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HEV power management control strategy for urban driving

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  • Wei, Zhen
  • Xu, John
  • Halim, Dunant

Abstract

The aim of this paper is to further improve the fuel efficiency and keep the balance of battery SOC simultaneously during urban driving. A dynamic programming (DP)-optimized HEV power management control strategy is proposed. The DP algorithm is applied to obtain the optimal engine/motor power distribution and utilized for the design of the fuzzy control strategy. The traditional DP algorithm is modified with the consideration of SOC balance for HEVs. In the analysis of DP simulation results, rules of torque slip behaviors have been found, which are directly utilized in the design of fuzzy control strategy. In order to improve the practicality of the control strategy to meet the diversities of city driving patterns, an urban driving pattern recognition method is presented. To evaluate the control performance, the proposed control strategy is also compared with the conventional rule-based strategy. The simulation results indicate that by adopting the proposed strategy the fuel efficiency of HEV is improved, and the SOC of the battery is kept in balance during different urban driving cycles.

Suggested Citation

  • Wei, Zhen & Xu, John & Halim, Dunant, 2017. "HEV power management control strategy for urban driving," Applied Energy, Elsevier, vol. 194(C), pages 705-714.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:705-714
    DOI: 10.1016/j.apenergy.2016.10.023
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    References listed on IDEAS

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    1. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
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    Cited by:

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    2. Zou, Runnan & Fan, Likang & Dong, Yanrui & Zheng, Siyu & Hu, Chenxing, 2021. "DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle," Energy, Elsevier, vol. 225(C).
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    4. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    5. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    6. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    7. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.
    8. Fan, Likang & Wang, Jun & Peng, Yiqiang & Sun, Hongwei & Bao, Xiuchao & Zeng, Baoquan & Wei, Hongqian, 2024. "Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles," Energy, Elsevier, vol. 298(C).
    9. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).

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