基于APSO-BP-PID控制的质子交换膜燃料电池热管理系统温度控制

doi:10.13229/j.cnki.jdxbgxb.20231068

吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (9): 2401-2413.doi: 10.13229/j.cnki.jdxbgxb.20231068

• 车辆工程·机械工程 •

基于APSO-BP-PID控制的质子交换膜燃料电池热管理系统温度控制

商蕾¹(),杨萍²,杨祥国¹(),潘建欣³,杨军³,张梦如¹

^1.武汉理工大学船海与能源动力工程学院，武汉 430063
^2.武汉理工大学交通与物流工程学院，武汉 430063
^3.武汉氢能与燃料电池产业技术研究院有限公司技术研发中心，武汉 430064

收稿日期:2023-10-09 出版日期:2024-09-01 发布日期:2024-10-28
通讯作者: 杨祥国 E-mail:shanglei@whut.edu.cn;yangxiangguo123@163.com
作者简介:商蕾（1974-），女，教授，博士.研究方向：燃料电池热管理，系统仿真与控制.E-mail：shanglei@whut.edu.cn
基金资助:
国家重点研发计划项目(2023YFB4301704);电磁能技术全国重点实验室开放基金项目(61422172220403)

Temperature control of proton exchange membrane fuel cell thermal management system based on APSO-BP-PID control strategy

Lei SHANG¹(),Ping YANG²,Xiang-guo YANG¹(),Jian-xin PAN³,Jun YANG³,Meng-ru ZHANG¹

^1.School of Naval Architecture，Ocean and Energy Power Engineering，Wuhan University of Technology，Wuhan 430063，China
^2.School of Transportation and Logistics Engineering，Wuhan University of Technology，Wuhan 430063，China
^3.Technology R&D Center，Wuhan Institute of Hydrogen and Fuel Cell Industrial Technology Co. ，Ltd. ，Wuhan 430064，China

Received:2023-10-09 Online:2024-09-01 Published:2024-10-28
Contact: Xiang-guo YANG E-mail:shanglei@whut.edu.cn;yangxiangguo123@163.com

摘要/Abstract

摘要：

针对质子交换膜燃料电池热管理系统存在响应速度慢、系统振荡、温度波动大及强耦合性等问题，本文提出了一种冷却液流量跟随电流控制，基于自适应粒子群算法优化神经网络的PID控制策略，并在Matlab/Simulink平台搭建燃料电池堆功率为125 kW质子交换膜燃料电池热管理系统，用于分析各零部件之间的流量分配和热量交换，在不同工况下与神经网络优化的PID控制策略和传统PID控制策略进行对比。仿真结果表明：在不同工况下，本文提出的控制策略能实现散热风扇和循环水泵的解耦；在阶跃信号测试下，实现循环水泵流量跟随电流快速响应；在动态性能测试下，实现无超调且在30 s内稳定，减小系统的振荡量，减轻燃料电池堆进出口冷却液温差和燃料电池堆电压的波动程度。结果说明该控制策略具有良好的控制性能。

关键词: 动力机械工程, 燃料电池, 神经网络, 热管理系统, 控制策略

Abstract:

Aiming at solving the issues of slow response， system oscillation， large temperature fluctuation and strong coupling of the proton exchange membrane fuel cell thermal management system， this paper proposes a coolant flow following the current control， optimizes the PID control strategy of neural network based on adaptive particle swarm algorithm， and constructs a 125 kW proton exchange membrane fuel cell thermal management system on the Matlab/Simulink platform to analyze the flow distribution and heat exchange between components. Compared with PID control strategy optimized by neural network and traditional PID control strategy under different working conditions. -The simulation results show that the control strategy proposed in this paper can realize the decoupling of cooling fan and circulating water pump under different working conditions； Under the step signal test， the flow rate of the circulating water pump follows the current and responds quickly； Under the dynamic performance test， the air volume control of the cooling fan is achieved without overshoot and stable within 30 s， reducing the oscillation of the system， and both the temperature difference between the inlet and outlet coolant of the fuel cell stack and the fluctuation degree of the fuel cell stack voltage is decreased. The results indicate that the proposed control strategy achieves satisfying control performance.

Key words: power mechanical engineering, fuel cell, neural network, thermal management system, control strategy

中图分类号:

TM911.4

商蕾,杨萍,杨祥国,潘建欣,杨军,张梦如. 基于APSO-BP-PID控制的质子交换膜燃料电池热管理系统温度控制[J]. 吉林大学学报(工学版), 2024, 54(9): 2401-2413.

Lei SHANG,Ping YANG,Xiang-guo YANG,Jian-xin PAN,Jun YANG,Meng-ru ZHANG. Temperature control of proton exchange membrane fuel cell thermal management system based on APSO-BP-PID control strategy[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2401-2413.

图/表 17

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表2

125 kW燃料电池堆额定工况参数"

参数	数值	参数	数值
$λ /$ ［W·（m·K）^-1］	0.023	$I s t / A$	534
$A / m 2$	1.972	$N c e l l s$	340
$δ / m$	0.05	$σ /$ ［W·（m²·K）^-1］	5.67×10^-8
$Δ t / K$	50	$T s t / K$	348

表2

表3

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参考文献 20

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参数	数值
膜电极数量	340片
双极板类型	金属板
质子交换膜反应面积/cm²	304
质子交换膜厚度/mm	0.012 5
散热方式	水冷
燃料电池堆温度/℃	75

参数	数值
使用介质温度/℃	-40~95
额定流量/（L·min^-1）	210
额定扬程/m	22
额定转速（r·min^-1）	6 950

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基于APSO-BP-PID控制的质子交换膜燃料电池热管理系统温度控制

Temperature control of proton exchange membrane fuel cell thermal management system based on APSO-BP-PID control strategy

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