Proton exchange membrane fuel cells (PEMFC) have the advantages of long operation cycles, high energy efficiency and no pollution of reaction products. Temperature is an important factor to ensure the operation of fuel cell systems. Too high temperature will cause irreversible damage to the proton exchange membrane, and too low temperature will greatly reduce the power generation efficiency of fuel cells. Therefore, the effective thermal management temperature control can ensure the stable operation of the system under steady state and dynamic variable load. It can also improve the reaction efficiency of the fuel cell system and prolong the life of the fuel cell. This paper mainly summarized the cooling mode and control strategy of PEMFC based on thermal management system. The application of different cooling methods is further discussed. The characteristics of traditional proportional-integral-derivative (PID) control, fuzzy PID control, predictive control, adaptive control and other common thermal management control strategies were described in detail. The research status of scholars in various countries were analyzed respectively, and the cooling effects of different strategies are compared. Through the review and research on the temperature control of PEMFC, it is found that the stable operation of the stack is inseparable from the coordination of reasonable cooling mode and control strategy.

The temperature prediction of fuel cell thermal management systems (FCTMS) has been the focus of research in fuel cell vehicle. Herein, a prediction model of FCTMS based on the backpropagation neural network is proposed. Predictive models are applied to fuel cell TMSs for predicting temperature changes within the system. First, the fuel cell TMS is established based on Amesim software and verified by using experimental data. Then a prediction model is established based on the simulation data of the system model. After the validation calculation, the highest accuracy of the stack temperature prediction was found, with a relative error of 0.75%. The heat sink outlet temperature prediction accuracy is the worst, with a relative error of 4.3%. The mean square error of the overall output of the prediction model is 0.043, and the mean absolute percentage error of the three results is 0.23%, 0.48%, and 0.16%. Both are below 5%. Therefore, the prediction model has more precise prediction performance, which helps the parameter study and control decision setting of FCTMS.