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

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.