Thermal Science 2021 Volume 25, Issue 4 Part B, Pages: 2975-2982
https://doi.org/10.2298/TSCI2104975G
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Optimization of fuel cell thermal management system based on back propagation neural network
Ge Qianqian (School of Electronics and Information, Zhejiang Business Technology Institute, Ningbo, China), 28272154@qq.com
Wei Cuncun (School of Electronics and Information, Zhejiang Business Technology Institute, Ningbo, China)
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.
Keywords: control strategy, back propagation neural network, thermal management system, fuel cell