Collaborative optimization of car-following control and energy management for PHEBs based on deep reinforcement learning

To address the synergistic optimization of car-following control and energy management, this study proposes a collaborative optimization framework based on deep reinforcement learning (DRL). At the car-following control level, a predictive cruise control (PCC) model is developed using the twin delayed deep deterministic policy gradient (TD3) algorithm, which incorporates safety and passenger comfort and power demand into the reward function. At the energy management level, a TD3-based energy management strategy (EMS) is formulated, incorporating constraints on battery state of health (SOH) degradation, temperature violations, state of charge (SOC) fluctuations, and comprehensive operating costs. Simulation results demonstrate that, compared to the traditional hierarchical optimization framework, the proposed strategy achieves significant improvements in terms of mean absolute jerk, root-mean-square (RMS) value of acceleration, power demand, battery SOH degradation, battery temperature violation, and comprehensive operating cost, with optimization rates of 32.7%, 49.55%, 5.39%, 28.30%, 91.76%, and 22.82%, respectively. Furthermore, generalization validation indicates that the proposed framework maintains strong robustness and adaptability under unknown driving conditions.