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The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses

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  • Song, Ziyou
  • Hou, Jun
  • Xu, Shaobing
  • Ouyang, Minggao
  • Li, Jianqiu

Abstract

This paper analyzes the influence of different driving cycles on the integrated optimization of hybrid energy storage system, including the optimization of supercapacitor size and energy management strategy for the electric vehicle application. The driving cycle is divided into micro-trips, and a fuzzy pattern recognition algorithm is proposed to distinguish different micro-trips within a driving cycle. The intensity factor indicates how intensely the micro-trip drains energy from the hybrid energy storage system. The distribution of each driving cycle is analyzed by the probability density function. The integrated optimization of the hybrid energy storage system is conducted based on four driving cycles. Simulation results show that for different driving cycles, the optimal supercapacitor size and the on-line energy management strategy are directly determined by the maximum intensity factor. The driving cycles with similar maximum intensity factors can use same amount of supercapacitor modules and employ the same on-line energy management strategy. Therefore, the optimization results can be easily generalized to practical bus lines.

Suggested Citation

  • Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:91-100
    DOI: 10.1016/j.energy.2017.06.096
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    10. Papa, Gregor & Santo Zarnik, Marina & Vukašinović, Vida, 2022. "Electric-bus routes in hilly urban areas: Overview and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    11. Ruan, Jiageng & Song, Qiang & Yang, Weiwei, 2019. "The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle," Energy, Elsevier, vol. 183(C), pages 315-330.
    12. Lu, Ziwang & Tian, He & sun, Yiwen & Li, Runfeng & Tian, Guangyu, 2023. "Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    13. Liu, Huimin & Lin, Cheng & Yu, Xiao & Tao, Zhenyi & Xu, Jiaqi, 2024. "Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle," Applied Energy, Elsevier, vol. 365(C).
    14. Xu, Bin & Wang, Hanchen, 2023. "A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods," Energy, Elsevier, vol. 269(C).
    15. Ghosh, Sourav & Yadav, Sarita & Devi, Ambika & Thomas, Tiju, 2022. "Techno-economic understanding of Indian energy-storage market: A perspective on green materials-based supercapacitor technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    17. Bhattacharjee, Debraj & Ghosh, Tamal & Bhola, Prabha & Martinsen, Kristian & Dan, Pranab K., 2019. "Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance," Energy, Elsevier, vol. 183(C), pages 235-248.
    18. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    19. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    20. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. Zhang, Feitie & Yang, Fuyuan & Xue, Dianlun & Cai, Yuanchun, 2019. "Optimization of compound power split configurations in PHEV bus for fuel consumption and battery degradation decreasing," Energy, Elsevier, vol. 169(C), pages 937-957.
    22. Tian, He & Li, Shengbo Eben & Wang, Xu & Huang, Yong & Tian, Guangyu, 2018. "Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus," Energy, Elsevier, vol. 142(C), pages 55-67.
    23. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
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    25. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal scheduling for electric bus fleets based on dynamic programming approach by considering battery capacity fade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

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