Multi-Objective Energy Management Strategy for Fuel Cell Trucks Using DP-Pretrained SAC

In the development of fuel cell hybrid electric trucks, the energy management strategy (EMS) plays a key role in enhancing system efficiency and operational economy. The objectives of the EMS include not only minimizing hydrogen consumption but also extending the powertrain system's lifespan to support long-term sustainable commercial operation. This paper proposes a multi-objective energy management framework that integrates Dynamic Programming (DP) and the Soft ActorCritic (SAC) algorithm. Specifically, DP is utilized offline to generate optimal energy allocation trajectories, which are then used to initialize the SAC replay buffer and thereby warm start the training process. Furthermore, a multi-objective reward function is designed to jointly optimize hydrogen consumption and the degradation of both the fuel cell and lithium-ion battery systems. Simulation results show that the proposed strategy incurs only 2.86% higher total cost compared to the DP baseline, while achieving 5.11% and 23.74% lower costs than the standard SAC and rule-based strategies, respectively. In addition, the training time is reduced by 50.64% compared to the standard SAC method, confirming the effectiveness of the pretrained SAC approach in accelerating convergence and delivering highperformance energy management.