The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Migrating towards Using Electric Vehicles in Fleets – Proposed Methods for Demand Estimation and Fleet Design

Carsharing and electric vehicles have emerged as sustainable transportation alternatives to mitigate transportation, environmental, and social issues in cities. This dissertation combines three correlated topics: carsharing feasibility, electric vehicle carsharing fleet optimization, and efficient fleet management. First, the potential demand for electric vehicle carsharing in Beijing is estimated using data from a survey conducted the summer of 2013 in Beijing. This utilizes statistical analysis method, binary logit regression. Secondly, a model was developed to estimate carsharing mode split by the function of utilization and appropriate carsharing fleet size was simulated under three different fleet types: an EV fleet with level 2 chargers, an EV fleet with level 3 chargers, and a gasoline vehicle fleet. This study also performs an economic analysis to determine the payback period for recovering the initial EV charging infrastructure costs. Finally, this study develops a fleet size and composition optimization model with cost constraints for the University of Tennessee, Knoxville motor pool fleet. This will help the fleet manage efficiently with minimum total costs and greater demand satisfaction. This dissertation can help guide future sustainable transportation planning and policy

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. 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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). 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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). 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Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.

To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on the environmental implications of these emerging systems is often limited by using aggregated travel pattern data to characterize personal mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect several key factors that determine potential environmental impacts, including charging behaviors, connection needs between different transportation modes, and car/ride sharing potentials. Therefore, to better understand these systems and inform decision making, travel patterns at the individual level need to be considered. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefits of integrating individual travel patterns into environmental assessments through three case studies (vehicle electrification, charging station siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification study indicate that individual travel patterns can impact the environmental performance of fleet electrification. When battery cost exceeds $200/kWh, vehicles with greater battery range cannot continuously improve travel electrification and can reduce electrification rate. At the current battery cost of$400/kWh, targeting subsidies to vehicles with battery range around 90 miles can achieve higher electrification rate. The public charging station siting case demonstrates that individual travel patterns can better estimate charging demand and guide charging infrastructure development. Charging stations sited according to individual travel patterns can increase electrification rate by 59% to 88% compared to existing sites. Lastly, the ride sharing case shows that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides in Beijing can reduce total travel distance and air emissions by 33% with 10-minute travel time deviation tolerance. Only minimal tolerance to travel time change (4 minutes) is needed from the riders to enable significant ride sharing (sharing 60% of the trips and saving 20% of travel distance). In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems.PhDNatural Resources and Environment and Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113510/1/caih_1.pd

Sustaining Uber: Opportunities for Electric Vehicle Integration

Uber and Lyft, the “unregulated taxis” that are putting traditional taxi companies out of business, are expanding quickly and changing the landscape of urban transportation as they go. This thesis analyzes the environmental impacts of Transportation Network Companies, particularly in California, with respect to travel behavior, congestion, and fuel efficiency. The analysis suggests that fuel efficient taxis are being replaced by less fuel efficient Uber and Lyft vehicles. Linear regressions were run on data from the Clean Vehicle Rebate Project’s Electric Vehicle Consumer Survey of electric vehicle owners in California. The findings indicate that Uber drivers are more reliant upon the state rebate than the general population of electric vehicle owners in California

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid