多堆燃料电池系统温度模型预测控制
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作者:
作者单位:

1.同济大学 汽车学院,上海 201804;2.同济大学 中德学院,上海 201804;3.上海机动车检测认证技术研究中心有限公司,上海 201805

作者简介:

沈 伟(1979—),男,博士生,主要研究方向为动力电池热管理系统集成与控制技术。 E-mail:ceo@tongji.edu.cn

通讯作者:

裴冯来(1983 — ),男,高级工程师,工学博士,主要研究方向为氢燃料电池系统测评及故障诊断方法。 E-mail: fenglaip@smvic.com.cn

中图分类号:

TM911.4

基金项目:

同济大学-重塑科技联合实验室基金


Analysis of Temperature Model Predictive Control of a Multi-Stack Fuel Cell System
Author:
Affiliation:

1.College of Automotive Studies, Tongji University, Shanghai 201804,China;2.Chinesisch-Deutsches Hochschulkolleg, Tongji University, Shanghai 201804, China;3.Shanghai Motor Vehicle Inspection Certification and Tech Innovation Center, Shanghai 201805, China

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    摘要:

    针对多堆燃料电池系统各电堆温度的控制问题,提出了一种将阴阳极出口气体温度作为电堆温度修正项的并联式热管理子系统模型,并对多堆燃料电池系统进行热平衡分析。首先,采用模型预测控制方法进行温度控制;然后,应用系统模型辨识的方法建立多个预测模型,通过切换预测模型控制不同工况点的电堆温度过程;最后,设计测试工况进行仿真验证。结果表明:在相应的温度指标下,并联式热管理子系统应用模型预测控制算法能够快速准确地进行多堆燃料电池系统中各电堆的温度控制,并且增加典型工况点的多个预测模型有助于提升控制效果,使得超调量减小,调节时间缩短。

    Abstract:

    Aimed at the problem of controlling the temperature of each stack in the multi-stack fuel cell system, a parallel connection thermal management subsystem is proposed and built. The outlet gas temperature of anode and cathode is taken as the correction term of the stack temperature, and a thermal balance analysis is conducted for the multi-stack fuel cell system. First, the model predictive control method is used to control the temperature. Then, several prediction models are established by using the system identification method, and the performance of the stack is predicted by switching between different prediction models. Finally, the test conditions are designed for simulation verification. The results show that under the corresponding temperature index, the model predictive control algorithm can be used to control the temperature of each stack in the multi-stack fuel cell system quickly and accurately, and the increase of prediction points can improve the control effect, reduce the overshoot, and shorten the regulation time.

    参考文献
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沈伟,石霖,陈春光,周苏,王宁,裴冯来.多堆燃料电池系统温度模型预测控制[J].同济大学学报(自然科学版),2022,50(9):1368~1376

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  • 收稿日期:2021-10-13
  • 在线发布日期: 2022-09-29
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