TY - JOUR
TI - Optimization of fuel cell thermal management system based on back propagation neural network
AU - Ge Qianqian
AU - Wei Cuncun
JN - Thermal Science
PY - 2021
VL - 25
IS - 4
SP - 2975
EP - 2982
PT - Article
AB - Two thermal management control strategies, namely flow following current and  power mode and back propagation neural network auto-disturbance rejection  method, were proposed to solve significant temperature fluctuation problems,  long regulation time, and slow response speed in fuel cell thermal  management system variable load. The results show that the flow following  current and power control strategy can effectively weaken the coupling  effect between pump and radiator fan and significantly reduce the overshoot  and adjustment time of inlet and outlet cooling water temperature and  temperature difference reactor. Although the control effect of the neural  network and strategy is insufficient under maximum power, the overall  control effect is better than that of the flow following the current control  strategy.