重庆理工大学学报2026，Vol.40Issue(3)：10-18,9.DOI:10.3969/j.issn.1674-8425(z).2026.02.002
燃料电池汽车跟车速度控制与能量管理分层优化
Hierarchical optimization of car-following speed control and energy management for fuel cell vehicles
摘要
Abstract
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).关键词
生态驾驶/分层优化/燃料电池汽车/深度强化学习/跟车场景Key words
eco-driving/hierarchical optimization/fuel cell vehicles/deep reinforcement learning/car-following scenario分类
交通工程引用本文复制引用
张玉坤,霍为炜,龚国庆,罗通强..燃料电池汽车跟车速度控制与能量管理分层优化[J].重庆理工大学学报,2026,40(3):10-18,9.基金项目
国家自然科学基金面上项目(52077007) （52077007）
