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Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene

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  • Li, Cheng
  • Xu, Xiangyang
  • Zhu, Helong
  • Gan, Jiongpeng
  • Chen, Zhige
  • Tang, Xiaolin

Abstract

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy.

Suggested Citation

  • Li, Cheng & Xu, Xiangyang & Zhu, Helong & Gan, Jiongpeng & Chen, Zhige & Tang, Xiaolin, 2024. "Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s036054422400358x
    DOI: 10.1016/j.energy.2024.130586
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    1. 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).

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