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

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Listed:
  • 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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    1. 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.
    2. 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.
    3. Zhang, Fengqi & Hu, Xiaosong & Langari, Reza & Wang, Lihua & Cui, Yahui & Pang, Hui, 2021. "Adaptive energy management in automated hybrid electric vehicles with flexible torque request," Energy, Elsevier, vol. 214(C).
    4. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    5. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    6. 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.
    7. Yang, Yalian & Pei, Huanxin & Hu, Xiaosong & Liu, Yonggang & Hou, Cong & Cao, Dongpu, 2019. "Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach," Energy, Elsevier, vol. 166(C), pages 929-938.
    8. Qin, Yechen & Tang, Xiaolin & Jia, Tong & Duan, Ziwen & Zhang, Jieming & Li, Yinong & Zheng, Ling, 2020. "Noise and vibration suppression in hybrid electric vehicles: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Suri, Girish & Onori, Simona, 2016. "A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries," Energy, Elsevier, vol. 96(C), pages 644-653.
    10. 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).
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