Decoupled safety supervision empowering efficient and safe energy management for fuel cell vehicles
Chunchun Jia, Wei Liu, Hongwen He, K. T. Chau
Abstract
Abstract Simultaneously ensuring operational efficiency and safety of energy systems remains a critical challenge for fuel cell vehicle energy management. Mainstream deep reinforcement learning (DRL) approaches often inadequately address explicit safety constraints, especially concerning lithium-ion battery (LIB) thermal management. This study proposes a safety-guided DRL framework introducing an independent safety-guided network to explicitly and reliably enforce safety constraints. By decoupling safety assurance from objective optimization, our architecture overcomes the mutual interference and reward-tuning difficulties inherent in existing reward-penalty methods. Validated on a fuel cell bus platform, our method outperforms state-of-the-art baselines, improving fuel economy by 8.36% and LIB thermal safety by 10.14% under full-load conditions. Notably, it maintains a zero unsafe duration ratio across real-world scenarios and reduces violation severity by up to 21.88% under extreme thermal conditions. These results demonstrate the proposed method’s robust safety assurance and generalization capability, positioning it as a practical solution for intelligent vehicle energy management.