Data-Driven Control for Proton Exchange Membrane Fuel Cells: Method and Application

• Main Authors: Jiawen Li, Kedong Zhu, Tao Yu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-09-01
• Series: Frontiers in Energy Research
• Subjects: proton exchange membrane fuel cells; PID controller; data-driven optimal control; double-delay deep deterministic policy gradient; control system
• Online Access: https://www.frontiersin.org/articles/10.3389/fenrg.2021.748782/full

A data-driven optimal control method for an air supply system in proton exchange membrane fuel cells (PEMFCs) is proposed with the aim of improving the PEMFC net output power and operational efficiency. Moreover, a marginal utility-based double-delay deep deterministic policy gradient (MU-4DPG) algorithm is proposed as a an offline tuner for the PID controller. The coefficients of the PID controller are rectified and optimized during training in order to enhance the controller’s performance. The design of the algorithm draws on the concept of marginal effects in Economics, in that the algorithm continuously switches between different forms of exploration noise during training so as to increase the diversity of samples, improve exploration efficiency and avoid Q-value overfitting, and ultimately improve the robustness of the algorithm. As detailed below, the effectiveness of the control method has been experimentally demonstrated.