燃料电池汽车跟车速度控制与能量管理分层优化OA

With the advances of autonomous driving and hydrogen fuel cell technology,energy management strategies for fuel cell vehicles in internet-connected environments have garnered keen academic attention.To address the eco-driving challenges of fuel cell vehicles in car-following scenarios,this paper proposes a hierarchical optimization solution(SAC-DP).The upper-level car-following speed control is realized by employing deep reinforcement learning,integrating multiple objectives of the driving process into the reward function to enhance following efficiency and comfort while ensuring safety.The lower-level energy management is achieved by employing dynamic programming,aiming to reduce hydrogen consumption and fuel cell degradation,thus improving overall efficiency.Results from two simulation scenarios indicate ride comfort improves by≥27.26%,safety by≥21.66%,following efficiency by≥10.08%,hydrogen consumption decreased by≥4.13%,and fuel cell degradation reduced by≥54.45%compared to two other strategies(Krauss-DP and CACC-DP).