Energy management and optimization for fuel cell vehicles incorporating deep reinforcement learning and traffic light information

This paper proposes a Soft Actor-Critic algorithm with prioritized experience replay (SAC-PER) based on deep reinforcement learning, which is used for speed planning and energy management for fuel cell vehicles (FCV) through intersection scenarios. A FCV model is established, and two traffic scenarios with 6-signal and 12-signal intersections are built for scenario validation. By optimizing the speed and power allocation of the FCV, we can reduce start-stop events, lower energy consumption, and extend the fuel cell’s lifespan. In the constructed simulation scenario, a comparison with traditional hierarchical optimization methods shows that the SAC-PER algorithm strategy can reduce energy consumption by at least 48.32% and decrease fuel cell degradation by at least 38.75%. These findings are significant for improving energy utilization efficiency, reducing hydrogen consumption, and extending fuel cell lifespan in FCV.