Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

[EN] The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.This research was funded by the project SIS.JCG.19.03 of Universidad de las Americas, Ecuador.Clairand-Gómez, J.; Guerra-Terán, P.; Serrano-Guerrero, JX.; González-Rodríguez, M.; Escrivá-Escrivá, G. (2019). Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies. Energies. 12(16):1-22. https://doi.org/10.3390/en12163114S1221216Emadi, A. (2011). Transportation 2.0. IEEE Power and Energy Magazine, 9(4), 18-29. doi:10.1109/mpe.2011.941320Fahimi, B., Kwasinski, A., Davoudi, A., Balog, R., & Kiani, M. (2011). Charge It! IEEE Power and Energy Magazine, 9(4), 54-64. doi:10.1109/mpe.2011.941321Yilmaz, M., & Krein, P. T. (2013). Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles. IEEE Transactions on Power Electronics, 28(5), 2151-2169. doi:10.1109/tpel.2012.2212917Tagliaferri, C., Evangelisti, S., Acconcia, F., Domenech, T., Ekins, P., Barletta, D., & Lettieri, P. (2016). Life cycle assessment of future electric and hybrid vehicles: A cradle-to-grave systems engineering approach. Chemical Engineering Research and Design, 112, 298-309. doi:10.1016/j.cherd.2016.07.003Zackrisson, M., Fransson, K., Hildenbrand, J., Lampic, G., & O’Dwyer, C. (2016). Life cycle assessment of lithium-air battery cells. Journal of Cleaner Production, 135, 299-311. doi:10.1016/j.jclepro.2016.06.104Wu, Y., Yang, Z., Lin, B., Liu, H., Wang, R., Zhou, B., & Hao, J. (2012). Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of China. Energy Policy, 48, 537-550. doi:10.1016/j.enpol.2012.05.060Shen, W., Han, W., Chock, D., Chai, Q., & Zhang, A. (2012). Well-to-wheels life-cycle analysis of alternative fuels and vehicle technologies in China. Energy Policy, 49, 296-307. doi:10.1016/j.enpol.2012.06.038Wang, R., Wu, Y., Ke, W., Zhang, S., Zhou, B., & Hao, J. (2015). Can propulsion and fuel diversity for the bus fleet achieve the win–win strategy of energy conservation and environmental protection? Applied Energy, 147, 92-103. doi:10.1016/j.apenergy.2015.01.107Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid. IEEE Transactions on Power Systems, 25(1), 371-380. doi:10.1109/tpwrs.2009.2036481Shafiee, S., Fotuhi-Firuzabad, M., & Rastegar, M. (2013). Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems. IEEE Transactions on Smart Grid, 4(3), 1351-1360. doi:10.1109/tsg.2013.2251483Pieltain Fernandez, L., Gomez San Roman, T., Cossent, R., Mateo Domingo, C., & Frias, P. (2011). Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Transactions on Power Systems, 26(1), 206-213. doi:10.1109/tpwrs.2010.2049133Lucas, A., Bonavitacola, F., Kotsakis, E., & Fulli, G. (2015). Grid harmonic impact of multiple electric vehicle fast charging. Electric Power Systems Research, 127, 13-21. doi:10.1016/j.epsr.2015.05.012Turker, H., Bacha, S., Chatroux, D., & Hably, A. (2012). Low-Voltage Transformer Loss-of-Life Assessments for a High Penetration of Plug-In Hybrid Electric Vehicles (PHEVs). IEEE Transactions on Power Delivery, 27(3), 1323-1331. doi:10.1109/tpwrd.2012.2193423Kempton, W., & Tomić, J. (2005). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources, 144(1), 268-279. doi:10.1016/j.jpowsour.2004.12.025Guille, C., & Gross, G. (2009). A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy, 37(11), 4379-4390. doi:10.1016/j.enpol.2009.05.053Geng, Z., Conejo, A. J., Chen, Q., Xia, Q., & Kang, C. (2017). Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production. Applied Energy, 193, 540-549. doi:10.1016/j.apenergy.2017.02.051Verbruggen, A., Fischedick, M., Moomaw, W., Weir, T., Nadaï, A., Nilsson, L. J., … Sathaye, J. (2010). Renewable energy costs, potentials, barriers: Conceptual issues. Energy Policy, 38(2), 850-861. doi:10.1016/j.enpol.2009.10.036Oda, T., Aziz, M., Mitani, T., Watanabe, Y., & Kashiwagi, T. (2018). Mitigation of congestion related to quick charging of electric vehicles based on waiting time and cost–benefit analyses: A japanese case study. Sustainable Cities and Society, 36, 99-106. doi:10.1016/j.scs.2017.10.024Arkin, E. M., Carmi, P., Katz, M. J., Mitchell, J. S. B., & Segal, M. (2019). Locating battery charging stations to facilitate almost shortest paths. Discrete Applied Mathematics, 254, 10-16. doi:10.1016/j.dam.2018.07.019Gallardo-Lozano, J., Milanés-Montero, M. I., Guerrero-Martínez, M. A., & Romero-Cadaval, E. (2012). Electric vehicle battery charger for smart grids. Electric Power Systems Research, 90, 18-29. doi:10.1016/j.epsr.2012.03.015Aziz, M., Oda, T., & Ito, M. (2016). Battery-assisted charging system for simultaneous charging of electric vehicles. Energy, 100, 82-90. doi:10.1016/j.energy.2016.01.069Mehboob, N., Restrepo, M., Canizares, C. A., Rosenberg, C., & Kazerani, M. (2019). Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties. IEEE Transactions on Smart Grid, 10(3), 2999-3009. doi:10.1109/tsg.2018.2816404García-Villalobos, J., Zamora, I., San Martín, J. I., Asensio, F. J., & Aperribay, V. (2014). Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches. Renewable and Sustainable Energy Reviews, 38, 717-731. doi:10.1016/j.rser.2014.07.040Richardson, D. B. (2013). Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration. Renewable and Sustainable Energy Reviews, 19, 247-254. doi:10.1016/j.rser.2012.11.042Haidar, A. M. A., Muttaqi, K. M., & Sutanto, D. (2014). Technical challenges for electric power industries due to grid-integrated electric vehicles in low voltage distributions: A review. Energy Conversion and Management, 86, 689-700. doi:10.1016/j.enconman.2014.06.025Mwasilu, F., Justo, J. J., Kim, E.-K., Do, T. D., & Jung, J.-W. (2014). Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and Sustainable Energy Reviews, 34, 501-516. doi:10.1016/j.rser.2014.03.031Habib, S., Kamran, M., & Rashid, U. (2015). Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks – A review. Journal of Power Sources, 277, 205-214. doi:10.1016/j.jpowsour.2014.12.020Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renewable and Sustainable Energy Reviews, 53, 720-732. doi:10.1016/j.rser.2015.09.012Raslavičius, L., Azzopardi, B., Keršys, A., Starevičius, M., Bazaras, Ž., & Makaras, R. (2015). Electric vehicles challenges and opportunities: Lithuanian review. Renewable and Sustainable Energy Reviews, 42, 786-800. doi:10.1016/j.rser.2014.10.076Rahman, I., Vasant, P. M., Singh, B. S. M., Abdullah-Al-Wadud, M., & Adnan, N. (2016). Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renewable and Sustainable Energy Reviews, 58, 1039-1047. doi:10.1016/j.rser.2015.12.353Faddel, S., Al-Awami, A., & Mohammed, O. (2018). Charge Control and Operation of Electric Vehicles in Power Grids: A Review. Energies, 11(4), 701. doi:10.3390/en11040701Ercan, T., Onat, N. C., & Tatari, O. (2016). Investigating carbon footprint reduction potential of public transportation in United States: A system dynamics approach. Journal of Cleaner Production, 133, 1260-1276. doi:10.1016/j.jclepro.2016.06.051Kwan, S. C., & Hashim, J. H. (2016). A review on co-benefits of mass public transportation in climate change mitigation. Sustainable Cities and Society, 22, 11-18. doi:10.1016/j.scs.2016.01.004Kolbe, K. (2019). Mitigating urban heat island effect and carbon dioxide emissions through different mobility concepts: Comparison of conventional vehicles with electric vehicles, hydrogen vehicles and public transportation. Transport Policy, 80, 1-11. doi:10.1016/j.tranpol.2019.05.007Zalakeviciute, R., Rybarczyk, Y., López-Villada, J., & Diaz Suarez, M. V. (2018). Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM 2.5 pollution in a mid-size South American city. Atmospheric Pollution Research, 9(1), 66-75. doi:10.1016/j.apr.2017.07.001Dell’ Olio, L., Ibeas, A., & Cecin, P. (2011). The quality of service desired by public transport users. Transport Policy, 18(1), 217-227. doi:10.1016/j.tranpol.2010.08.005Mahmoud, M., Garnett, R., Ferguson, M., & Kanaroglou, P. (2016). Electric buses: A review of alternative powertrains. Renewable and Sustainable Energy Reviews, 62, 673-684. doi:10.1016/j.rser.2016.05.019Nissan Leafhttps://www.nissan.co.uk/vehicles/new-vehicles/leaf/range-charging.htmlIntroducing the Fully Charged 2020 Kia Soul EVhttps://www.kia.com/us/en/content/vehicles/upcoming-vehicles/2020-soul-eve6https://en.byd.com/wp-content/uploads/2017/06/e6_cutsheet.pdfTesla Model Shttps://www.tesla.com/modelsBushttps://en.byd.com/bus/40-electric-motor-coach/Urbino Electrichttps://www.solarisbus.com/en/vehicles/zero-emissions/urbino-electricVolvo 7900 Electrichttps://www.volvobuses.co.uk/en-gb/our-offering/buses/volvo-7900-electric/specifications.htmlCollin, R., Miao, Y., Yokochi, A., Enjeti, P., & von Jouanne, A. (2019). Advanced Electric Vehicle Fast-Charging Technologies. Energies, 12(10), 1839. doi:10.3390/en12101839Yang, Y., El Baghdadi, M., Lan, Y., Benomar, Y., Van Mierlo, J., & Hegazy, O. (2018). Design Methodology, Modeling, and Comparative Study of Wireless Power Transfer Systems for Electric Vehicles. Energies, 11(7), 1716. doi:10.3390/en11071716Bi, Z., Song, L., De Kleine, R., Mi, C. C., & Keoleian, G. A. (2015). Plug-in vs. wireless charging: Life cycle energy and greenhouse gas emissions for an electric bus system. Applied Energy, 146, 11-19. doi:10.1016/j.apenergy.2015.02.031Siqi Li, & Mi, C. C. (2015). Wireless Power Transfer for Electric Vehicle Applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 3(1), 4-17. doi:10.1109/jestpe.2014.2319453Musavi, F., & Eberle, W. (2014). Overview of wireless power transfer technologies for electric vehicle battery charging. IET Power Electronics, 7(1), 60-66. doi:10.1049/iet-pel.2013.0047Wang, Z., Wei, X., & Dai, H. (2015). Design and Control of a 3 kW Wireless Power Transfer System for Electric Vehicles. Energies, 9(1), 10. doi:10.3390/en9010010Sarker, M. R., Pandzic, H., & Ortega-Vazquez, M. A. (2015). Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station. IEEE Transactions on Power Systems, 30(2), 901-910. doi:10.1109/tpwrs.2014.2331560Adegbohun, F., von Jouanne, A., & Lee, K. (2019). Autonomous Battery Swapping System and Methodologies of Electric Vehicles. Energies, 12(4), 667. doi:10.3390/en12040667OPPChargeCommon Interface for Automated Charging of Hybrid Electric and Electric Commercial Vehicleshttps://www.oppcharge.org/dok/OPPCharge Specification 2nd edition 20190421.pdfFast Charging of Electric Vehicleshttps://www.oppcharge.orgJiang, C. X., Jing, Z. X., Cui, X. R., Ji, T. Y., & Wu, Q. H. (2018). Multiple agents and reinforcement learning for modelling charging loads of electric taxis. Applied Energy, 222, 158-168. doi:10.1016/j.apenergy.2018.03.164Fraile-Ardanuy, J., Castano-Solis, S., Álvaro-Hermana, R., Merino, J., & Castillo, Á. (2018). Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet. Energy Conversion and Management, 157, 59-70. doi:10.1016/j.enconman.2017.11.070Rao, R., Cai, H., & Xu, M. (2018). Modeling electric taxis’ charging behavior using real-world data. International Journal of Sustainable Transportation, 12(6), 452-460. doi:10.1080/15568318.2017.1388887Litzlbauer, M. (2015). Technische Machbarkeitsanalyse einer rein elektrisch betriebenen Taxiflotte. e & i Elektrotechnik und Informationstechnik, 132(3), 172-177. doi:10.1007/s00502-015-0296-3Liao, B., Li, L., Li, B., Mao, J., Yang, J., Wen, F., & Salam, M. A. (2016). Load modeling for electric taxi battery charging and swapping stations: Comparison studies. 