Deep Reinforcement Learning Algorithm Based on Fusion Optimization for Fuel Cell Gas Supply System Control

• Published in: World electric vehicle journal Vol. 14; no. 2; p. 50
• Main Authors: Yuan, Hongyan, Sun, Zhendong, Wang, Yujie, Chen, Zonghai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Algorithms; Control algorithms; Controllers; Deep learning; deep reinforcement learning; Dynamic response; Efficiency; Energy resources; Flow control; Fuel cells; fusion optimization; gas supply system control; Gases; Humidity; Hydrogen; Machine learning; Methods; Neural networks; Optimization; Process controls; proton exchange membrane fuel cell (PEMFC); Proton exchange membrane fuel cells; Response time (computers)
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj14020050

In a proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning controller with continuous state based on fusion optimization (FO-DDPG) and a control optimization strategy based on net power optimization are proposed in this paper, and the effects of whether the two gas controls are decoupled or not are compared. The experimental results show that the undecoupled FO-DDPG algorithm has a faster dynamic response and more stable static performance compared to the fuzzy PID, DQN, traditional DRL algorithm, and decoupled controllers, demonstrated by a dynamic response time of 0.15 s, an overshoot of less than 5%, and a steady-state error of 0.00003.