A Multi-Objective Temperature Control Method for a Multi-Stack Fuel Cell System with Different Stacks Based on Model Predictive Control

Wei Shen, Hongtao Su (), Jianhua Gao, Lei Fan (), Gang Zhang and Su Zhou
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Wei Shen: School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China
Hongtao Su: School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China
Jianhua Gao: College of Automotive Studies, Tongji University, Shanghai 201804, China
Lei Fan: School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China
Gang Zhang: Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co., Ltd., Shanghai 201805, China
Su Zhou: School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China

Energies, 2025, vol. 18, issue 10, 1-17

Abstract: The multi-stack fuel cell system (MFCS) has advantages such as a wide range, long life, and high efficiency; however, its multiple heat sources impose higher requirements on the thermal management system, especially for different stacks. In order to control each stack temperature in an MFCS, the model predictive control (MPC) algorithm based on the backpropagation (BP) neural network is proposed. Firstly, dynamic characteristics have been obtained experimentally for selected PEMFC stacks of different powers. Based on experimental data, a parallel multi-stack fuel cell thermal management subsystem with different stack powers model is established and a system prediction model of the BP neural network is trained by applying the MFCS thermal management subsystem model simulation data. Then, the step response matrix of the system prediction model is obtained at typical operating conditions, and a dynamic matrix controller (DMC) is designed. Finally, a test operating condition is designed for simulation analysis. The results show that the DMC based on BP neural network can quickly and accurately control each stack temperature of the MFCS, while having the characteristics of small overshoot and short regulation time.

Keywords: PEMFC; multi-stack; parallel thermal management system; model predictive control; neural network; operating condition; dynamic matrix controller (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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