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Integrated energy management of hybrid power supply based on short-term speed prediction

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  • Wu, Gang
  • Wang, Chunyan
  • Zhao, Wanzhong
  • Meng, Qikang

Abstract

The energy management strategy and output limitation of energy storage system affect the actual regenerative braking recovery. In order to optimize the performance and energy efficiency of vehicle energy storage system in the process of braking energy recovery, an integrated energy management strategy based on short-term speed prediction is proposed in this paper. Firstly, support vector machine classification method is used for off-line training and on-line recognition of driving patterns, and then input-output hidden Markov model and Gaussian mixture model are fused to establish speed prediction models under urban, suburban, and high-speed conditions for real-time prediction of speed sequences. Then, the power is distributed by the dynamic programming algorithm according to the short-term speed prediction results, and the distribution coefficient is corrected through the integrated strategy of braking force distribution and power distribution. Finally, the simulation results under continuously changing driving conditions show that the pattern recognition with changeable span is conducive to improving its accuracy during the transition period, and the proposed integrated energy management strategy is helpful to optimizing the performance of the hybrid energy storage system. The renewable energy utilization efficiency index indicates the efficiency of the integrated energy management strategy.

Suggested Citation

  • Wu, Gang & Wang, Chunyan & Zhao, Wanzhong & Meng, Qikang, 2023. "Integrated energy management of hybrid power supply based on short-term speed prediction," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222025063
    DOI: 10.1016/j.energy.2022.125620
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    References listed on IDEAS

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    1. Zhu, Yueying & Wu, Hao & Zhen, Chengcong, 2021. "Regenerative braking control under sliding braking condition of electric vehicles with switched reluctance motor drive system," Energy, Elsevier, vol. 230(C).
    2. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    3. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
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