus ensuring the safety and reliability of operation of public transport. Keywords Public transport


Cybersecurity
Electric bus
STEEP analysis

1 Introduction For many years, urban agglomerations throughout the world have been facing the challenge of ensuring the development of diverse means of transport that would meet current and future mobility needs, and thus enable economic growth and continuous improvement of the quality of life of city residents (the principle of sustainable mobility) [1]. In this context, the list of fundamental concerns includes the security of transport systems, as well as the need to reconcile the growing expectations of residents in terms of travel with those of the economy, related to the S. Markusik Silesian University of Technology, Gliwice, Poland e-mail: [email protected] A. Bułkowski (&) Higher Technical School, Katowice, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_11

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movement of people and goods. As many agglomerations are taking steps aimed at advancing the introduction of electric vehicles—not only those owned by individuals, but above all those that form part of the public transport systems—the latter must be protected against both external interference (terrorism) and a range of random events (e.g. blackouts). The widespread introduction of electric buses must be consistent with broadly understood cybersecurity related to the use of Intelligent Transport Systems (ITS) in modern public transport systems. Ensuring cybersecurity in public transport is understood as achieving a level of cyberspace security that guarantees a complete autonomy of the transport system of an urban agglomeration and its resistance to external interference. This is possible through increasing the availability of modern hard-ware and programming tools as parts of intelligent IT systems that ensure the safety and reliability of public transport systems. The operation of an electric bus fleet requires an appropriate technical infra-structure ensuring the supply of energy (charging stations), along with a coherent and secure IT system (for instance, an on-line system providing the transport operator, on a continuous basis, with information about the battery charge level of individual buses currently in operation). The operational experience to date indicates strong interconnections between technical aspects and the IT (organization-al) system of public transport in agglomerations ensuring its protection against undesirable external interference from persons (or organizations) trying to disrupt its operation. Consequently, of great importance are, among others, the spatial structure of bus, as well as tram or trolleybus lines (networks), the organization of their operation (timetables), as well as any IT links, monitoring measures and means of protecting the system against any external interference.

2 Cybersecurity and Digital Identity in Public Transport The definition of cybersecurity draws on the classic definition of security, understood as maintaining confidentiality, integrity and availability of information, transposed into the realities of cyberspace. However, a number of security issues are typical only of cyberspace (e.g. in the context of public transport management) and, for them, the existing security solutions, relating to the transmission and collection of information through ICT and network-based systems prove inadequate. It can be explained by the multiplicity of security domains that are separated not only logically, but also geographically and managed by various organizations and service providers. Their devices and networks operate according to their own distinct rules, follow their own operational practices and obey specific legal regulations. Given this fragmentation of security aspects, different for each domain, it seems necessary to implement mechanisms ensuring an effective cooperation between various organizations and service providers, especially in the area of counteracting, identifying and responding to security breach incidents [2, 3].

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Identity is generally defined as a set of attributes associated with a specific IT unit. Therefore, digital identity is information used to determine the representation of a given unit within the ICT system. Digital identity is necessary to allow the system to clearly distinguish each unit operating within it [4]. Depending on which IT system is used, digital identity can be constructed on the basis of various attributes: data pertaining to the physical aspects of the entity (e.g. company address, geographical location of the device used), data relating to the history of the entity (staff qualifications, management applications installed, device configuration), data specifying what the entity is (e.g. registration or ownership data), data assigned to the entity (e.g. role, electronic signature of Board members, number in the National Court Register), reference data (e.g. tax identification number, telephone number), etc. In the coming years, the cybersecurity market in Poland and in the European Union is expected to be affected by two basic factors [4, 5]: 1. Market developments—related to the dynamic development of digital economy, inevitably entailing increased risk arising from threats to the integrity, confidentiality and availability of information; 2. Regulations—related to legal acts, as well as EU regulations and directives that are affecting or will soon affect, directly or indirectly, the pace and directions of market development. Cyberspace is characterised by distinct security domains, which means that in different security domains the requirements relating to digital identity and the level of credibility of the presented set of attributes defining identity may be different, depending on the needs of a given security domain. Defining methods and techniques of transferring digital identities within cyberspace that will ensure not only an appropriate level of credibility of a digital identity, but also sufficient privacy protection for an individual or an organization, is among the greatest challenges of cyber-security. Cybersecurity tasks in the area of public transport focus on IT solutions that facilitate cooperation and the coordination of activities between various transport organizations striving to maintain the integrity and security of cyberspace, with particular emphasis on the digital identity of individual road and rail systems, considered as individual or collective. These assumptions include legal and regulatory aspects of cybersecurity and other indirect measures affecting security, in particular those aimed at raising awareness and informing the users of various means of transport [4]. When it comes to digital identity in public transport, reference is only made to certain methods and techniques of identification and authentication. They do not include solutions of a larger scope (ITS), such as digital identity management platforms. The implementation and effectiveness of various methods of ensuring safety in public transport shall be monitored using defined product indicators:

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respectively, the direct result (in relation to specific objectives), and long-term results (in relation to the main objective), identified in a previously conducted analysis of the need to introduce electric buses into the public transport system [2, 3]. The actual level of security in public transport, which translates the confidence and trust of citizens and entrepreneurs in ITS solutions, shall be measured by statistical indicators and ought to rise significantly compared to the current state, generating an increased public demand for public transport services, at the expense of individual transport means. In addition, the increased competitiveness and innovation of Polish IT products, including ICT security aspects, would contribute to a real improvement of cyberspace security in the area of public transport, globally, on the European level and, in particular, within the Polish cyberspace [5, 6].

3 Diagnosis of the IT Security of Public Transport Ensuring cybersecurity in public transport means, first and foremost, improving cybersecurity in urban agglomerations through increasing the availability of hardware and programming tools contained in intelligent information systems (ITS) that are conducive to the safety and reliability of public transport. The specific goal is the implementation of such technological solutions (ITS) in order to facilitate cooperation and coordination of measures addressed at different domains of cyberspace security within the ambit of public transport management, with particular emphasis on problems related to the introduction of electric buses into the public transport system [2–4]. The basis for assessing the cyber-security of a given transport system is the diagnosis of its current condition and the analysis of the digital identity of the system. The diagnosis is based on the results of an analysis of the political, legal, environmental, economic, socio-cultural and technological environment, carried out using the STEEP (or PEST) methodology. In each of the above categories, key factors, or opportunities that the study may create, are identified from the point of view of the findings within a given environment, and the potential risks that will remain if appropriate measures are not applied. The analysis of security needs of the IT network used within public transport from the point of view of the political and economic conditions may differ between Poland and other EU countries, especially in cases when diesel buses are rapidly replaced with an electrical fleet. Such analysis will include the planning of future activities aimed at creating appropriate deployment conditions. It should be tailored to local needs, and implemented taking into account the importance (attractiveness) and feasibility of these tasks. Such analysis can be based on the foresight methodology, understood as a process involving cooperation, discussions and consultations within the design environment: scientific, practical, social and political, dealing with broadly understood public transport, leading to the development of joint process planning methods aimed at determining the possibility of long-term scientific, technological and practical transition from a fleet of diesel

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buses to an electrical fleet, delivered in the form of a software package implementing these processes into IT tools based on internet technologies. Technology foresight is used in order to demarcate areas of interest specific to individual industries or organizations. The process involves a systematic (scientific) examination of the long-term perspectives of technology, science, economy and society, aimed at identifying areas of strategic research and emerging generic technologies that have the potential of brining greatest economic and social benefits over a specific period of time (e.g. electromobility) [6–8]. Technology foresight is applied to identify emerging key technologies that have the potential of generating greatest economic and social benefits. Key technologies are those most likely to have an impact on the competitiveness of a given enterprise, region or country, and on the security of the organization and society. Foresight programmes prove helpful in creating sustainable development plans for individual industries (regions). From the point of view of the sustainable development of a region, the standard of living, security, energy, sustainability, technologies applied to ensure environmental protection, economic growth and infrastructure all seem paramount. In turn, the development of all these elements is affected by an efficient and sustainable public transportation system. For transportation has been and will remain one of the most important sectors of the national economy, directly affecting the efficiency of all socio-economic areas [6, 9, 10]. Technological foresight applied to a regional transport system is a process that involves a scientific examination of the long-term development of transport systems (electromobility), aimed at identifying areas of strategic research, social needs and the deployment of new transport technologies (including those pertaining to cybersecurity) adapted to the needs of specific regions (Fig. 1). Each foresight development project carried out thus far has relied on expert knowledge imparted by individuals participating in the so-called work panels or, more broadly, (expert) panels [8]. Panel participants are individuals capable of solving a specific problem within a set timeframe (Fig. 2). In addition, under technical partnerships, panel members include expert consultants who are representatives of various industries operating within the transport sector. Opinions expressed by local governments and non-governmental organizations (in particular those specialising in environmental issues) also ought to be taken into account. Panels’ tasks are carried out through work meetings, attended either in person or remotely, via electronic mail. From the organizational point of view, the problem of cyber-security of information systems in public transport can also be solved by think-tanks which, by definition, are independent non-profit advisory bodies providing research into and analysis of public matters (e.g. public transport). A conversion of a fleet of public transport buses from conventional to electric should involve an analysis of all related needs of a given urban agglomeration, as well as the planning of future measures (including IT security) that would result in developing appropriate conditions for implementing such plans, adapted to local needs, enacted depending on their degree of importance (attractiveness) and the feasibility of specific transportation tasks. Such an analysis can be conducted from

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Analysis of the current transport situation in a city or a region in the context of electromobility development

Factor analysis using the STEEP method

Identification of areas of potential cyberspace security development

Environment analysis using the SWOT method

A list of new cybersecurity technologies

List of key factors impacting public transport security

List of key cybersecurity technologies (eg. FMEA)

Developing electromobility development variants

Developing an implementation plan for cybersecurity technologies

Multi-criteria method – selection of scenarios of conversion from a conventional to an electric bus fleet

Fig. 1 Block diagram of the creation of scenarios for the development of transport technologies in the context of cybersecurity. Source Authors’ own

the vantage point of the political and economic environment; its results may differ depending on the national or local policy implemented [7, 11]. A macroeconomic method of examining external factors, classified into separate segments, may be employed in order to identify potential problems (including those related to cybersecurity), before a critical analysis of the need to replace conventional buses (fitted with diesel or hybrid engines) with electric vehicles in Poland and in other EU Member States can be performed. It is known as STEEP and involves the assessment of factors classified in the following groups: Socio-cultural, Technological, Economic, Environmental, and Political. The STEEP analysis is not a strict research procedure (an algorithm); instead, it identifies the most important issues, as defined by a panel of experts (or a think-tank), that require in-depth assessment and critical analysis. The purpose of the STEEP analysis is to compile a list of external (structural) factors determining the current state and the future development of public transport with a fleet of electric buses. External factors pertain to the conditions for the development of a given transport-related area

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A panel of experts carrying out specific tasks pertaining to cybersecurity in public transport

Experts in the field of transportation, economy, environmental protection, social matters, etc.

