Hierarchical thermal management for PEM fuel cell with machine learning approach

Abstract

Thermal management is crucial for the mass transport and water balance of proton exchange membrane fuel cell (PEMFC). Inspired by this, a hierarchical thermal management strategy (TMS) is proposed for fuel cell hybrid electric vehicle (FCHEV). In particular, the transient TMS demands are determined by a well-designed energy management strategy (EMS) taking health and thermal safety into consideration. Furthermore, along with the high-efficiency heat dissipation, a hydrogen consumption minimization strategy (HCMS) is proposed via optimal temperature tracking, which investigates the desirable trace offline. These parallel strategies are incorporated through the deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) algorithm. With the help of its self-adaptive ability, DDPG deals with the complicated TRS problem in multidimensional coupled cooling system, through a mutually updated actor-critic framework. Results suggest the superiority and reliability of proposed TMS with respect to the stack efficiency, fuel economy and tracking performance.