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Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios

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  • Chen, Bin
  • Wang, Miaoben
  • Hu, Lin
  • He, Guo
  • Yan, Haoyang
  • Wen, Xinji
  • Du, Ronghua

Abstract

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model.

Suggested Citation

  • Chen, Bin & Wang, Miaoben & Hu, Lin & He, Guo & Yan, Haoyang & Wen, Xinji & Du, Ronghua, 2024. "Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios," Applied Energy, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:appene:v:365:y:2024:i:c:s0306261924006019
    DOI: 10.1016/j.apenergy.2024.123218
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    1. Xuezhao Zhang & Zijie Chen & Wenxiao Wang & Xiaofen Fang, 2024. "Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network," Energies, MDPI, vol. 17(12), pages 1-21, June.

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