Stakeholders

Science

Public transport organisations

Local governments, NGOs

Politicians

Fig. 2 Organizational structure of an expert panel. Source Authors’ own

(including cybersecurity) with a view to choosing the optimum method of conversion from a fleet of conventional to electric buses (Fig. 3). Relevance is understood as the attractiveness of a given external factor that leads to ensuring IT security of the electric bus fleet, and shall be assessed from the point of view of technical, economic, social, environmental and political aspects. External factors (inputs—Fig. 2) refer to all aspects and circumstances that affect the development of a given transport area, in the context of choosing appropriate methods for the protection of IT systems. Individual factors may be specific to a

Examination of results of the STEEP analysis, generation of a sufficient amount of outputs for model research

Brainstorming in work panels

Brainstorming in work panels

List of key factors (analysis of needs)

STEEP analysis of key factors Fig. 3 Procedure of analyzing key factors using the STEEP method. Source Authors’ own

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South Africa Canada

0,08 0,10 0,14 0,17 0,32

Brazil Singapore

0,41

European Union

0,41

China

0,63

USA

0,64

The Netherlands

1,50

Germany 0,00

1,60 0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1,80

Gross Domestic Product [%]

Fig. 4 Cybercrime, expressed as a percentage of Gross Domestic Product in selected countries in 2011. Source Authors’ own, based on Estimating the Global Cost of Cybercrime Economic impact of cybercrime [5]

given territory and affect a city, an agglomeration, a country or the entire European Union. Therefore, when identifying cybersecurity needs for an electric bus fleet, we must take into account the specificity of a given territorial area, as well as political, environmental and political aspects that can be discerned using appropriate simulation models. As we strive to identify what is needed to ensure a safe fleet conversion, STEEP analysis may be regarded as a concluding measure, as it enables the generation of an appropriate amount of output data necessary later on in the process of modelling the conversion of the bus fleet in a systemic and functional perspective (Fig. 4). Results obtained through the PEST/STEEP method and affecting the environment can be used in the analysis of strengths, weaknesses, opportunities and threats, the so-called SWOT method, which allows one to assess risks that a specific action entails, in this case: cybersecurity matters inherent in the process of replacing a fleet of diesel/hybrid buses with electrical vehicles.

4 Description of Social Factors Electrical bus technology is relatively new, not yet entirely recognized and accepted both among professionals (public transport organizers and operators) and passengers. Cyberspace security is not only a matter of concern for national and local governments and transport companies, but also, to an increasing extent, for individual users. As we analyse cyberspace security breaches, it becomes evident that some of them concern, directly or indirectly, digital identity. Table 1 presents

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Table 1 Greatest cyber threats in the European Union Threat

At present (2019) (%)

During the past 3 years (%)

Difference (%)

Cyberwar Incidents involving high-value data Hacker attacks supported by states Incidents destroying critical infrastructure (including transport infrastructure) Incidents disrupting business activities and ICT processes Emergence of organized cybercrime Advanced individual hacker attacks Incidents involving large amounts of data Cyber-criminals inside companies and organizations Incompetent employees

22 43 30 37

51 71 58 65

+29 +28 +28 +28

41

62

+21

42 43 46 36

60 55 53 38

+18 +12 +7 +2

31

29

−3

threats for various areas of social activity, outlining an increase in the number of security breaches in cyberspace over the past three years [8, 12]. Table 1 provides insights into the greatest cyber threats and the impact of new technologies on ICT security. Research shows an increase in users’ concerns regarding cyberspace security in all categories [8]. However, in categories related to digital identity in transport, the rise is more noticeable, especially when data from Poland is compared with the EU average. The largest number of users (68%) have expressed concerns with identity theft (an increase in this category was also recorded in an earlier study conducted in 2012). Given that a large part of Polish critical infrastructure remains in the hands of the state, a natural opportunity arises for cooperation between the public sector and businesses in the field of IT solutions protecting this infrastructure. Key risk factors in the process of implementing cybersecurity measures are as follows: 1. One of the reasons for the disproportion between the perception of problems and the failure to recognize risks is the low level of awareness of cyberspace security problems among Polish internet users, combined with a very low level of actual cyberspace protection in Poland. 2. Internet users across the EU are concerned with potential security incidents in cyberspace, in particular those regarding digital identity. This may translate into the need to introduce measures protecting against unwanted phenomena. Any changes in these trends result from the mass use of services provided in cyberspace, and the needs arising from security concerns can translate into a significant increase in demand for public transport solutions that would combine a low

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cost of implementation with a satisfying and verified level of security. Social expectations in the field of cybersecurity, in particular those related to security in public transport, may create a gap between the needs of transport systems users in Poland and the actual level of Internet security. This, in turn, is likely to cause a sharp decline in confidence in online transport services, caused by incidents with significant consequences, not only financial, for both public transport users and organizers. However, replacing diesel buses owned by public transport companies with an electrical fleet is also an opportunity to improve the image of such organisations among passengers and, more broadly, among all residents of a given city or region, if information about the possibilities and safety of this type of transport is provided in a simple and understandable manner. The deployment of modern vehicles, including electric buses, may contribute to a greater popularity of public transport among passengers and have a positive impact on the city’s image. This, in turn, should translate into a positive social perception of the new technology and convince residents of the merits of a further expansion of the network of electric vehicles.

5 Description of Technical and Organizational Factors Technical and organizational factors related to cybersecurity in transport systems ought to be considered from the point of view of the reliability of both system elements and processes occurring within it. In order to secure IT systems related to the electric bus fleet, attention should be paid to the following aspects of cybersecurity [13]: 1. 2. 3. 4. 5.

Reliability of mechatronic systems of buses. Reliability of batteries and power supply systems. Monitoring of energy levels and battery charging processes. Reliability of individual technical elements of the charging system. Safety and reliability of the IT system of public transport.

The above aspects should be analysed with respect to each stage of the process of ensuring IT security for the electric bus fleet, taking into account fleet operation and structure (initial, transitional, final). In relation to R&D issues related to cyber-security in the area of transport, two strategic directions of research and development have been formulated in the National Research Program [6]: • Advanced information, telecommunications and mechatronic technologies as an ITS package. • National security and defense that take into account transport and logistics. In the context of information and communication technologies, cybersecurity is referred to in the following paragraph [6]:

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Despite sizeable benefits brought by the development of technology and information society, attention should also be paid to threats arising from the use and democratization of networks, including cyberterrorism or cybercriminals’ access to sensitive, confidential or secret data.

However, the strategic direction of “advanced information, telecommunications and mechatronic technologies” touches only slightly upon the problems related to cyberspace security of collective public transport in Poland. A number of specific risks associated with technical and organizational activities related to cybersecurity can be enumerated: • The absence of cybersecurity solutions devised in Poland, which is due to the lack of investment in R&D programmes in the area of cyberspace security in public transport, may result in the country’s complete dependence on foreign solutions. • The shortage of skills and experience in the implementation of R&D projects among the staff of research units and enterprises. They lack competences enabling ITS production, which may result in devising solutions that are non-innovative, and thus uncompetitive in relation to those developed in other European countries. • The lack of experience in establishing scientific and industrial consortia and, more broadly, in R&D will prevent Polish teams from obtaining European funding for research and development in the field of cybersecurity in public transport. The following ought to be enumerated among key factors in the technological development of cybersecurity systems (ITS) in public transport: 1. It can be surmised that the innovativeness of ITS solutions in the area of ICT security, which must be based on R&D, has been insufficiently supported in Poland. 2. Research and development units, as well as local governments and transport enterprises have limited experience and competences in the development and implementation of innovative solutions in the field of cyber security in public transport. 3. European-wide research and innovation programme pertaining to cyber security in public transport ought to be initiated in order to ensure greater competitiveness; it ought to have the form of a public-private partnership; Poland’s contribution should be significant.

6 Description of Economic Factors Along with services and the use of innovative transport systems as part of innovative solutions pertaining to mobility, business and social processes, there are growing problems related to cyberspace security in the organization and

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The Netherlands

9 9 9

France Slovenia Greece

10 10 10

Austria Poland Lithuania

12

Latvia

15

Belgium

19 19 19

EU average Italy Hungary

20 20

Ireland Slovakia

21

Norway

22 22

Luxembourg Germany

26 26

Czech Republic Sweden

28

Finland

29 29

Denmark Spain Portugal

0

5

10

15

20

25

30

35

40 40

45

Percentage of enterprises reporting IT security breaches [%]

Fig. 5 IT security breaches in the European Union. Source Authors’ own, based on Eurostat Year-book 2010 [5]

economization of public transport. Figure 5 presents the estimated impact of cybercrime on Gross Domestic Product (GDP) in several of the world’s leading economies, as well as selected Member States of the European Union. Figure 5 shows that if Germany’s GDP in 2011 amounted to USD 3,745 billion, annual losses that the German economy incurred due to cybercrime have been estimated at USD 59 billion. For transport companies and public transport organizers, economic cybersecurity problems translate primarily into financial losses. According to available surveys, losses incurred as a result of a single breach of ICT security grow every year. In the short term, entrepreneurs also point out non-material losses, e.g. related to the company’s image; in the long term, though, these also translate into economic losses. Available studies indicate that ICT security problems encountered by transport enterprises prove a significant obstacle to business operations, in addition to adversely affecting the company’s image and discouraging members of the general public from using public transport. It ought to be emphasized that, compared to enterprises from other EU countries (Fig. 6), Polish transport companies rarely identify or inform the public about IT security breaches (the percentage of Polish entrepreneurs reporting cybersecurity problems is half of the EU average, and nearly three times lower compared to Germany). However, the latest statistical research conducted by Statistics Poland in the area of cybersecurity shows a change in this area and growing concerns of Polish transport entrepreneurs. Roughly a third of public transport companies (including entities providing transport companies with financial services) do not use

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Risks (the probability of certain external conditions allowing for the safe use of IT systems)

Extent (the effect of the adopted cybersecurity system)

Time (duration of the risk)

Quality of the IT system

Cost (resources needed) Fig. 6 Main elements of a cybersecurity system. Source Authors’ own

cloud services due to concerns about security breaches. The process of fleet conversion can be spread over several stages, with a gradual implementation of various measures (e.g. IT security), taking into account differences in the fleet structure, the financial standing of the enterprise or institution implementing specific measures in the conversion process, as well as the life cycle of implemented technologies. These stages are impacted by various factors associated with those identified in the STEEP analysis. The economic analysis should allow cost-benefit comparisons for different fleet conversion options, taking into account total costs and selected components (including cybersecurity). This, in turn, would be conducive to a rational economic assessment necessary for developing and recommending fleet conversion strategies for policy makers or local government authorities, public transport operators and organizers, electric vehicle manufacturers, and even relevant NGOs. An examination of the potential needs of transport companies in terms of innovative ICT security systems in transport indicates the following: • Growing needs of enterprises and transport service organizers in the area of cyberspace security, which may create demand for IT products and services guaranteeing a high and properly verified level of security. These services can be provided by both private entrepreneurs and specialized state administration units, or by public-private partnerships. • The dynamic development of information and communication techniques may incite transport organizers to invest in new solutions in order to ensure ICT security of transport services they offer.

