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<p>Battery state-of-charge (SOC) planning utilizing traffic information from the intelligent transportation system (ITS) and the Internet of Vehicles is promising to improve the performance of the connected plug-in hybrid electric vehicle (PHEV) energy management strategy. In this paper, a novel reference SOC planning method based on traffic prediction was proposed. A data-driven spatio-temporal graph convolutional neural network model with adaptive graph generation module (A-STGCN) was proposed to predict the traffic speed of road networks. Based on the predicted traffic speed, the long-term global speed profile can be predicted. A dynamic programming (DP)-oriented simplified vehicle power balance model was established and the global reference SOC trajectory was planned through online DP algorithm. Finally, the proposed SOC planning algorithm was combined with an adaptive equivalent consumption minimization strategy (A-ECMS) and verified through simulation. Simulation results using real world traffic data illustrate that A-ECMS combined with the proposed reference SOC planning method achieves 98.52% of fuel economy of the off-line global optimal solution, which outperforms other reference SOCs.</p>

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Traffic Prediction Based Battery State-of-Charge Planning for Connected Plug-in Hybrid Electric Vehicle

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Dongwei Yao,
  • Fanlong Zeng,
  • Xinwei Lu,
  • Junhao Shen,
  • Kaiyang Huang,
  • Feng Wu

摘要

Battery state-of-charge (SOC) planning utilizing traffic information from the intelligent transportation system (ITS) and the Internet of Vehicles is promising to improve the performance of the connected plug-in hybrid electric vehicle (PHEV) energy management strategy. In this paper, a novel reference SOC planning method based on traffic prediction was proposed. A data-driven spatio-temporal graph convolutional neural network model with adaptive graph generation module (A-STGCN) was proposed to predict the traffic speed of road networks. Based on the predicted traffic speed, the long-term global speed profile can be predicted. A dynamic programming (DP)-oriented simplified vehicle power balance model was established and the global reference SOC trajectory was planned through online DP algorithm. Finally, the proposed SOC planning algorithm was combined with an adaptive equivalent consumption minimization strategy (A-ECMS) and verified through simulation. Simulation results using real world traffic data illustrate that A-ECMS combined with the proposed reference SOC planning method achieves 98.52% of fuel economy of the off-line global optimal solution, which outperforms other reference SOCs.