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Comparative study of energy management in parallel hybrid electric vehicles considering battery ageing

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  • Zhang, Fengqi
  • Xiao, Lehua
  • Coskun, Serdar
  • Pang, Hui
  • Xie, Shaobo
  • Liu, Kailong
  • Cui, Yahui

Abstract

This article presents a thorough comparative study of energy management strategies (EMSs) for a parallel hybrid electric vehicle (HEV), while the battery ageing is considered. The principle of dynamic programming (DP), Pontryagin's minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) considering battery ageing is elaborated. The gearshift map is obtained from the optimization results in DP to prevent frequent shifts by taking into account drivability and fuel economy, which is then applied in the PMP and ECMS. Comparison of different EMSs is conducted by means of fuel economy, battery state-of-charge charge-sustainability, and computational efficiency. Moreover, battery ageing is included in the optimization solution by utilizing a control-oriented model, aiming to fulfill one of the main cost-related design concerns in the development of HEVs. Through a unified framework, the torque split and battery degradation are simultaneously optimized in this study. Simulations are carried out for DP, PMP, and ECMS to analyze their features, wherein results indicate that DP obtains the best fuel economy compared with other methods. Additionally, the difference between DP and PMP is about 2% in terms of fuel economy. The observations from analysis results provide a good insight into the merits and demerits of each approach.

Suggested Citation

  • Zhang, Fengqi & Xiao, Lehua & Coskun, Serdar & Pang, Hui & Xie, Shaobo & Liu, Kailong & Cui, Yahui, 2023. "Comparative study of energy management in parallel hybrid electric vehicles considering battery ageing," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222001220
    DOI: 10.1016/j.energy.2022.123219
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    References listed on IDEAS

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    2. Chang, Chengcheng & Zhao, Wanzhong & Wang, Chunyan & Luan, Zhongkai, 2023. "An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions," Energy, Elsevier, vol. 283(C).

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