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<p>To address the challenges of energy efficiency and multi-objective coordinated control in connected automated range-extended electric vehicle (CAR-EEV), a hierarchical energy management strategy based on pre-deceleration is proposed. A connected traffic environment comprising multiple lanes, junctions and fixed-cycle traffic lights is established based on simulation of urban mobility and Simulink. Focused on single-vehicle scenarios and car-following scenarios involving interfering vehicles, a dual-layer control strategy is developed: in the upper layer, the target vehicle speed is optimized through a pre-deceleration control algorithm, while in the lower-layer, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed to achieve real-time power distribution. Simulations and bench tests demonstrate that the speed curve is significantly smoothed, and the number of stops at red lights is significantly reduced by the upper-layer algorithm. In the lower layer, the improved TD3 algorithm has a faster convergence rate and better stability, achieving balanced performance close to the dynamic programming benchmark in terms of fuel economy, battery state of charge maintenance and battery life preservation under the evaluated scenarios. In summary, this study provides a well-structured and effective hierarchical energy management strategy for CAR-EEV, laying a solid foundation for the future development of integrated vehicle–road-cloud energy-saving control systems.</p>

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Research on hierarchical energy management strategies for connected automated range-extended electric vehicle based on pre-deceleration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Boxing Wang,
  • Hanwu Liu,
  • Jianjun Tang,
  • Wencai Sun,
  • Song Wang,
  • Kaicheng Yang

摘要

To address the challenges of energy efficiency and multi-objective coordinated control in connected automated range-extended electric vehicle (CAR-EEV), a hierarchical energy management strategy based on pre-deceleration is proposed. A connected traffic environment comprising multiple lanes, junctions and fixed-cycle traffic lights is established based on simulation of urban mobility and Simulink. Focused on single-vehicle scenarios and car-following scenarios involving interfering vehicles, a dual-layer control strategy is developed: in the upper layer, the target vehicle speed is optimized through a pre-deceleration control algorithm, while in the lower-layer, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed to achieve real-time power distribution. Simulations and bench tests demonstrate that the speed curve is significantly smoothed, and the number of stops at red lights is significantly reduced by the upper-layer algorithm. In the lower layer, the improved TD3 algorithm has a faster convergence rate and better stability, achieving balanced performance close to the dynamic programming benchmark in terms of fuel economy, battery state of charge maintenance and battery life preservation under the evaluated scenarios. In summary, this study provides a well-structured and effective hierarchical energy management strategy for CAR-EEV, laying a solid foundation for the future development of integrated vehicle–road-cloud energy-saving control systems.