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• A higher level of security of ICT solutions (ITS) may contribute to economic modernization and investment in innovative transport solutions. There are also specific risks associated with the above: • Absence of effective cybersecurity solutions, combined with growing concerns of public transport enterprises, may result in a decreased demand for ICT products and services (ITS) and inhibit the implementation of new solutions. • In the absence of active measures, both at the EU level and in Poland, an increase in cybercrime in transport may prove a significant barrier to Poland’s economic growth. The economic analysis should enable cost-benefit comparisons (the total cost of ownership of rolling stock, TCO) for different fleet conversion options, which would allow the assessment of these options in terms of their total cost, as well as selected cost components (IT security costs). Such assumptions make it possible to consider economic aspects when developing, and then recommending fleet conversion strategies in specific cases.

7 Description of Environmental Factors The identification of environmental factors is an important aspect of the conversion of a conventional bus fleet into an electric fleet. The obligation to take into account environmental aspects stems from UN recommendations and European Union directives [1, 11], where requirements and criteria for assessing air quality in urban agglomerations have been defined. Soot and nitrogen dioxide are air pollutants generated by combustion engines. These criteria were taken into account in the publication [14] that presents the concept of a sustainable model and the environmental impact of different types of buses (diesel, CNG, hybrid and electric). A conversion towards a fleet of electric buses fleet is expected to have a number of environmental impacts, namely: • • • • • •

Reduced amount of greenhouse gases Reduced noise level Reduced demand for fossil fuels Improved air quality Improved public health Increased environmental awareness of the general public.

Thanks to their design, electric buses generate significantly less noise compared to conventional vehicles. Charging vehicles with energy from renewable sources means, in turn, a reduction of the amount of greenhouse gases, and thus an improvement in air quality. These are locally-felt effects of the change; global effects, however, depend on the source of energy used for powering vehicles.

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Reduced pollution levels improve the quality of life and health of the inhabitants of areas where electric buses operate; they are essentially immune to cybersecurity threats.

8 Description of Political and Legal Factors The process of conversion will require appropriate measures of both Polish and EU authorities, enabling a smooth and efficient process of replacing the rolling stock with electric vehicles. Hence the determination in adopting uniform legal requirements in the European Union and in Poland, relating to all technical, organizational and environmental aspects of fleet conversion. The most important political and legal matters related to the introduction of sustainable public transport (including the cybersecurity of IT systems) are: • the implementation of means that reduce the negative impact of transport in terms of noise generation and greenhouse gas emissions, consonant with recommendations regarding the use of environmentally friendly public transport means [15, 16], • a new model of cyberspace development, emphasizing a sovereign strategic vision, principles, objectives and priorities for the country’s economic, social and spatial development in the 2050 perspective, as an instrument facilitating the deployment of low-emission public transport solutions [14, 16], • an electromobility development strategy for Poland, providing for the establishment of a transport system conducive to economic development that responds to the challenge of increasing the temporal and spatial availability of sustainable and secure transport services and planning investments to support the sector of public transport. An electromobility development strategy would serve as a tool for territorial integration and a catalyst for projects aimed at strengthening the position of public transport based on secure systems and sustainable means of transport [6, 7, 17]. Efforts expanded over more than a decade and aimed at creating an integrated system of cyberspace protection of EU Member States were crowned with the adoption of the Directive (EU) 2016/1148 of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union (NIS Directive) [4]. The following are the most important directions of measures undertaken by the European Union and EU Member States, resulting from the need to implement the NIS Directive into national legal systems over the next few years, and to join the pan-European cyberspace defence system, in particular in the area of public transport, while ensuring an effective and safe operation of the Polish cyberspace: • creating a cooperation mechanism between the Member States and the Commission, operating as an early warning system through which threats and

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incidents are reported. This mechanism is implemented through national CSIRTs (Computer Security Incident Response Team), • providing solutions that take into account dependencies between sectors within transport systems and the resilience to cyberspace attacks of ICT infrastructure of operators providing the so-called key services, including an effective structure for reporting serious security incidents in various transport systems, in particular in public transport. Supporting the standardization and interoperability that enables data transfer and the use of services regardless of their location (not only within the European Union, but also throughout the world), • eradicating the digital exclusion of citizens and of micro, small and medium-sized transport enterprises due to their limited capability to afford safe, and therefore more expensive products and services. The design of the Polish cyber-security strategy responds, to a great extent, to the challenges identified in the strategic documents of the European Union, in particular the NIS Directive. One of the essential elements necessary for the secure provision of services within the financial and transport cyberspace is the electronic identity of an organization (e-Identity). As citizens and transport companies transfer significant parts of their activities into the virtual space, new risks for the stability of the information system emerge, due to cyberspace security threat models that are utterly different from those typical of the traditional territorial organization of national public transport, conventional methods of service provision (e.g. a revolution in financial services), and social contacts (social media). One important EU legal act related to electromobility, although only indirectly concerning the cybersecurity of public transport, ought to be mentioned: Action plan on urban mobility [12]. The Action Plan identifies a number of challenges related to urban mobility. It is to support local, regional and national authorities in addressing difficulties, e.g. those brought about by the introduction of electromobility. Although responsibility for solving these problems lies primarily with local, regional and national authorities, urban electromobility decisions are also made in the context of EU policies and legislation, and may affect the overall state of the environment and the free movement of people, goods and services within the EU.

9 Risks In the context of the security of public transport systems, risk can be defined as a state in which the possibility and the probability of unintended interference within the system are unknown, while the risk itself occurs when the result attained in the future, as a result of the introduction of a specific security system, is undetermined, yet it is possible to identify future situations in which threats may occur, or to assess the probability of such individual threats actually occurring [8]. With the introduction of electric propulsion in motor vehicles—not only individual, but above all

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those that form part of the public transport system—comes the issue of the IT security of such transport systems (cybersecurity) and protection against undesirable external interference (e.g. terrorism), chaos (e.g. congestion), but also random phenomena (e.g. blackout or road accidents). Cybersecurity in public transport is understood as ensuring such a level of security in the cyberspace of transport systems of urban agglomerations that guarantees their complete reliability and autonomy, as well as resistance to attempts of unauthorized external interference. Problems related to risks that may occur due to using a given transport system can be presented in the form of the so-called cybersecurity milestones; in public transport, these include [18]: • IT system management variables. • Selection of system management models (relevant applications, e.g. Busman). • IT system management methodology. The first type of risk lies in the subjective selection of variables (e.g. according to the STEEP method, see Fig. 3) that will be considered in the modelling or the selection of an existing application, with a view to ensuring the cybersecurity of transport systems in cities (agglomerations). Inaccurate selected system management variables or their ambiguous description may result in an incorrect operation of the IT system or an increased level of risk related to its operation. However, the professionalism and experience of the staff servicing the system may lower this risk (the issue of appropriate staff selection). The risk related to choosing the wrong IT system management model lies in making wrong assumptions about the transport model (choosing an inadequate application). It results from possible irregularities that may occur already at the stage of system design and from interaction between model variables, as well as imperfections in the process of adapting the experience of users of similar management systems implemented by other partners. In this case, risk level is assumed to be average owing to the awareness of risk issues confronted by other partners operating similar public transport management systems. A contingency plan provides an overview of existing models of public transport management and better organization of the process of adapting knowledge and experience of all stakeholders operating identical or similar IT systems. The risk related to the imperfection of the adopted IT system management methodology is related to generating a better public transport control system for the agglomeration in question. Given objective social and geographical differences between cities or agglomerations, and social expectations, the risk level is considered average. It may concern in a particular way the interface of the formulated methodology of managing urban transport systems. Considering the risk of the IT system of public transport management defined in this manner, it can be assumed that risk is one of the elements of the system’s quality (Fig. 6). In order to identify risks in the public transport IT system, it is necessary to trace all risks and decide which of them can be ignored, and which need to be addressed.

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Thus, potential difficulties are avoided, as well as encountering them with limited material resources, which would increase the risk of IT system failure. It is, therefore, necessary to analyse threats and discern those that are serious and those whose impact on the system’s operational reliability is minor. The analysis of risk as a repetitive process must feature certain elements conducive to identifying risks related to a specific cyber-security task (e.g. based on foresight methodology testing). Furthermore, the likelihood of adverse events should also be anticipated in the risk analysis. Factors whose probability is negligible ought to be identified, as well as those whose occurrence is almost certain (e.g. temporary public transport chaos). In such situations, it will also be necessary to juggle with any resources available, however limited they may be. Determining the actual risk related to the reliability of IT systems used in collective public transport management presents great methodological difficulties. The methodology currently used for production management by the automotive industry, the so-called FMEA (Failure Mode and Effect Analysis) could be applied [19]. In terms of the cyber-security of the public transport information system, the objectives of FMEA may include: 1. Consistent and permanent elimination of flaws (‘weak points’) of the transport system or its management (e.g. traffic monitoring processes) through identifying the real causes of their occurrence and applying appropriate preventive measures, 2. Avoiding the occurrence of both identified and unknown flaws in new or modernized transport systems through reference to the knowledge and experience of other agglomerations, as well as the analysis of weaknesses of own IT systems. Very importantly, when using the FMEA methodology, one cannot ignore or downplay any of the possible flaws of the IT system. Many potential defects that occur had initially been underestimated. In 2011, in Fukushima (Japan), a nuclear reactor failed at a nuclear power plant near the Sea of Japan. Tsunami waves flooded the electric generator providing emergency power supply and, as a consequence, the reactor exploded after its cooling system had failed. Before this event, it had been assumed that a tsunami could not possibly impact the generator. Nevertheless, it turned out that anything is possible. A flaw identified in FMEA may or may not occur; this is why it is referred to as a ‘potential flaw’ (e.g. a terrorist attack on a subway station), as opposed to a design (real) flaw of the IT system used. The purpose of FMEA is to systematically identify individual flaws in the public transport IT control system and to either eliminate them, or to minimize their effects. It is achieved through establishing cause and effect relationships with respect to potential system flaws, taking into account risk factors. FMEA is used as a simple risk management tool. The quantitative risk analysis in FMEA refers to three variables:

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1. Probability of occurrence of a flaw (related to its cause) (P), 2. Difficulty in detecting a flaw (related to the flaw itself) (D), 3. Significance of the impact of a flaw (related to its occurrence) (S). The product of these three variables is the overall size of the risk of the analysed IT system, expressed as RPN, i.e. Risk Priority Number. It denotes an overall assessment of the level of threat related to a flaw. RPN ¼ P
D
S The S (severity) variable is an indicator of the acuteness of the impact of a given flaw on the operation of a particular IT system. The most serious and undesirable result that a flaw that may generate is expressed as 10. On the other hand, the most desirable result, i.e. when the impact of a flaw is minimal, is expressed as 1. These variables, however, are not clearly defined and require an individual, case-by-case assessment. Actually, management of automotive industry recommend the limit value of RPN = 100, as permissible risk. Transport companies using FMEA for effective risk management ought to define their own evaluation criteria that will result in the most accurate assessment, but on primary analysis level of cybersecurity IT system, we can support on the automotive industry experiences.

