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Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Author

Listed:
  • Shyang-Chyuan Fang

    (Department of Tourism and Leisure, National Penghu University of Science and Technology, Makung 880, Taiwan)

  • Bwo-Ren Ke

    (Department of Electrical Engineering, National Penghu University of Science and Technology, Makunk 880, Taiwan)

  • Chen-Yuan Chung

    (Department of Electrical Engineering, National Penghu University of Science and Technology, Makunk 880, Taiwan)

Abstract

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system.

Suggested Citation

  • Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:890-:d:103191
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    References listed on IDEAS

    as
    1. Wang, Shunli & Shang, Liping & Li, Zhanfeng & Deng, Hu & Li, Jianchao, 2016. "Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation," Applied Energy, Elsevier, vol. 166(C), pages 44-58.
    2. Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu & Zhibin Song, 2011. "Model Predictive Control-Based Fast Charging for Vehicular Batteries," Energies, MDPI, vol. 4(8), pages 1-19, August.
    3. Yuqing Yang & Weige Zhang & Liyong Niu & Jiuchun Jiang, 2015. "Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale," Energies, MDPI, vol. 8(2), pages 1-17, February.
    4. Trappey, Amy J.C. & Trappey, Charles & Hsiao, C.T. & Ou, Jerry J.R. & Li, S.J. & Chen, Kevin W.P., 2012. "An evaluation model for low carbon island policy: The case of Taiwan's green transportation policy," Energy Policy, Elsevier, vol. 45(C), pages 510-515.
    5. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    6. Arturo Valdivia-Gonzalez & Daniel Zaldívar & Fernando Fausto & Octavio Camarena & Erik Cuevas & Marco Perez-Cisneros, 2017. "A States of Matter Search-Based Approach for Solving the Problem of Intelligent Power Allocation in Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 10(1), pages 1-14, January.
    7. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    8. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    9. Xingping Zhang & Rao Rao & Jian Xie & Yanni Liang, 2014. "The Current Dilemma and Future Path of China’s Electric Vehicles," Sustainability, MDPI, vol. 6(3), pages 1-27, March.
    10. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    11. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    12. Ke, Bwo-Ren & Chung, Chen-Yuan & Chen, Yen-Chang, 2016. "Minimizing the costs of constructing an all plug-in electric bus transportation system: A case study in Penghu," Applied Energy, Elsevier, vol. 177(C), pages 649-660.
    13. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    14. Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
    15. Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.
    16. Peters, Glen P., 2008. "From production-based to consumption-based national emission inventories," Ecological Economics, Elsevier, vol. 65(1), pages 13-23, March.
    17. Ganesh Mohan & Francis Assadian & Stefano Longo, 2013. "An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains," Energies, MDPI, vol. 6(10), pages 1-31, October.
    18. Maigha & Mariesa L. Crow, 2014. "Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging," Energies, MDPI, vol. 7(4), pages 1-23, March.
    19. Dongqi Liu & Yaonan Wang & Yongpeng Shen, 2016. "Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making," Energies, MDPI, vol. 9(3), pages 1-17, March.
    20. Chang, Yuan & Ries, Robert J. & Wang, Yaowu, 2010. "The embodied energy and environmental emissions of construction projects in China: An economic input-output LCA model," Energy Policy, Elsevier, vol. 38(11), pages 6597-6603, November.
    21. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
    22. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
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    2. Bwo-Ren Ke & Shyang-Chyuan Fang & Jun-Hong Lai, 2022. "Adjustment of bus departure time of an electric bus transportation system for reducing costs and carbon emissions: A case study in Penghu," Energy & Environment, , vol. 33(4), pages 728-751, June.
    3. Sun, Hao & Yang, Jun & Yang, Chao, 2019. "A robust optimization approach to multi-interval location-inventory and recharging planning for electric vehicles," Omega, Elsevier, vol. 86(C), pages 59-75.
    4. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    5. Jing Wang & Heqi Wang & Chunguang Wang, 2023. "Optimal Charging Pile Configuration and Charging Scheduling for Electric Bus Routes Considering the Impact of Ambient Temperature on Charging Power," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    6. Andrzej Łebkowski, 2019. "Studies of Energy Consumption by a City Bus Powered by a Hybrid Energy Storage System in Variable Road Conditions," Energies, MDPI, vol. 12(5), pages 1-39, March.
    7. Zhang, Jie & Bai, Lihui & Jin, Tongdan, 2021. "Joint planning for battery swap and supercharging networks with priority service queues," International Journal of Production Economics, Elsevier, vol. 233(C).

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