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Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness

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  • Du, Jiuyu
  • Chen, Jingfu
  • Song, Ziyou
  • Gao, Mingming
  • Ouyang, Minggao

Abstract

Energy management strategy and battery capacity are the primary factors for the energy efficiency of range-extended electric buses (REEBs). To improve the energy efficiency of REEBs developed by Tsinghua University, an optimal design method of global optimization-based strategy is investigated. It is real-time and adaptive to variable traction battery capacities of series REEBs. For simulation, the physical model of REEB and key components are established. The optimal strategy is first extracted by the power split ratio (PSR) from REEB simulation result with dynamic program (DP) algorithm. The power distribution map is obtained by series simulations for variable battery capacity options. The control law for developing optimal strategy are achieved by cluster regression for power distribution data. To verify the effect of the proposed energy management strategy, characteristics of powertrain, energy efficiency, operating cost, and computing time are ultimately analyzed. Simulation results show that the energy efficiency of the global optimization-based strategy presented in this paper is similar to that of the DP strategy. Therefore, the overall energy efficiency can be significantly improved compared with that of the CDCS strategy, and operating costs can be substantially reduced. The feasibility of candidate control strategies is thereby assessed via the employment of variable parameters.

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  • Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    • Handle: RePEc:eee:energy:v:121:y:2017:i:c:p:32-42
    DOI: 10.1016/j.energy.2016.12.120
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    as
    1. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    2. Honarmand, Masoud & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition," Energy, Elsevier, vol. 65(C), pages 572-579.
    3. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    4. Hellgren, Jonas, 2007. "Life cycle cost analysis of a car, a city bus and an intercity bus powertrain for year 2005 and 2020," Energy Policy, Elsevier, vol. 35(1), pages 39-49, January.
    5. 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.
    6. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    7. Xu, Liangfei & Ouyang, Minggao & Li, Jianqiu & Yang, Fuyuan & Lu, Languang & Hua, Jianfeng, 2013. "Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost," Applied Energy, Elsevier, vol. 103(C), pages 477-487.
    8. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    9. Redelbach, Martin & Özdemir, Enver Doruk & Friedrich, Horst E., 2014. "Optimizing battery sizes of plug-in hybrid and extended range electric vehicles for different user types," Energy Policy, Elsevier, vol. 73(C), pages 158-168.
    10. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    11. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    12. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
    13. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    14. Shiau, Ching-Shin Norman & Samaras, Constantine & Hauffe, Richard & Michalek, Jeremy J., 2009. "Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles," Energy Policy, Elsevier, vol. 37(7), pages 2653-2663, July.
    Full references (including those not matched with items on IDEAS)

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    13. Wu, Zhou & Ling, Rui & Tang, Ruoli, 2017. "Dynamic battery equalization with energy and time efficiency for electric vehicles," Energy, Elsevier, vol. 141(C), pages 937-948.
    14. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    15. Hong Zhang & Zhuang Xing & Jiajian Song & Qiangqiang Yang, 2018. "Development and Test Application of an Auxiliary Power-Integrated System," Energies, MDPI, vol. 11(1), pages 1-18, January.
    16. Lihe Xi & Xin Zhang & Chuanyang Sun & Zexing Wang & Xiaosen Hou & Jibao Zhang, 2017. "Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network," Energies, MDPI, vol. 10(11), pages 1-18, November.
    17. Loïc Joud & Rui Da Silva & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2020. "Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction," Energies, MDPI, vol. 13(11), pages 1-17, June.
    18. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    19. Salman, Waleed & Qi, Lingfei & Zhu, Xin & Pan, Hongye & Zhang, Xingtian & Bano, Shehar & Zhang, Zutao & Yuan, Yanping, 2018. "A high-efficiency energy regenerative shock absorber using helical gears for powering low-wattage electrical device of electric vehicles," Energy, Elsevier, vol. 159(C), pages 361-372.
    20. García-Vázquez, Carlos A. & Llorens-Iborra, Francisco & Fernández-Ramírez, Luis M. & Sánchez-Sainz, Higinio & Jurado, Francisco, 2017. "Comparative study of dynamic wireless charging of electric vehicles in motorway, highway and urban stretches," Energy, Elsevier, vol. 137(C), pages 42-57.
    21. Qiu, Chengqun & Wang, Guolin & Meng, Mingyu & Shen, Yujie, 2018. "A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations," Energy, Elsevier, vol. 149(C), pages 329-340.
    22. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    23. Liu, Teng & Wang, Bo & Yang, Chenglang, 2018. "Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning," Energy, Elsevier, vol. 160(C), pages 544-555.

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