10

Summary

1. Public transport systems in city centres can be expected to undergo a far-reaching transformation. Public transport fleets will be largely made up of vehicles powered by alternative fuels, in particular electricity. 2. Problems and concerns related to cybersecurity in collective transport may become a factor limiting demand for public transport. If the public favour individual means of transport, the growth of national economy may be slowed down, or even inhibited. 3. Research and development in the area of cybersecurity in public transport (ITS) is, by its very nature, burdened with risk due to the impossibility of predicting its practical implications. If the subjective sense of security is insufficient, the use of individual vehicles will be favoured over public transport.

References 1. Report of the World Commission on Environment and Development: Our Common Future (1987) United Nations 2. ISO/IEC 27032 (2012) Information technology—security techniques—guidelines for cybersecurity

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3. ISO/IEC 24760-1 (2011) Information technology—security techniques—a framework for identity management—part 1: terminology and concepts 4. Directive (EU) 2016/1148 of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union (NIS Directive) 5. Estimating the global cost of cybercrime economic impact of cybercrime II (2013) Center for strategic and international studies. Mc Affee. http://www.mcafee.com/us/resources/reports/rpeconomic-impactcybercrime2.pdf 6. Markusik S, Janecki R, Karoń G, Krawiec S, Łazarz B, Sierpinski G (2013) The technical and operational aspects of the introduction of electric-powered buses to the public transport. Contemporary transportation systems. Selected theoretical and practical problems. Wydawnictwo Politechniki Śląskiej, Gliwice, pp 163–186 7. Krawiec K (2020) A concept of conventional or mixed bus fleet conversion with electric vehicles: a planning process. Int J Electr Electron Eng Telecommun 9(1):8–12 8. Markusik S, Bułkowski A et al. Ocena systemu transportowego opartego o napęd elektryczny z punktu widzenia cyberbezpieczeństwa. Prace Naukowe s. Transport. Politechnika Warszawska, vol 124, pp 65–79 9. Markusik S et al (2016) Economic conditions to introduce the battery drive to busses in the urban public transport. Transp Res Procedia 14:2630–2639 10. Markusik S et al (2016) Urban public transport with the use of electric buses—development tendencies. Transp Probl 11(4):127–137 11. WHITE PAPER (2011) Roadmap to a single european transport area—towards a competitive and resource efficient transport system. COM, 144 final version. Brussels 12. Europejski Rynek Cyberbezpieczeństwa, Potencjał Regionu Trójmorza (2018) Instytut Kościuszki and Investin, Kraków 13. Księga N (2008) Joint assistance to support project in European regions JASPERS—Sektor Transportu Publicznego. Ministerstwo Rozwoju Regionalnego, Warszawa 14. Perugia A, Moccia L, Cordeau J-F, Laporte G (2011) Designing a home-to-work bus service in a metropolitan area. Transp Res Part B 45:1710–1726 15. Action Plan on Urban Mobility (2009) The Commission of European Communities. SEC, 1212 final version. Brussels 16. Ibarra-Rojas OJ, Rios-Solis YA (2012) Synchronization of bus timetabling. Transp Res Part B 46:599–614 17. Markusik S et al (2015) The technical and operational aspects of the introduction of electric-powered buses to the public transportation system. Logistic Transp 3:41–51 18. Tarczyński W, Mojsiewicz M (2001) Zarządzanie ryzykiem. Podstawowe zagadnienia. Polskie Wydawnictwo Ekonomiczne, Warszawa 2001 19. Chuang PT (2007) Combining service blueprint and FMEA for service design. Serv Ind J 27(2):91–104

Autonomous Bus Fleet in the Context of the Conventional-to-Electric Fleet Conversion Process Krzysztof Krawiec and Marcin Jacek Kłos

Abstract According to the transport policies the increase in numbers of alternatively fueled buses is expected in public transport across the European Union. Battery electric buses are among them, however, the induction of them in cities and agglomerations is associated with many difficulties of technical, economic and operative nature. Since the fleet conversion process is complex and requires considerable organizational effort, one can take this opportunity to consider some steps towards the autonomous bus fleet. This chapter presents an overview of the bus fleet conversion process and the place of the autonomous buses in that conversion within public transport in a given area. Keywords Electric bus Autonomous bus


Electromobility deployment
Public transport


1 Introduction A planning process of converting the urban bus fleet from the present state to a 100% electric one includes a set of activities, projects and tasks to be carried out within a certain period. Such a process seems to be multidimensional and should consider regulations and legislation on European and national level, and well-prepared in economic and organizational aspects. A complex surrounding of the transition process requires the analysis of a great number of facts (some of which may be in interrelated), technical solutions, processes taking place in transport systems, legal analysis, and other phenomena occurring in this process. The dynamic technology development, as well as the uncertainty about their growth K. Krawiec (&)
M. J. Kłos Faculty of Transport and Aviation Engineering, Silesian University of Technology, Katowice, Poland e-mail: [email protected] M. J. Kłos e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_12

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potential, implies decisions to be deeply thought out. When planning such a process, it is necessary to pay attention to the complexity of the approach to the problem. The above-mentioned circumstances force the highly adaptive nature of the planning process. With this in mind, one may also think about including autonomous vehicles to the process. Due to the reasons set out below, the transition towards alternativefueled bus fleet is a good moment to consider the presence of autonomous vehicles in it, too. Placing both electric and autonomous buses into service require in-depth analysis, whose area overlaps to a large extent. Hence, it is also worthwhile to take the latter into account in the discussion about the future shape of the bus fleet.

2 Key Information on Technical and Operational Constraints of Battery Electric Buses As mentioned earlier in this book, electric buses differ in technical and operational parameters from conventional buses (Diesel and Hybrid buses). Electric vehicles have certain advantages, in particular the efficiency of electric drive, trace amounts locally emitted harmful substances and slight noise emission. On the other hand, such issues as a limited range (often not sufficient to perform day-long vehicle cycles) and the consequent necessity to buy and operate some additional technical infrastructure (such as charging stations located in depots and across the network), and power grid limitations are undoubtedly disadvantages of these vehicles [1]. Due to the limited range of electric buses, it is often necessary to strategize the process of energy supplement in the battery (or the process of battery replace and exchange). The following charging strategies may be implemented [2, 3]: • Overnight charging in the depot (mostly plug-in)—see more in chapter “Electric Buses: A Review of Selected Concepts Solutions and Challenges” • Overnight charging in the depot and recharging where necessary and possible (with the use of pantograph or inductive charging)—see more in chapter “Electric Buses: A Review of Selected Concepts Solutions and Challenges” • Opportunity charging—see more in chapters “Electric Buses: A Review of Selected Concepts Solutions and Challenges” and “Evaluation of Alternatives for Realization of Opportunity Charging at Transit Stops by Analyzing the Power Grid” • In-motion charging—see more in chapter “Electric Buses: A Review of Selected Concepts Solutions and Challenges”. The above strategies are of different degree of automation. Here the automation refers to the management of the charging process as well as the charging itself.

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3 The Conversion Process Towards a 100% Electric Bus Fleet in Transit Companies Numerous research projects were held to evaluate electric bus systems across Europe and support decision-makers with guidelines within this scope. Among others, ZeEUS [4] and ASSURED [5] projects are of the greatest importance. The project, in which particular emphasis was placed on the conversion process was named ‘Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet’ (acronym: PLATON, [6]). Let us set this out shortly. The goal of the PLATON’s conversion process is to achieve a state in which the entire bus fleet is electric-powered. This state can be reached in stages or, seldom, at once. A general scheme of the bus fleet conversion process is presented in Fig. 1. The planning process involves many activities which are divided into the following categories which are assigned colors in the scheme [7]: • • • • •

Electric bus deployment from the perspective of policymakers (yellow) Economic calculations (purple) Energy consumption models (green) Step-by-step conversion towards a 100% electric bus fleet (red) Scheduling and optimization (blue).

Step-by-step conversion towards a 100% battery electric bus fleet refers to the adjustment of vehicles to the present timetable (to carry out the analysis of the feasibility of a subsequent vehicle cycle to be operated with the use of electric buses). For the already defined set of vehicle cycles to be operated, one can perform one of the energy consumption models to get the discharging characteristics. These models may also be used standalone, without aiming to achieve a fully electric bus fleet. More on buses’ energy consumption calculations can be found in Chap. 6. Another option is to create a new bus schedule with a new vehicle cycle scheme. The advantage of the latter is the ability to operate the vehicle cycle right away. On the other hand, this may entail the raise of costs or deterioration of the transport offer for passengers. One may create such a schedule—inter alia—with the use of optimization models—these are available in [6]. In the context of bus fleet conversion towards a 100% electric bus fleet, it is impossible not to mention the policy-makers. They may influence decisions based on arbitral premises. Nevertheless, the constraints resulting from nonideal battery technology have to be fulfilled also in this case. The last element is to calculate the total cost of ownership of electric buses in a transit company. These economic calculations may either static or dynamic. The dynamics of the TCO is understood as modelling costs changing over time, but also variant consideration of investment items. The following components have been developed to tackle the above-mentioned issues:

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1. DataProc—to process bus-related and battery-related data, and those from the transit network. 2. CellParameters—determining the values of the circuit elements in the traction battery charge status model. 3. CollectApp—to collect data on potential routes to be served by electric buses. 4. SyntheticalTrips—to generate speed profiles for a hypothetical electric bus route in the transit network. 5. BusVehicleSimulation—to calculate electricity demand to perform a vehicle cycle on a given route. 6. ECBus—to determine the energy consumption of electric buses for specific conditions. 7. TCOModel—to investigate all costs of the total cost of ownership of electric buses over their entire life cycle. 8. OptimSched—to solve optimization problems related to the fleet conversion problem. 9. VisualGrids—to on-line search for power grid facilities. 10. NMEA simulator—to calculate the energy balance for the investigated operational models (charging methods). 11. ReportGenerator for procurement decision support—to generate executive reports e.g. on the TCO projection, bus route electrification priority, and recommended battery configuration. One may move among these components—inter alia—via the following website [7]. The use of modern solutions in the field of artificial intelligence (such as neural networks, machine learning or deep learning) could potentially improve the present design of the bus fleet conversion process. Additionally, adding artificial intelligence to the application may enable better identification of ready-made system solutions (e.g., charging, choice of charging facility location) related to conversion for decision-makers. The general application of artificial intelligence may involve the whole process or just a part of it. Input values filled in by the users and the generated results of the fleet conversion process would be stored in the memory and introduced into the patterns for teaching the neural network. The diversity of users in terms of requirements, needs and possibilities of the target structure of the bus fleet will shape the operation of the neural network. A pattern for the developed neural network could be existing public transport networks where electric buses have been introduced—for example, in the city of Shenzhen (China) [8].

4 Autonomous Vehicles in Public Transport There is an intensive development of innovative technologies related to autonomous vehicles, which are systematically introduced into operation. Therefore, autonomous electric buses (AEBs) may and should be considered when planning the conversion of the bus fleet. Here we briefly describe the possibilities of using

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such buses and indicate the potential benefits. We also describe some potential problems posed by the autonomous fleet of electric vehicles. An AEB combines both autonomous driving efficiency and eco-friendly powertrain. This kind of bus could render the public transport sector more valuable and sustainable. The usage of the AEBs fleet may better fulfil the passengers’ needs. As the process of conversion could take place in different time stages, new technologies, like AEB, need to be considered. The decision to convert vehicles should require decision-makers to think ahead, considering the potential benefits and risks of introducing autonomous electric buses. There are two definitions for the autonomous vehicle in the European Union [9]. The first is the automated vehicle, which is described as a vehicle equipped with technology: a driver could transfer part of his tasks to the on-board system. Another definition is called as the autonomous vehicle and it is for the fully automated vehicle: this type can guide itself without any human conduction [10]. The autonomous bus is a vehicle that can sense the surrounding environment and communicate with other vehicles and infrastructure. Development of autonomous vehicles requires many technologies such as GPS, odometer, radars, sensors, laser lights, computer vision and high precision maps which must be constantly updated [8]. The vast majority of the autonomous buses are also supported by artificial intelligence. Collected data for rides allows optimizing existing algorithms. Introducing AEBs into the city needs to be considered in connection to the potential advantages [11–13]: • Limitation of drivers’ fatigue • Human error risk reduction (and the resulting improvement in road safety indicators) • Reduce in the emissions of harmful substances (including CO2) due to the improved driving style • Cost savings on wages and fuel (or electricity) consumption. Indirectly, the deployment of autonomous vehicles may also increase the number of passengers due to the improved image of public transport. AEBs also have disadvantages, which need to be considered. Many of these are related to technological limitations, however, they can also be associated, e.g., with sociological issues. Many of technology constraints will cease to exist in the future. The potential disadvantages of the AEBs are as follows: • The need for adjustment of legal laws to the operation of AEBs • Fear of driverless buses by passengers • Unexplored behavior of the buses in the case of very bad weather conditions (e.g., snow, heavy fog) • Difficult to estimate reaction to the signals given by traffic police (in an emergency or when traffic lights are broken down) • Need to ensure cybersecurity safety (e.g., in the case of a cyberattack) • Lack of the transit company’s representative in the bus (e.g., in the case of vandalism).

Autonomous Bus Fleet in the Context … Table 1 Autonomous driving levels according to the SAE classification

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Level

Name

0 1 2 3 4 5

No driving automation Driver assistance Partial driving automation Conditional driving automation High driving automation Full driving automation

It is expected that at least some of the technological constraints will cease shortly. However, before we present the possible induction of AEBs in the bus fleet conversion process, let us remind the five levels of autonomous driving according to the Society of Automobile Engineers (SAE). The latter are presented in Table 1. (For more details on these levels we refer the reader to Chapter Electromobility in Smart Cities). Level zero is the situation when a driver performs the entire dynamic driving task even when the vehicle is enhanced by an active safety system. In the next level vehicle is also controlled by the driver but in a specific driving conditions, support may be included in the vehicle design. For the second level, the system is used for both driving and speed control—a driver is only responsible for monitoring the surroundings. In next level is the possible to take over the vehicle control of all the aspects of driving, but the driver must all the time be ready for taking over the control back. As for the fourth level, the performed technology could also take control of all aspects of driving but without any expectation, that driver will respond to the request from the system for taking control. The ultimate goal is to integrate AEBs into public transport systems with the SAE level 5, which is the Full Driving Automation. It means that AEBs could drive in every traffic situation without human conduction [14, 15]. Currently, prototype AEBs are being introduced in the world: the operation of the first full size autonomous electric bus Volvo 7900 Electric may be an example, which is currently tested in Singapore. Scania also works on their autonomous bus, which is named Citywide Electric. Operator Nobina wants to make trial of autonomous buses on regular routes in the Stockholm area in 2020. The buses will be having a driver to monitor operations. There are also other examples from Vienna (Austria), Jaworzno and Gdańsk (Poland) [16].

5 Autonomous Buses in the Context of Bus Fleet Conversion Process Let us discuss the issue of the inclusion of AEBs into the bus fleet conversion process from the same perspectives as it is shown in Fig. 1.

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Undoubtedly the decision to take advantage of AEBs is of a political nature both at National and at the local level. To make the deployment of AEBs in public transport possible, appropriate legislation is required. Its adoption (or not) depends on the country’s strategy in this regard, becoming a strictly political issue. Nevertheless, even assuming that regulations on AEBs operation are issued on the National level, a discussion is expected in this matter among local communities (including politicians). Keeping in mind the above-mentioned social aspects of autonomous vehicle operation (namely the anxiety over new technology in the society) one would expect that political-driven arguments may be raised. Such public debates may delay the decision on AEBs operation in a local public transport system. The inclusion of AEBs in the already-discussed conversion process implies changes in the cost structure. It is difficult to talk about the details of such changes at this stage (at least in the initial period of time, drivers are to be on-board to avoid the embarrassment of passengers). It also seems that launching AEBs to operate regular bus lines is so far-distant that the structure of expenses may change regardless of the AEBs themselves. Hence it is quite a challenge to predict the changes in the Total Cost of Ownership resulting from the deployment of AEBs. As indicated in Sect. 4 of this chapter, it is assumed that there are predictions that autonomous vehicles will be consuming less energy (comparing to the conventional vehicles, driven by a human). To the best of the authors’ knowledge, no detailed models are developed which would include the influence of autonomous drive on the energy consumption of the bus. This is certainly an interesting field for further studies. Another aspect related to energy consumption is the issue of charging. The automation of the bus movement may have a positive impact on the charging procedure. This impact may concern: • The increased safety due to the absence of a human in the charging process • More accurate or faster positioning of the bus under the charging facility • Better communication between the bus battery management system and the charging facility • Optimization of the charging process in terms of effectiveness. Unlike the energy consumption model perspective, the induction of AEBs to operation in public transport has no major impact on the bus fleet electrification process. This is due to the fact that there is no difference from this perspective, whether a battery electric bus is driven by a human or only by a computer. When electrifying consecutive transit lines (and, as a consequence, vehicle cycles), we rather focus on the ability of the line to be operated by battery-powered vehicles. In Fig. 2, we present four stages of the autonomous bus fleet deployment. Due to the reasons set out above, it will be a hurdle to put them into everyday operation. Hence, we propose to familiarize the society with driverless buses in limited-access areas, such as fair or exhibitions. In the next steps we indicate e.g., airside airport buses and bus-lane-only transit lines as suitable to be operated by this type of vehicles. Regular bus routes, in the opinion of the authors, should be handled last.

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Stage I Autonomous buses serve special areas (e.g., fair, exhibitions, industry screenings)

Stage II Autonomous buses operate areas of limited accessibility (e.g., airside airport buses, inner-factory transport)

Stage III Autonomous buses operate transit lines with seperated bus lanes (e.g., bus rapid transit)

Stage IV Autonomous buses operate regular transit lines (e.g., based on the order of electrification) Fig. 2 Stages of autonomous bus deployment

In the optimization and scheduling field, the goals of procedure may change (comparing to the present situation). Given the dynamic development of this technology, these goals may be set out only indicative. However, as far as battery technology is not developed enough to carry out the day-long vehicle cycle, all of the current optimization and scheduling problems remain valid. These include searching for the: • Optimal locations of charging facilities • Rational charging strategy • Scheduling of the charging process (having regard to the power grid and capacity limitations). Needless to say that regardless of whether we induct an autonomous bus fleet or regular-electric one, vehicle routing and scheduling problems remain.

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6 Summary It is possible to merge the discussion on the autonomous buses with aiming to achieve as great share of electric vehicles as possible in the fleets of public transport operators. However, due to the complexity of the bus fleet conversion process as well as a high degree of uncertainty regarding technology development, it is not possible to be precise in the predictions. The issue of the inclusion of AEBs into the bus fleet conversion process depends on the given perspective (more details in Sect. 5). We stressed out the political nature of the decision to allow autonomous vehicle moving around (both at National and local level). We also indicated the variations in cost structure when operating autonomous vehicle (primarily involving driver-related costs). It is difficult to assess the changes in energy consumption between conventional battery electric buses and AEBs in real-traffic as no data is available—more research on this topic is needed. Further, we proposed a basic order for the induction of autonomous vehicles in public transport. According to the latter, we should start with demonstrations in limited-access areas, heading to the ultimate operation of regular bus routes. We also indicated the related optimization problems related to the issue discussed. Acknowledgements The present research has been financed from the means of the National Centre for Research and Development as a part of the international project within the scope of ERANET EMEurope program ‘Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet’.

References 1. Krawiec K (2020) A concept of conventional or mixed bus fleet conversion with electric vehicles: a planning process. Int J Electr Electron Eng Telecommun 9(1):8–12 2. Leou R-C, Hung J-J (2017) Optimal charging schedule planning and economic analysis for electric bus charging stations. Energies, 438 3. Bagherinezhad A, Palomino AD, Li B, Parvania M (2020) Spatio-temporal electric bus charging optimization with transit network constraints. IEEE Trans Ind Appl, 5741–5749 4. https://zeeus.eu/ 5. https://assured-project.eu/ 6. http://platon.publictransport.info/

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7. http://projektplaton.polsl.pl/#/planning/home 8. Huang P, Li P (2019) Politics of urban energy transitions: new energy vehicle (NEV) development in Shenzhen, China. Environ Politics, 1–22 9. European Union. Briefing: automated vehicles in the EU. Available at http://www.europarl. europa.eu/RegData/etudes/BRIE/2016/573902/EPRS_BRI(2016)573902_EN.pdf 10. Ilková V, Ilka A (2017) Legal aspects of autonomous vehicles—an overview. In: 2017 21st international conference on process control (PC), 2017 Jun 6, IEEE, pp 428–433 11. Herrenkind B, Brendel AB, Nastjuk I, Greve M, Kolbe LM (2019) Investigating end-user acceptance of autonomous electric buses to accelerate diffusion. Transport Res D: Tr Environ 74:255–276 12. Sinner M, Brawand S, Weidmann U (2018) Networking planning with autonomous buses 13. Zhang W, Jenelius E, Badia H (2019) Efficiency of semi-autonomous and fully autonomous bus services in trunk-and-branches networks. J Adv Transport 14. Nordelöf A, Mia R, Tivander J (2019) Life cycle assessment of city buses pow-ered by electricity, hydrogenated vegetable oil or diesel. Transport Res D: Tr Environ 75:211–222 15. Montes H, Salinas C, Fernández R, Armada M (2017) An experimental platform for autonomous bus development. Appl Sci 7(11):1131 16. Soe RM (2020) Mobility in smart cities: will automated vehicles take it over? In: Smart governance for cities: perspectives and experiences, pp 189–216

How to Connect Hyperloop Technology with the Smart City Transportation Concept Arun Kumar Yadav and Janusz Szpytko

Abstract The paper is trying to find the answer for the following question: how to connect Hyperloop technology with the green-based transportation concept, as well as discussing the implementation of Hyperloop technology concept to improve communication for smart cities. The paper discusses also the possibilities of developing smart cities using the potential of innovative electrical-based transport technologies with use the vacuum transport system for carrier passengers, as well any types of loads in the carriage in pipes with reduced air pressure.




