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Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles

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  • Lu, Ziwang
  • Tian, He
  • sun, Yiwen
  • Li, Runfeng
  • Tian, Guangyu

Abstract

The neural network energy management strategy can be implemented online and effectively save the energy for the plug-in hybrid electric vehicles (PHEVs). However, the selection of input features and application to untrained driving cycles are two issues faced by the strategy. This paper proposes an energy management strategy with the optimal input features for the PHEV. The global optimal datasets are first obtained based on the dynamic programming (DP) algorithm. Then the random forest classification models are trained with different combinations of input features to select the input feature combination with the highest classification accuracy. A neural network with the selected input features is finally trained using the optimal datasets for online control. With the optimal input features, the strategy can be adopted to both trained driving cycles and untrained driving cycles. Results demonstrate that the proposed strategy can save 4.44% to 7.75% energy compared to the charge depleting and charge sustaining strategy on trained cycles and 3.80% to 7.70% on untrained cycles respectively. The efficiency of the proposed strategy is only less than 2.41% worse than the DP algorithm.

Suggested Citation

  • Lu, Ziwang & Tian, He & sun, Yiwen & Li, Runfeng & Tian, Guangyu, 2023. "Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027937
    DOI: 10.1016/j.energy.2023.129399
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    References listed on IDEAS

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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    3. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    4. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    5. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    6. Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    7. Yu, Huilong & Tarsitano, Davide & Hu, Xiaosong & Cheli, Federico, 2016. "Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system," Energy, Elsevier, vol. 112(C), pages 322-331.
    8. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    9. 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.
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