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC). doi:10.1109/spec.2016.7846135Zou, Y., Wei, S., Sun, F., Hu, X., & Shiao, Y. (2016). Large-scale deployment of electric taxis in Beijing: A real-world analysis. Energy, 100, 25-39. doi:10.1016/j.energy.2016.01.062Asamer, J., Reinthaler, M., Ruthmair, M., Straub, M., & Puchinger, J. (2016). Optimizing charging station locations for urban taxi providers. Transportation Research Part A: Policy and Practice, 85, 233-246. doi:10.1016/j.tra.2016.01.014Yang, J., Dong, J., & Hu, L. (2017). A data-driven optimization-based approach for siting and sizing of electric taxi charging stations. Transportation Research Part C: Emerging Technologies, 77, 462-477. doi:10.1016/j.trc.2017.02.014Jiang, C., Jing, Z., Ji, T., & Wu, Q. (2018). Optimal location of PEVCSs using MAS and ER approach. IET Generation, Transmission & Distribution, 12(20), 4377-4387. doi:10.1049/iet-gtd.2017.1907Pan, A., Zhao, T., Yu, H., & Zhang, Y. (2019). Deploying Public Charging Stations for Electric Taxis: A Charging Demand Simulation Embedded Approach. IEEE Access, 7, 17412-17424. doi:10.1109/access.2019.2894780Chen Lianfu, Zhang, W., Huang, Y., & Zhang, D. (2014). Research on the charging station service radius of electric taxis. 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). doi:10.1109/itec-ap.2014.6941081Yang, Y., Zhang, W., Niu, L., & Jiang, J. (2015). Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale. Energies, 8(2), 1256-1272. doi:10.3390/en8021256Niu, L., & Zhang, D. (2015). Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization. The Scientific World Journal, 2015, 1-9. doi:10.1155/2015/354952Yang, Z., Guo, T., You, P., Hou, Y., & Qin, S. J. (2019). Distributed Approach for Temporal–Spatial Charging Coordination of Plug-in Electric Taxi Fleet. IEEE Transactions on Industrial Informatics, 15(6), 3185-3195. doi:10.1109/tii.2018.2879515Rossi, F., Iglesias, R., Alizadeh, M., & Pavone, M. (2020). On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms. IEEE Transactions on Control of Network Systems, 7(1), 384-397. doi:10.1109/tcns.2019.2923384Liang, Y., Zhang, X., Xie, J., & Liu, W. (2017). An Optimal Operation Model and Ordered Charging/Discharging Strategy for Battery Swapping Stations. Sustainability, 9(5), 700. doi:10.3390/su9050700XU, X., YAO, L., ZENG, P., LIU, Y., & CAI, T. (2015). Architecture and performance analysis of a smart battery charging and swapping operation service network for electric vehicles in China. Journal of Modern Power Systems and Clean Energy, 3(2), 259-268. doi:10.1007/s40565-015-0118-yJing, Z., Fang, L., Lin, S., & Shao, W. (2014). Modeling for electric taxi load and optimization model for charging/swapping facilities of electric taxi. 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). doi:10.1109/itec-ap.2014.6941160Wang, Y., Ding, W., Huang, L., Wei, Z., Liu, H., & Stankovic, J. A. (2018). Toward Urban Electric Taxi Systems in Smart Cities: The Battery Swapping Challenge. IEEE Transactions on Vehicular Technology, 67(3), 1946-1960. doi:10.1109/tvt.2017.2774447You, P., Yang, Z., Zhang, Y., Low, S. H., & Sun, Y. (2016). Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses. IEEE Transactions on Power Systems, 31(5), 3473-3483. doi:10.1109/tpwrs.2015.2487273Yang, Z., Sun, L., Chen, J., Yang, Q., Chen, X., & Xing, K. (2014). Profit Maximization for Plug-In Electric Taxi With Uncertain Future Electricity Prices. IEEE Transactions on Power Systems, 29(6), 3058-3068. doi:10.1109/tpwrs.2014.2311120Yang, Z., Sun, L., Ke, M., Shi, Z., & Chen, J. (2014). Optimal Charging Strategy for Plug-In Electric Taxi With Time-Varying Profits. IEEE Transactions on Smart Grid, 5(6), 2787-2797. doi:10.1109/tsg.2014.2354473Yang, J., Xu, Y., & Yang, Z. (2017). Regulating the Collective Charging Load of Electric Taxi Fleet via Real-Time Pricing. IEEE Transactions on Power Systems, 32(5), 3694-3703. doi:10.1109/tpwrs.2016.2643685Du, R., Liao, G., Zhang, E., & Wang, J. (2018). Battery charge or change, which is better? A case from Beijing, China. Journal of Cleaner Production, 192, 698-711. doi:10.1016/j.jclepro.2018.05.021Yang, J., Dong, J., Lin, Z., & Hu, L. (2016). Predicting market potential and environmental benefits of deploying electric taxis in Nanjing, China. Transportation Research Part D: Transport and Environment, 49, 68-81. doi:10.1016/j.trd.2016.08.037You, P., Low, S. H., Yang, Z., Zhang, Y., & Lingkun Fu. (2016). Real-time recommendation algorithm of battery swapping stations for electric taxis. 