THERMAL SCIENCE

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Qianqian Ge

,

Cuncun Wei

OPTIMIZATION OF FUEL CELL THERMAL MANAGEMENT SYSTEM BASED ON BACK PROPAGATION NEURAL NETWORK

ABSTRACT
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
PAPER SUBMITTED: 2020-11-16
PAPER REVISED: 2020-12-30
PAPER ACCEPTED: 2021-01-18
PUBLISHED ONLINE: 2021-07-31
DOI REFERENCE: https://doi.org/10.2298/TSCI2104975G
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 4, PAGES [2975 - 2982]
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Optimization of fuel cell thermal management system based on back propagation neural network

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence