Data‐driven optimal PEMFC temperature control via curriculum guidance strategy‐based large‐scale deep reinforcement learning

Abstract

Abstract As the proton exchange membrane fuel cell (PEMFC) is a nonlinear, time‐varying, multiple‐input multiple‐output system, an advanced controller with strong robustness and adaptability is required for controlling PEMFC stack temperature and achieve a high operation efficiency. In this paper, a data driven optimal controller is proposed for controlling the stack temperature, which is based on large‐scale deep reinforcement learning. In addition, a new deep reinforcement learning algorithm termed curriculum guidance strategy large‐scale dual‐delay deep deterministic policy gradient (CGS‐L4DPG) algorithm is proposed for this controller. The design of this algorithm introduces the concepts of the curriculum guidance strategy and imitation learning, and its inclusion improves the performance and robustness of the proposed controller. The simulation results show that, taking advantage of the high adaptability and robustness of CGS‐L4DPG algorithm, the proposed controller can more effectively control the PEMFC stack temperature than existing control algorithms.