2016 IEEE Power and Energy Society General Meeting (PESGM). doi:10.1109/pesgm.2016.7741620Dai, Q., Cai, T., Duan, S., & Zhao, F. (2014). Stochastic Modeling and Forecasting of Load Demand for Electric Bus Battery-Swap Station. IEEE Transactions on Power Delivery, 29(4), 1909-1917. doi:10.1109/tpwrd.2014.2308990Mohamed, M., Farag, H., El-Taweel, N., & Ferguson, M. (2017). Simulation of electric buses on a full transit network: Operational feasibility and grid impact analysis. Electric Power Systems Research, 142, 163-175. doi:10.1016/j.epsr.2016.09.032Zhang, X. (2018). Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm. Energies, 11(6), 1449. doi:10.3390/en11061449Ding, H., Hu, Z., & Song, Y. (2015). Value of the energy storage system in an electric bus fast charging station. Applied Energy, 157, 630-639. doi:10.1016/j.apenergy.2015.01.058Qin, N., Gusrialdi, A., Paul Brooker, R., & T-Raissi, A. (2016). Numerical analysis of electric bus fast charging strategies for demand charge reduction. Transportation Research Part A: Policy and Practice, 94, 386-396. doi:10.1016/j.tra.2016.09.014Huimiao Chen, Zechun Hu, Zhiwei Xu, Jiayi Li, Honggang Zhang, Xue Xia, … Mingwei Peng. (2016). Coordinated charging strategies for electric bus fast charging stations. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). doi:10.1109/appeec.2016.7779677Chen, H., Hu, Z., Zhang, H., & Luo, H. (2018). Coordinated charging and discharging strategies for plug-in electric bus fast charging station with energy storage system. IET Generation, Transmission & Distribution, 12(9), 2019-2028. doi:10.1049/iet-gtd.2017.0636Gao, Y., Guo, S., Ren, J., Zhao, Z., Ehsan, A., & Zheng, Y. (2018). An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors. Energies, 11(8), 2060. doi:10.3390/en11082060Cheng, Y., & Tao, J. (2018). Optimization of A Micro Energy Network Integrated with Electric Bus Battery Swapping Station and Distributed PV. 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). doi:10.1109/ei2.2018.8582236Sebastiani, M. T., Luders, R., & Fonseca, K. V. O. (2016). Evaluating Electric Bus Operation for a Real-World BRT Public Transportation Using Simulation Optimization. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2777-2786. doi:10.1109/tits.2016.2525800Wang, Y., Huang, Y., Xu, J., & Barclay, N. (2017). Optimal recharging scheduling for urban electric buses: A case study in Davis. Transportation Research Part E: Logistics and Transportation Review, 100, 115-132. doi:10.1016/j.tre.2017.01.001Liu, Z., Song, Z., & He, Y. (2018). Planning of Fast-Charging Stations for a Battery Electric Bus System under Energy Consumption Uncertainty. Transportation Research Record: Journal of the Transportation Research Board, 2672(8), 96-107. doi:10.1177/0361198118772953Leou, R.-C., & Hung, J.-J. (2017). Optimal Charging Schedule Planning and Economic Analysis for Electric Bus Charging Stations. Energies, 10(4), 483. doi:10.3390/en10040483Bak, D.-B., Bak, J.-S., & Kim, S.-Y. (2018). Strategies for Implementing Public Service Electric Bus Lines by Charging Type in Daegu Metropolitan City, South Korea. Sustainability, 10(10), 3386. doi:10.3390/su10103386Chen, Z., Yin, Y., & Song, Z. (2018). A cost-competitiveness analysis of charging infrastructure for electric bus operations. Transportation Research Part C: Emerging Technologies, 93, 351-366. doi:10.1016/j.trc.2018.06.006Cheng, Y., Wang, W., Ding, Z., & He, Z. (2019). Electric bus fast charging station resource planning consid