Keywords Hyperloop Smart cities Low carbon alternative


Transport telematics
Electromobility


1 Introduction The concept of Hyperloop is first coined by Elon Musk in his Hyperloop Alpha in 2013 on a white paper and proposed the idea. Hyperloop is a new mode of transportation that is represented in this paper. Using the concept of magnetic levitation, if the levitated pods are achieved to propelled at high speeds transportation system can be highly revolutionized at a cheaper rate of transport. Despite the fact, the technology is still in the gap and the installation cost is so high, a lot of countries are showing interest in this technology. This new way of transportation can be propelled at a maximum speed of 760 miles per hour using magnetically levitated pods inside a tube. Hyperloop Alpha is the hand-written draft proposal given by Elon Musk. The Hyperloop by 2021 has become a strong factor for many countries and companies to invest their resources. A literature survey has identified A. K. Yadav (&)
J. Szpytko AGH University of Science and Technology, Av. a. Mickiewicza 30, PL 30-059 Krakow, Poland e-mail: [email protected] J. Szpytko e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 K. Krawiec et al. (eds.), Electric Mobility in Public Transport—Driving Towards Cleaner Air, Lecture Notes in Intelligent Transportation and Infrastructure, https://doi.org/10.1007/978-3-030-67431-1_13

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a huge number of countries and companies working on research and feasibility tests. Being one of the best alternative and faster than the current air travel and rail travel, it is appreciated to connect all the major cities to support and help in combining all different markets from labor to commercial, and also to utilize the national airport capacity by integrating the services to airports. Due to raise in huge transits and economic growth, the transportation has become a huge liability for many people, companies, and countries, and being a minimal energy option with a low carbon alternative to the existing mode of transportation. Hyperloop could help in controlling the peak demand for travel. Despite the positives in hyperloop, a few issues on the safety of passengers and economics and comfort of passengers would need further demonstrations in real-time and need to overcome in time. The biggest challenge is the land as it is not always flat and due to this topography of the world, a major challenge in implementing the magnetically levitated hyperloop is put under pressure. Hyperloop’s success mostly depends on the countries ready to offer economic support and political support. In this chapter, there is a try to find the answer for the following question: how to connect hyperloop technology with the smart city transportation concept, as well as discussing the implementation of hyperloop technology concept to improve communication for smart cities. The chapter also discusses the possibilities of developing smart cities using the potential of innovative electric-based transport technologies with use the vacuum transport system for carrier passengers, as well any types of loads in carriage in pipes with reduced air pressure. Can the hyperloop concept inspire solutions in intelligent urban transport in smart cities?

2 Hyperloop History and Solution Hyperloop is one of the highly popular terms in the twenty-first century. It is one of the fastest transportation systems proposed for long distance public transport. Elon Musk from Tesla Motors and Space Exploration Technologies, and a few talented engineers along with Elon Musk proposed the first pitching paper on hyperloop [1]. Surrounded by a long pipe or tube, when a capsule or compartment or train consisting certain load where the movement and holding position is achieved by the principle of electromagnetic induction, particularly magnetic levitation. The tube basically consists of a low-pressure difference with the capsule, which will make the capsule to travel at both high as well low speeds and this system uses a compressor and linear induction motor for driving. For safe transport of the pod or capsule, the pipe or tube would allow a low pressure, and surrounding the pod will be a cushion of air that allows the pod to travel. Building pressure because of air displacement will be an issue, so to prevent this and also keeping the pod holding its position Elon Musk has given specific recommendations in design by placing an air compressor at the front side of the pod [1–3]. This system will allow the air to move from front of the pod to the tail end.

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Representing two sides of a coin, the transportation system and civilization have grown. Our self-research on new ways of transportation at a cheaper cost and can travel faster is still the biggest question to many researchers and scientists in the twenty-first century. Making a commercial system in advanced transportation is one of the failures for many researchers. Our four modes of transportation were rail, road, air, and water. All these were successful and yet our necessity has outgrown our needs and a need for a new transport method became necessary. Elon Musk, the Founder, CEO of Tesla Inc. presented an open-source paper called An Alpha Vision, which gave the description of the fifth mode of transportation. The performance would be a huge parameter asset and is better than the air and high-speed transport systems; the idea is still in the conceptual stage. Safety, reduction in transport charges and travel time are a few of the biggest advantages other than performance [1, 4]. A vacuum train or vactrain of a hyperloop has a 3-part system: magnetic levitation, vacuum-based transport and linear motor propulsion. The major idea of An Alpha Vision is to operate between major cities of the United States like San Francisco to Los Angeles. The expected maximum speed of hyperloop is around 760 miles per hour (1 mile = 1.60934 km) and this is a revolutionary transportation technology build for the future [1]. A newly proposed solution that can revolutionize with faster, safer, on-demand and direct origin to destination is hyperloop. A low capacity of 221-40 people travelling on a pod which travels at a speed of 760 miles per hour using magnetic levitation and pods propulsion, and also being a low friction environment, which works at extremely low pressure i.e. at 100 N per m2 and all the parameters held inside a tube system are the features of the model [1, 2, 5, 6]. Ever since Elon Musk’s paper on Hyperloop Alpha got proposed between Los Angeles and San Francisco considering an alternative for fast transport, the proposed idea is so adopted by speed rail development with high ambition. Later many companies across the world accepted the concept and the technology and adapted their research for achieving various goals and one of the highly successful developments is Hyperloop One and working with a key objective to commercialize the technology by 2021 [1, 7]. Many countries like USA, France, India, Saudi Arabia, Russia, China, the UK and Sweden have been working on feasibility studies. Virgin Hyperloop has been working on implementing at various numbers of places and routes even within the UK. Providing high-speed travel on terrestrial grounds with very small pricing and by consumption at low energy can be a rival factor for normal modes of transportation we prefer. Rob Lloyd, CEO of Virgin Hyperloop one, stated we will move people and goods at very high speeds, with very little energy, no noise pollution and a very small footprint, all of which gives us something that is ultimately faster, safer, cheaper and greener than other current transportation alternatives. Is it challenging the transport market as how uber challenged the local taxis or people simply call it as Elon Musk effect [1, 8, 9]. The key question for modern large and crowded cities is how connect Hyperloop technology with the smart city transportation concept, and how to implement the hyperloop technology concept to improve communication for smart cities.

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Working of the Hyperloop System

Hyperloop works on the principle of Magnetic Levitation. When a series of magnets on the track is used for guidance a vehicle suspended on the top can be propelled, this is the working principle of magnetic levitation [2, 5, 8]. The propulsion of the vehicle is achieved using Linear Induction Motor. Important elements of hyperloop structure are tube, capsule, compressor and propulsion. The tube is a steel containment; theoretically, the purpose of the tube is to create a vacuum inside it. The vacuum will remove the air resistance in the moving direction of the train or pod, but creating a vacuum for such long-distance is the biggest challenge. For this problem, the capsule or pod will have a low-pressure air unit that offers very negligible or little resistance. A capsule is a compartment unit with an aerodynamic shape, which will be propelled at a high momentum thus gaining high speed and the levitation is gained by high-pressure air cushion. The two different types of capsules are passenger plus vehicle version and passenger carrier version. Made out of polymers, the capsules operate using a reservoir of compressed air and aerodynamic lift supported by air bearings. The compressor is added at the nose of the capsule to avoid Kantrowitz limit, which defines the problem of low-pressure air. As the capsule starts moving through levitation the air gets compressed leading to propulsion and also build resistance of pressure surrounding it and eventually stops the capsule to move. This is called Kantrowitz limit. Adding a compressor at the nose of the capsule helps for counteracting with this phenomenon and leads to achieving high speeds in a short time. Placing the compressed fan will help in inhaling the accumulated air from the nose of the capsule and exhaling near the air bearing. This effect helps in removing the resistance and no further effect of Kantrowitz limit is observed. The supply of air to the bearings also helps in providing the balance and weight to the capsule. Compressor fan also has one huger role to play to overcome suspension friction. Suspension friction is a major problem for hyperloop. The only possible option is to remove all the surface contact and levitate the train in air. To achieve it, air bearings were installed over the surface to exhale the air taken from the nose compressor fan. Suspension system through air bearing gives better stability and very low drag at a very reasonable cost. A consistent air bearing system is an amazing source for better safety as well as reliability. Propulsion systems are developed all along the length at various locations. Linear accelerators were used at locations to accelerate the capsules and the rotors that are located in the capsules help to transfer the momentum to the pod or capsules through the linear accelerators. Important elements of the hyperloop structure are tube, capsule, compressor, drive. Can the distinguished elements of the structure be used in solutions to urban transport that comes to the environment? The entire hyperloop concept works on the phenomenon of magnetic levitation. As we know that the passenger pad travels through low-pressure tube. In the hyperloop system, an air compressor fan is fitted on the front side of the pod which sucks the air. It transfers high-pressure air front side to the rear side of the capsule

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(pod) and it propels the pod. It creates the air cushion around the pod, so that the pod is suspended in air within the tube. On the basis of magnetic levitation principle, the pod will be propelled by the linear induction motor. By the linear induction motor, the capsule sends from one place to another place to a subsonic velocity that is slower than the speed of sound. The air between the capsule acts as a cushion to prevent two capsules from colliding within the tube [1, 2, 10]. An aluminium sheet works as a rotor for the engine located on the bottom. The stator bend produces a linearly moving magnetic field acting on the bottom surface of the capsule. In the transportation system, the main driver is the Aluminum sheets, which located in the area has vortex currents induced in it, in this way creating an opposite magnetic field. The two different magnetic fields force back each other and produce the motion of the capsule. It would not be challenging to accelerate the capsule to reach the velocity of 760 miles per hour and decelerate it for the better and safer braking system [1, 2, 6, 10]. The acceleration will reboot in a periodically every 110 km (roughly). There should have been a minimum interval time of 30 s between two side by side capsule. To get the maximum velocity, the friction should be very lower between capsule and tube. That is why air cushion mode around the capsule is the right method to prevent the friction between capsule and tube. In the front part of the capsule, an air compressor receives a counter flow of air to increases the incoming air pressure by 20 times. It feeds in specific proportions through a system of different parts of the capsule surface. As so far, the capsule must move through the tube by air currents without touching the tube wall. When a solid body moves in the air, the air cushion pushes back from the front. The strength of resistance to movement increases with the increasing speed of the body. For reducing air resistance, it is proposed to maintain a pressure of 100 Pa (1/1000 atmospheric pressure) in the tube. The vacuum pump system maintains the required reduced pressure. The energy exhausted by using the air cushion is 21 MW. Solar panels are located on the outer surface of the tube to produce the power for the whole system. The cells could a massive amount of energy, almost 57 MW. To get maximum speed from any high-speed transportation system, the path should be a straight line. Therefore, the hyperloop tube should be a straight line as possible as we can. Due to the curvilinear nature of the route in urban areas of Los Angeles and San Francisco, the capsule reduces the speed on these sections of the road. In 2016, the work already had begun; it is planned to finish the project in 2020. Elon Musk estimates the costs of this project at $6 billion for only passenger capsule [1]. Two companies, Hyperloop One and Hyperloop Transportation Technologies (HTT), began to work for this project. They have involved in solving technical problems associated with the new technology. They have built an experimental testing grounds and have started for the reality of specific projects in the US and other countries. The NTT is working to the realization of this project in California. It also has initial agreements with other countries like Central Europe, United Arab Emirates (UAE), and From Asia (China and India) for the development of hyperloop passengers project. In 2016, Hyperloop One organized a worldwide competition to select places where the

