An optimization model for a battery swapping station in Hong Kong

In this paper, a battery swapping station (BSS) model is proposed as an economic and convenient way to provide energy for the batteries of the electric vehicles (EVs). This method would overcome some drawbacks to the use of electric vehicles like long charging time and insufficient running distance. On the economic concern of a battery swapping station, the station would optimize the availability of the batteries in stock, and at the same time determine the best strategy for recharging the batteries on hand. By optimizing the charging method of the batteries, an optimization model of BSS with the maximum number of batteries in stock has been developed for the bus terminal at the Hong Kong International Airport. The secondary objective would be to minimize a cost on the batteries due to the use of different charging schemes. The genetic algorithm (GA) has been used to implement the optimization model, and simulation results are shown.published_or_final_versio

Scheduling of EV Battery Swapping, I: Centralized Solution

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information

Online Station Assignment for Electric Vehicle Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs' locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters

Online Station Assignment for Electric Vehicle Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs' locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters

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

Energy storage impact on light rail developments

– Smart cities imply a range of efficient mobility solutions for people and goods at the same time as minimising the environmental burden. This short paper focuses on Light Rail and particularly Tram systems as having advantages in responding to these needs and is the first stage on a longer project which will provide greater detail in due course. It further considers the alternatives for powering the system as an important component in the development of a clean, attractive and economic urban mass transit resource for the smart city. This leads to energy storage as a potential alternative to continuous energy supply such as overhead cables, and is followed by a comparison of various methods of on-board energy storage including batteries, supercapacitors and hydrogen. Interim conclusions are presented

Cost-Benefit Analysis of Electric Bus Fleet with Various Operation Intervals

Electric buses are particularly suitable for city and suburban routes due to zero local exhaust and noise emissions. The operation schedule interval defines the charging power, bus fleet size and total cost of ownership of a bus. We propose a novel cost-benefit method for the scheduling of an electric city bus fleet on a single route. Three different charging infrastructure scenarios were considered. In the first scenario, only one charging station was used. The second scenario considered two charging stations that were located at the same terminus. In the third scenario, two charging stations were located at opposite terminuses. The costs and utilization rates of the buses were analyzed with operation intervals up to 40 minutes. The first scenario with a single charging station had the lowest costs for the entire bus fleet system when the utilization rate was considered. Furthermore, the results show that certain schedule intervals are more cost-beneficial in terms of vehicle specific life-cycle costs than others. In the future, the proposed method is expanded to aid the design of bus network scheduling under energy demand uncertainty.Peer reviewe

Cost Minimization for Charging Electric Bus Fleets

Recent attention for reduced carbon emissions has pushed transit authorities to adopt battery electric buses (BEBs). One challenge experienced by BEB users is extended charge times, which create logistical challenges and may force BEBs to charge when energy is more expensive. Furthermore, BEB charging leads to high power demands, which can significantly increase monthly power costs and may push the electrical infrastructure beyond its present capacity, requiring expensive upgrades. This work presents a novel method for minimizing the monthly cost of BEB charging while meeting bus route constraints. This method extends previous work by incorporating a more novel cost model, effects from uncontrolled loads, differences between daytime and overnight charging, and variable rate charging. A graph-based network-flow framework, represented by a mixed-integer linear program, encodes the charging action space, physical bus constraints, and battery state of the charge dynamics. The results for three scenarios are considered: uncontested charging, which uses equal numbers of buses and chargers; contested charging, which has more buses than chargers; and variable charge rates. Among other findings, we show that BEBs can be added to the fleet without raising the peak power demand for only the cost of the energy, suggesting that conversion to electrified transit is possible without upgrading power delivery infrastructure

Sustainable Perspective of Electric Vehicles and Its Future Prospects

Vehicles running on fossil fuel are creating a threat to the environment by emitting pollutants such as carbon monoxide, carbon dioxide and sulfur and nitrogen oxides into the environment. Electric vehicles and hybrid electric vehicles provide a perennial solution to this problem and since the utilization of renewables for charging, the market is on verge of electric vehicle revolution. Electric propulsion systems can also be used in heavy transport vehicles, thus transitioning them to electric. This paper puts forth an overview of the electric vehicles for transportation of masses and freight across the globe and emphasis on the battery charging infrastructures. Recent trends and advancements in electric vehicle batteries are discussed briefly, along with sustainability in Li-ion batteries and its materials; moreover, a comparative study of different electric vehicles available in the Indian market is done. Similarly, the incentives offered by government, challenges faced by these vehicles and future development areas are conversed at the end of the paper

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Purpose: The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes. Design/methodology/approach: The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model. Findings: This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically. Originality/value: The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses