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LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain

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  • Bao, Shuyue
  • Tang, Shifa
  • Sun, Ping
  • Wang, Tao

Abstract

Recurrent neural networks (RNNs) have been used for vehicle speed prediction, trajectory prediction and state diagnosis. As a variant of RNNs, long short-term memory (LSTM) has better state memory ability to deal with problems that have sequential characteristics. In this study, a neural network with two substructures was developed and LSTM was used as the core part for a power-split hybrid powertrain energy management problem, and the optimal control data obtained from the dynamic programming (DP) algorithm was used as the training set. Then on a vehicle model established on coasting-down test data, LSTM conducted real-time control. This paper analyzes the battery status, fuel consumption, carbon dioxide emissions and operating condition of the engine and motor. The results showed that LSTM has good generalization ability and real-time control capability. In the test driving cycles of CHTC-LT and C-WTVC, the theoretical optimal scheme given by the DP algorithm is only 12.15% and 17.96% lower than the LSTM scheme in terms of fuel consumption. In addition, the influence of network size, training epochs and training set on energy-saving effect is compared in detail, it was found that the relatively optimal model required 128 LSTM layer units and 500 training epochs.

Suggested Citation

  • Bao, Shuyue & Tang, Shifa & Sun, Ping & Wang, Tao, 2023. "LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026610
    DOI: 10.1016/j.energy.2023.129267
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    References listed on IDEAS

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    1. Bao, Shuyue & Sun, Ping & Zhu, Jianxin & Ji, Qian & Liu, Junheng, 2022. "Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 256(C).
    2. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    3. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    4. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    5. Caux, Stéphane & Gaoua, Yacine & Lopez, Pierre, 2017. "A combinatorial optimisation approach to energy management strategy for a hybrid fuel cell vehicle," Energy, Elsevier, vol. 133(C), pages 219-230.
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    Cited by:

    1. Wang, Jun & Cao, Junxing, 2024. "Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning," Energy, Elsevier, vol. 304(C).

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