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first hyperloop project will begin. It made awareness among individual citizens, universities, firms and government organizations from different countries who took part in this competition. The proposals should outline the need to use new ultra-high-speed transport technology to move goods and people in this particular place. As a result, the company received 2600 offers from individuals and organizations for five months. The company is establishing contact with Russia to construct of an experimental hyperloop cargo line 65 km long from Russian seaport Zarubino. Also, China has taken steps to construct this new technology located in Guizhou province [5, 11]. Another question is how to design loading stations as an interface between hyperloop and intelligent urban transport? Can freight transport using the hyperloop system be associated with an urban pipe transport system (for example pneumatic type) with individual customers in cities? What will the cargo and passenger distribution system look like using public transport using intelligent type tools?

3 Operation Potential of the Hyperloop Solution 3.1

Travel Time

Elon Musk claimed hyperloop could operate at top speeds of 760 miles per hour, however, to account for the required gradual acceleration and deceleration speeds would average 600 miles per hour [5]. At these projected speeds, it is suggested that hyperloop would be 2–3 times faster than high-speed rail and 10–15 times faster than traditional rail [11]. It could also act as a faster alternative to short-haul flights (c.250–500 miles per hour) [10]. The hyperloop concept is still within the testing phase and has only achieved a top speed of 240 miles per hour [4]. Assuming predicted speeds of 760 miles per hour can be achieved, evidence suggests that the station to station travel time of hyperloop would be faster than competing modes (i.e. rail and short-haul flights). Table 1 outlines the station to station travel times for both Musk’s original route and the London to Edinburgh route proposed by Virgin Hyperloop One—both of which demonstrate the speed advantage of hyperloop.

Table 1 Comparison chart of station wise travel times with different modes of transport with Hyperloop [1] Route

Company

Distance (in miles)

Hyperloop (min)

High-speed rail

Air

Los Angeles to San Francisco London to Edinburgh

Space X (Elon Musk) Virgin Hyperloop One

382

35

2 h 35 min

414

50

3 h 38 min

1h 20 min 1h 10 min

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The boarding process for hyperloop is anticipated to be relatively smooth due to the regular departure of hyperloop pods which would result in a steady and fast flow of passengers [6]. However, there could be occasions where more passengers arrive than the available pod capacity, resulting in queues forming and boarding times increasing. Trains have relatively efficient boarding times due to the presence of several carriages where passengers can enter. Those travelling by air experience longer boarding times due to passengers squeezing into few access points which creates a bottleneck in the process. Taxiing time is applicable only to flights and relates to the time taken to transfer the aeroplane between the passenger terminal and the runway. Recent figures indicate that on average US domestic flights take 15.8 min to taxi out and 7.1 min to taxi in [12]. The analysis above indicates that the speed at which a transportation system operates is not the only factor to affect the overall journey time and there are many other components to consider.

3.2

Capacity

Musk’s proposal suggested average capacity would be 840 passengers per hour with pods holding 28 people departing every 2 min. During rush hour this capacity could be increased to 3360 passengers with pods departing as frequently as every 30 s [8]. Similarly, hyperloop transport technologies suggested a capacity of 3600 an hour based on pods holding 40 people departing every 40 s [13]. The viability of pods departing every 30–40 s is questioned on the grounds of safety. Hyperloop pods travelling up to 760 miles per hour will have a maximum deceleration of 0.5 meters per second, which is equivalent to 10.9 miles per hour per second. At that rate of braking, it will take pods 68.4 s to come to a full stop. Safe vehicle operation dictates the minimum headway between vehicles should be equal to the distance required for the vehicle to stop safely. Therefore, the minimum separation of pods is likely closer to 80 s which would safely allow 45 departures per hour [8]. Based on this interval the maximum hourly capacity on hyperloop would be between 1260 (28 people per pod) and 1800 (40 people per pod). For the Los Angeles and San Francisco route, hyperloop capacity would be higher than air capacity which is currently 400 passengers per hour; however, it is significantly lower than the proposed California high-speed rail which would convey up to 12,000 per hour [14]. Capacity could be increased through the use of multiple tubes, but this would have implications for the infrastructure needed to support this and would also significantly increase the cost. Assuming it is necessary to move large volumes of people between Los Angeles and San Francisco, Hyperloop may not be the most suitable solution in this location, but could still be viable in other places such as the UK. Table 2 demonstrates daily passenger capacities for different transport modes for the London to Edinburgh route. The maximum daily capacity for flights is 7650 which is currently constrained until the 3rd runway is built at Heathrow [15], and is therefore currently unable to compete with Hyperloop.

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Table 2 Passenger capacity analysis for different modes of transportation [1, 2] Transport mode

Capacity per unit

Departure times

All

Average of 150 589

N/A 5:30 AM to 10 PM 5:30 AM to 10 PM 5:30 AM to 10 PM

High speed rail Hyperloop small capsule Hyperloop big capsule

28 40

Services per day

Services per hour

Departure frequency

Capacity per day

51

N/A

N/A

27

2

568

32

Every 30 min Every 113 s

15,904

398

22

Every 163 s

16,520

7650 15,903

However, rail delivers an approximate daily capacity of 15,903 which would pose viable competition to hyperloop. For hyperloop to match this capacity, pods would have to depart as regularly as every 113 s (assuming a pod capacity of 28). This comfortably aligns with the safety parameters discussed above, however, the practicalities of dealing with up to 32 pods departing per hour could be a challenge and present a bottle-neck for capacity. For example, the need to maintain a vacuum requires the use of airlocks at stations. When pods arrive at the station, the airlock will have to close, pressurize and open again. Then the pod has to clear the airlock before the next pod arrives. The speed at which this occurs would determine the viable distance between pods [16]. This issue only applies to a situation where the entire vehicle is supposed to be moved from the low-pressure side to the external environment. The process would be much simpler if a similar concept to air bridges at airports was adopted and indeed hyperloop transport technologies have included a similar proposal to this in one of their patents. It is important to note that passenger demand would not be consistent throughout the day and there would be significant peaks in demand during the rush hour. However, based on current train services, whilst there are alterations to the number of stopping points during peak times, there is not an increase in the number of trains, and as such there would still be a maximum of 1178 passengers arriving per hour on the rail for the London to Edinburgh route. The figures in Table 2 represent the numerical figures or numerical values. So this words actually represent the numerical values presented in Table 2.

3.3

Construction on the Ground or Below Ground

In his proposal, Musk suggested that the cost of land acquisition could be reduced through the construction of hyperloop on pylons and through the use of existing road and rail routes [9]. Musk implied that landlords would be prepared to sell overhead access and pylon rights for lower prices than would be required for

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ground-level construction of the proposed high-speed rail development. However, it is also argued that the high-speed rail development could feasibly be built on pylons and that developers have not chosen to pursue this option which suggests that perhaps the cost savings were not sufficient to overcome the additional complexities and costs of elevated construction. Due to the speeds at which hyperloop will operate, there is a requirement to restrict lateral forces below 0.1 g to ensure acceptable levels of passenger comfort [17]. To achieve this, hyperloop tubes need to be relatively straight. Motorways in the USA are fairly straight which make them ideal land for building this concept. However, in countries such as the UK, hilly topography, dense urban spaces; high land values; and numerous protected landscapes will be key challenges in the implementation of hyperloop [17]. It is likely that hyperloop would have to be built underground, which would affect capital costs and make maintenance and emergency evacuation more difficult. However, the required diameter of the tubes would be much smaller for hyperloop than those for high-speed rail and therefore costs could be reduced. Also, tunneling could greatly speed up the planning process and reduce issues associated with acquiring rights of way. Regardless of the potential merits associated with tunneling, there may still be significant challenges in tunneling in parts of the UK due to local geological conditions [18].

4 Selected Technical Issues of Hyperloop We discuss some technical features of the hyperloop transportation system, the critical technological and economic indicators as well (Table 3). For the passenger hyperloop from Los Angeles to San Francisco, the diameter of the tube is 2.23 m, and its cross-sectional area is 3.91 m2. The passenger capsule’s length, height, weight is respectively 25; 1.1; 1.35 m. The frontal projection area of the capsule is 1.4 m2. Though the tube will be low air pressure, the capsule should be aerodynamic shape to the air drag. Aerodynamic coefficients connected to Kantrowitz limit. The ratio of the capsule frontal projection area to the cross-section area of the tube a = 36%, the ratio of diameters b = 68%. The total weight of the passenger capsule with all equipment, including 28 passengers is 15,000 kg [5]. For cargo transportation, capsules will be different from passenger-only capsules, where the length is larger, the area of frontal projection is 4 m2, the required diameters are increased up to 3.3 m; the cross-sectional area is 8.55 m2. Aerodynamic coefficients would change to a = 47%, b = 68%. The total weight of the cargo capsule including passengers is maximum 2600 kg [1]. The capacity of carrying passengers and goods can be increased both by increasing the maximum capability of the capsules and numbers of tubes of the road. Cost One of the key selling points of hyperloop is its low costs of construction and operation which translates into low ticket prices for passengers. In his proposal, Musk indicated a roundtrip ticket price for Los Angeles to San Francisco of $40 (£29) so that Hyperloop was affordable for everyone. This ticket price was to cover

210 Table 3 Total cost of passenger hyperloop [2]

A. K. Yadav and J. Szpytko Component

Cost (million USD)

Pod structure and doors Propulsion Plumbing and compressor Inside and seats Air bearings and suspension Electronics and batteries Component assembly Pylon and tube building Tunneling Propulsion of tube Solar cells and batteries Vacuum pumps and station Land and permits Cost edge Total cost