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Rapid growth in the electrification of bus fleets, driven by substantial environmental benefits, is facing challenges such as range anxiety, prolonged charging durations, and reduced flexibility compared to combustion engine buses. This study first conducts a comprehensive bibliometric analysis of diverse publications to identify key research trends in electric buses (E-buses). It then offers a thorough comparison of charging technologies, encompassing topologies, power flow capabilities, costs, grid impacts, and efficiency, along with an examination of existing standards, norms, and challenges. With a classification of nearly 150 references, the study aims to illuminate the strengths and weaknesses of each charging technology, providing a solid background for selecting optimal topologies and strategies for specific applications. Emphasizing the importance of a nuanced trade-off between the quantity and type of chargers and E-bus battery capacity in each scenario, the research goes beyond technical considerations to explore potential future trends in the field. The information gathered in this review is a helpful guide for policymakers, industry experts, and researchers dealing with the complexities of E-bus charging infrastructure

Techno-Economic and Sustainable Challenges for EV Adoption in India: Analysis of the Impact of EV Usage Patterns and Policy Recommendations for Facilitating Seamless Integration

This paper explores the intricate challenges that are impeding the widespread adoption of Electric Vehicles (EVs) and explores the concerted efforts of the research community towards addressing these obstacles. The surge in interest surrounding EVs as a sustainable transportation alternative is undeniable, yet several hurdles persist in hindering their mass acceptance. From limitations in battery technology and charging infrastructure to concerns over range anxiety and manufacturing sustainability, these challenges form a multifaceted barrier. However, the research community has been actively engaged in tackling each issue with innovative solutions. Advancements in battery chemistry and energy storage, coupled with improvements in charging networks and smart grid integration, are poised to reshape the EV landscape. Moreover, studies on user behavior, public policy, and lifecycle analysis are contributing to the development of holistic strategies for enhancing EV adoption. By delving into these challenges and the ongoing research endeavors, this paper sheds light on the evolving pathway towards a future where EVs can thrive as a mainstream mode of transportation. Also, an analysis is conducted to evaluate the economic viability of EVs based on daily range considerations, with the objective of determining which category of users would benefit most from adopting EVs. Furthermore, policies are proposed that are aimed at establishing a harmonious and balanced EV ecosystem

ChargeUp! Data Swap: Using data from battery swapping e-motorcycles in Nairobi to assess impacts and plan infrastructure

The dearth of available data on e-motorcycle usage in African cities is a significant challenge in impact studies of e-motorcycle deployment. The ChargeUp! project aimed to fill this research gap using operational data from e-motorcycles and battery swap stations in Nairobi to perform modelling and analysis to determine several key outputs. This project included the analysis of: e-motorcycle trips; battery swapping demand; battery charging energy consumption; swap battery charging related emissions for a high renewables and high fossil energy mix scenarios; charging related electricity costs for different tariff scenarios; the effect of a co-ordinated charging scenario on emissions and tariffs; optimal battery ratios and required numbers of swap stations; and a methodology to determine optimal regions for battery swap stations based on trip data