A Soft Actor-Critic-Based Energy Management Strategy for Fuel Cell Vehicles Considering Fuel Cell Degradation

Energy management strategies (EMSs) play a critical role in improving both the efficiency and durability of fuel cell electric vehicles (FCEVs). To overcome the limited adaptability and insufficient durability consideration of existing deep reinforcement learning-based EMSs, this study develops a degradation-aware energy management strategy based on the Soft Actor–Critic (SAC) algorithm. By leveraging SAC’s maximum-entropy framework, the proposed method enhances exploration efficiency and avoids premature convergence to operating patterns that are unfavorable to fuel cell durability. A reward function explicitly penalizing hydrogen consumption, power fluctuation, and degradation-related operating behaviors is designed, and the influences of reward weighting and key hyperparameters on learning stability and performance are systematically analyzed. The proposed SAC-based EMS is evaluated against Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) strategies under both training and unseen driving cycles. Simulation results demonstrate that SAC achieves a superior and robust trade-off between hydrogen economy and degradation mitigation, maintaining improved adaptability and durability under varying operating conditions. These findings indicate that integrating degradation awareness with entropy-regularized reinforcement learning provides an effective framework for practical EMS design in FCEVs.