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Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

Author

Listed:
  • Sun, Xiaodong
  • Chen, Zongzhe
  • Han, Shouyi
  • Tian, Xiang
  • Zhijia Jin,
  • Cao, Yunfei
  • Xue, Mingzhou

Abstract

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Suggested Citation

  • Sun, Xiaodong & Chen, Zongzhe & Han, Shouyi & Tian, Xiang & Zhijia Jin, & Cao, Yunfei & Xue, Mingzhou, 2024. "Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224017882
    DOI: 10.1016/j.energy.2024.132014
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    References listed on IDEAS

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    1. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    2. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    3. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    4. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    5. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    6. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
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    Cited by:

    1. Wang, Shuhan & Yao, Kun & Guo, Wei & Xu, Xiangyang & Liu, Yiqiang & Qian, Pengfei & Zhao, Junwei & Dong, Peng, 2025. "Approximate optimal energy management strategy for multi-speed series-parallel PHEV integrating global prediction and real-time control," Energy, Elsevier, vol. 335(C).
    2. Wang, Feng & Chen, Yihang & Zhang, Yuanjian & Zhu, Xiaoyuan & Ni, Yi-Qing, 2025. "Car-following speed collaborative energy management strategy for connected PHEV," Energy, Elsevier, vol. 329(C).
    3. Sun, Xiaodong & Dong, Ziyin & Cai, Yingfeng & Jin, Zhijia & Lei, Gang & Tian, Xiang, 2025. "A comprehensive review of design optimization methods for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).

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