Modeling and control of PEMFC air supply system based on T-S fuzzy theory and predictive control. (1st December 2019)

• Record Type: Journal Article
• Authors: Yang, Duo; Pan, Rui; Wang, Yujie; Chen, Zonghai
• Is Part Of: Energy. Volume 188(2019)
• Publication Date: 2019-12-01
• Subjects: Proton exchange membrane fuel cell -- Air flow control -- Takagi-Sugeno fuzzy model -- Generalized predictive control; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116078
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The proton exchange membrane fuel cell has become the most widely used fuel cell in fuel cell vehicles. An effective and accurate control approach for its air supply system is crucial to ensure the performance and safety of the fuel cell system. In order to ensure safe and efficient operation of the air supply, this paper provides a novel modeling and control method based on Takagi-Sugeno fuzzy theory and predictive control. A local controlled autoregressive integrated moving average model for the air flow control is put forward, then the control-oriented T-S model is designed based on multi-model scheduling. The controller architecture is based on a fuzzy generalized predictive controller. The proposed controller can control the oxygen excess ratio in the ideal range and effectively suppress the fluctuation caused by the load change. In addition, an optimal control strategy is proposed aiming at avoiding the oxygen starvation and maximizing the system net power. According to the control results, the proposed method is proved to be able to accurately control the air supply at desire values. It enhances system output performance by fast response to better support the vehicle load variation, and improving the net power and system energy efficiency. Highlights: The CARIMA model for air flow local characteristic analysis is obtained. A novel control-oriented T-S fuzzy model of the air supply system is put forward. A fuzzy generalized predictive controller is proposed for the air flow control. The optimal control objective is designed based on net power optimization. Comparison with other methods shows the superiority of the proposed method.