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Health-conscious predictive energy management strategy with hybrid speed predictor for plug-in hybrid electric vehicles: Investigating the impact of battery electro-thermal-aging models

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Listed:
  • Han, Jie
  • Liu, Wenxue
  • Zheng, Yusheng
  • Khalatbarisoltani, Arash
  • Yang, Yalian
  • Hu, Xiaosong

Abstract

Improving plug-in hybrid electric vehicles (PHEVs) fuel economy requires a proper energy management strategy (EMS). Efforts to enhance the energy-saving performance of predictive EMSs have concentrated on advanced speed prediction methods. However, the impact of battery models on the predictive EMS hasn't been investigated. This paper aims to fill the research gap by suggesting a model predictive control-based (MPC) EMS framework that uses a hybrid speed predictor. Firstly, six control-oriented battery electro-thermal-aging models with various terminal voltage and temperature simulation accuracies have been developed and verified. Secondly, it also examines the effects of different battery models on MPC-based EMS through quantitative analysis, including battery dynamics, calculation time, and resultant operating costs, which leads to the suggestions of model selection for the design of the EMS under low- and room-temperature driving scenarios. Finally, a novel hybrid speed prediction model is proposed, where the historical speed is decomposed into strongly periodic intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and backpropagation (BP) neural network is utilized to learn the feature parameter and mapping relationship of each IMF. In addition, a case study is conducted by applying the proposed speed prediction model in an MPC-based EMS method. The simulation results highlight that the proposed hybrid speed prediction model can achieve preferable speed prediction. The operating cost errors (compared with the MPC-based EMS with 100% speed prediction) are reduced to 0.4% and 0.98% at −20 °C and 25 °C driving scenarios, respectively.

Suggested Citation

  • Han, Jie & Liu, Wenxue & Zheng, Yusheng & Khalatbarisoltani, Arash & Yang, Yalian & Hu, Xiaosong, 2023. "Health-conscious predictive energy management strategy with hybrid speed predictor for plug-in hybrid electric vehicles: Investigating the impact of battery electro-thermal-aging models," Applied Energy, Elsevier, vol. 352(C).
  • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013508
    DOI: 10.1016/j.apenergy.2023.121986
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    References listed on IDEAS

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    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    2. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    3. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Zhang, Xiaowu & Ouyang, Minggao, 2015. "The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis," Applied Energy, Elsevier, vol. 159(C), pages 576-588.
    4. Li, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    5. 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.
    6. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    7. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
    8. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    9. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    10. Wu, Yue & Huang, Zhiwu & Hofmann, Heath & Liu, Yongjie & Huang, Jiahao & Hu, Xiaosong & Peng, Jun & Song, Ziyou, 2022. "Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios," Energy, Elsevier, vol. 251(C).
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