9.8 5.0 11.0 10.2 8.0 6.0 4.0 3200.0 600.0 140.0 210.0 260.0 1000.0 536.0 6000.0

both the operating costs and to amortize the capital costs of the construction over a 20-year period [19]. The calculated construction and land acquisition costs for a hyperloop passenger service between Los Angeles and San Francisco was $16 million (£12 million) per mile, which was only 10% of the projected construction and land acquisition costs for the California High-speed rail Development of $177 million (£132 million) per mile [20]. Musk’s proposal offered no specific details around operating and maintenance costs. As the ticket price was relatively cheap and hyperloop’s projected ridership was moderate, we can assume that Musk’s projected operating costs are under representative of the true costs. This is likely to be due to the assumption that hyperloop’s largest operating cost (energy consumption) is fully covered by the self-sufficient solar panel system. Musk’s low construction costs have not aligned with projected costs in other commercial proposals. Virgin Hyperloop One gave a presentation citing an average cost for hyperloop of $25–27 million (£18–20 million) per mile just for the technology excluding land acquisition. Costs for specific routes proposed by Virgin Hyperloop One have been even higher. The costs of construction for the Abu Dhabi route are currently estimated at $52 million (£39 million) per mile excluding land acquisition [21]. An entirely underwater track from Helsinki to Stockholm was estimated to cost $64 million (£47 million) per mile [22]. Leaked documents from Virgin Hyperloop One indicated a 107-mile loop in California would cost $121 million (£89 million) per mile [23]. Moreover, recent analysis related to a Hyperloop System in Australia found that the cost of the system would be roughly 10 times more per mile than costs quoted by Musk [24]. From this, we can conclude that the cost of implementing hyperloop is likely to vary significantly in different locations and that Musk’s projections were on the optimistic side. Taking into

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account all the cost estimates, the average construction cost of Hyperloop is $73 million (£53 million) per mile. A round trip price of $120–200 rather than $40–60 would mean that hyperloop was slightly more expensive than some other transport modes for Los Angeles to San Francisco including the existing train/bus combo with a round trip cost of $118 and flights which vary between $95 (advance)–$116 (next day) for a round trip [1, 25]. However, as hyperloop would operate at considerably higher speeds, the round ticket price of $120–200 would still represent good value for money and allow hyperloop to compete well against other modes.

4.1

Safety and Security

Musk claimed that the design of hyperloop was considered with safety in mind from the start and whilst the concept throws up some unique safety challenges, elements of the proposed hyperloop system would be intrinsically safer than aeroplanes and trains. He detailed some clear safety benefits related to hyperloop compared to other transport modes, for example, the pods would not interact with transport or wildlife; a fully autonomous system would not be a victim of human error; and due to being built on pylons and the pods being enclosed within a tube, the system would be largely immune from adverse weather events. He also addressed some key safety challenges including: 1. Passenger emergencies, which are supported by multiple emergency braking systems to bring pods to a safe stop, 2. The structural integrity of the tube, which is constructed from strong thick steel which would be difficult to puncture, but in the event of a puncture would present no harm to passengers and result in slower pod speeds due to the higher air pressure within the tube, and may be overcome by the air pumping system and pods which are built to withstand variable air densities; and, 3. Pod depressurization, where in the event of a minor leak, the on-board environmental control system would maintain pressure with reserved air, and in the case of significant depressurization, oxygen masks would be deployed. Whilst some elements of safety were discussed by Musk, there were some key gaps in information on the risks of the system. Thus, many aspects related to the safety of the hyperloop have been raised by those reviewing the potential of the system. The business model of hyperloop relies on small headways between pods, which causes safety concerns if the system fails, and would require a full system shutdown should an emergency stop be triggered by a single pod. This particular element highlights the current single point failure within the proposed systems which brings the practicalities of operation and maintenance into question. Further to this, pods travelling at 760 miles per hour will generate kinetic energy with an equivalent of somewhere between 75 and 200 kg of TNT explosion (depending

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upon the likely weight range) and in the event of an accident would pose too big a risk to pass through urban areas without becoming a big safety concern. Design of pod. Collisions on hyperloop would be very serious and most likely fatal for passengers. However, there could be instances of harsh braking/deceleration that would far exceed levels experienced in cars. The design should take into account the crashworthiness of hyperloop, in particular the external construction of the pod and tube, the interior design of the pod, and optimal seating arrangements to enhance safety. Pod design would have to be subjected to performance testing on cushioning and appropriate restraint systems (e.g. seat belts and head restraints) in order to minimize injury to passengers. Design of pods and stations. The reactions, preferences and behavior of transport users play a critical role in effective, safe transport system design. A human factors team would risk assess and input into the design of pods and station layouts to improve levels of safety. Safety standard. Introducing an entirely new form of transportation into a world ruled by regulation will be one of the biggest challenges for the implementation of hyperloop. The existing regulations are out of date and not supportive of innovative technologies. Safety standards for an entirely new mode of transport would have to be developed, implemented through legislation and accepted by the industry. Type approvals. As a new mode of transport, type approvals for manufactured items e.g. chairs and tables and restraint systems within hyperloop will have to be devised to ensure their systems meet the required (new) legal standards. Testing and simulating. New technologies and designs need to be tested and quality assured prior to use, from component parts e.g. chairs and tables and restraint systems to entire vehicles/trains (which has parallels to hyperloop). Typically, a hyperloop pod would be replicated, and testing of component parts in crashes at low speeds would be undertaken, with the results being fed into a model simulating crashes at high speeds. Sustainability. Hyperloop would be one only transport system that will have its own sustainable power producer system that can run the whole system. The whole top of the tube’s outer side will have solar panels to produce power from sunlight. That makes it a more sustainable transport system. The system can transport passengers and goods at a very high speed one place to others. Currently, the speed 1200 km per hour, with this speed it can cover 561 km within 30 min. Compare to other transportation systems for high-speed rail it takes 2 h 38 min, by aircraft, it is covered in 1 h 15 min [1. From Table 4, it is so apparent that the implementation cost is cheap than other modes of high-speed transport systems. This transport system could be three modes of transportation like surface, underwater, underground as well. However, Association a unique underwater transport based on laying off the underwater tubes. As a result, the cargo can be transport with the speed of sound. It would be a vast and revolutionary turning point in the whole shipping industry. The system overcomes the limit on the speed of the land implemented transport in the most

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Table 4 Development of high-tension rail network around the world [5] Status

Asia

Europe

Others

Total world

In operation in kilometers Under construction in kilometers Total in kilometers

15,241 9625 24,866

7531 2929 10,280

362 200 562

22,954 12,754 35,708

advanced modern transportation systems, such as magnetic levitation (Maglev) train. A similar train in 2015 at the experimental site in japan reached a record of a maximum ground transportation speed of 603 km/h. This is half the acknowledged maximum speed of Hyperloop [1, 3]. According to [1], the twin-tube hyperloop route Los Angeles to San Francisco delivers passengers traffic in a volume of 840 people per hour, which consents to reach the route output capacity of 7.4 million people per year. The new transport system can guarantee low costs in the design and the realization of the transport system. A small weight of transport capsules of several tons compared to multi-tonnage railroad train allows the use of significantly simpler bridges and transitions in the construction of hyperloop roads. The total cost for passenger version hyperloop project from Los Angeles to San Francisco is $6 billion, while for the alternative high-speed rail project, the US governments are ready to spend $70 billion. Moreover, an interesting part of the project is a cheap ticket to travel long distances such as Los Angeles to San Francisco ticket will cost $20, a ticket for travel on the high-speed rail will cost $105. Lower operating outlays, consuming 21 MW of energy, the passenger pod moves with an air cushion. The energy produces by solar cells which can produce 57 MW of energy. The best part of the hyperloop is independence from weather conditions, no problem caused at high-speed by small solid counter particles. For subsonic transport, this is a big problem. This system is much quieter than the traditional high-speed transport system. Another part of this technology is ecological cleanliness due to using air, electricity generated by solar batteries. The system is safe from all the natural obstacles like floods, earthquakes, bad weather, against birds, animals and also different vehicles, pedestrians. As no physical interaction with cars and railways, pipelines, high voltage electric lines due to tube, this makes it safer and sounder traveling system. Path of motion, acceleration and deceleration processes are chosen to take into account the people experience overloads not exceeding 1 g. The fact is that the possible depressurization of the passenger capsule at the chosen pressure value in the tube leads to the instant death of passengers. To solve this problem each passenger should be dressed up with a special suit like spacesuits [1, 4]. However, if we do pressurize the passenger pod with air pressure, which pressure is suitable for the human body then also, we can sort out this kind of issue. However, after using the system the decision can limit the flow of passengers. Therefore, some experts believe that initially, we need to build the system to observe what kind of further issue still we have related

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to safety and reliability and also for cargo transports (if we want after passenger transportation system).

4.2

Connecting the Cities (Hyperloop Development in UK)

Looking at the current development rate in UK, there is a huge investment happening in the hyperloop development. This futuristic transport method can break the barrier of travel time at a very drastic rate. The distance between Edinburgh and London is approximately 330 miles, which would take 4 h and 20 min on a train. But the hyperloop transport can make it possible in 29 min. Several destinations such as from London to Nottingham or London to Sheffield would be several times faster with this technology [22]. Various routes were being suggested to the team while the consultation exercise. The supported route from all is to create a Northern Arc section connecting Liverpool, Manchester and Leeds, connecting the airports of Liverpool and Manchester to create a combined three-runway hub airport for the north [14]. This is a futuristic technology that is still a vision and visualization rather than real acceptable technology. The huge area of research and opportunity to work and develop a real-time working situation is so huge and to develop such modes of transportation that has the capability to dominate the world transport systems is a challenging aspect to consider. Magnetic levitation, air compression and suspension system, sustainability and ability to carry huge resources in a single go are the most prominent theories that many countries are focusing on. Also, the financial and economic trading on this technology is a huge research area for the countries to focus on and a lot of work gap is to be filled eventually. How should the hyperloop exploitation potential expressed through travel time, capacity, above ground or below, safety and security, sustainability be used in the urban transport system?

5 Conclusions As the present mode of transportation is not fast enough and safe as well. Hyperloop concept should be durable sustainable, reliable and faster than other modes of transportation. Many countries like India, China, Dubai, UK have taken the privilege to research on this technology and came forward with establishment plans. This is a major step taken in terms of financial assets and also to reduce travel time. Faster mode of transportation with safety is the key for better economy and material transfer is a mere requirement in the twenty-first century. So, proposing new modes of faster transport is necessary. The concept of connecting the urban areas with the hyperloop concept will enhance the safety and reduces the pollution caused by the vehicles.

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The new system proposed should be economical, safe, durable and sustainable in all conditions. An estimated financial investment of $6 billion dollars has been proposed for developing the tunnel track between Los Angeles and San Francisco. This substantial amount is for only public or human transportation and material transportation is done in regular methods. The speeds offered by hyperloop could also have wider implications for the economy by enabling labor and commercial markets to integrate within the existing centers of commerce, which would allow countries to grow by fully utilizing skills, workforces, and resources nationwide to remain competitive. However, a barrier to this integration would be the cost of travelling on hyperloop, which would far exceed the cost of the local journeys into economic centers and would be an unacceptable commuting cost for the majority of the population. By implementing the hyperloop concept by interlinking the cities will enhance mobility and make transportation faster and reliable.

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