基于前馈神经网络-教与学算法的质子交换膜燃料电池的智能参数辨识

李鸿彪; 郜登科; 石林龙; 郑飞; 杨博

李鸿彪,郜登科,石林龙,郑飞,杨博.基于前馈神经网络-教与学算法的质子交换膜燃料电池的智能参数辨识[J].电测与仪表,2026,63(7):137-145.
LI Hongbiao,GAO Dengke,SHI Linlong,ZHENG Fei,YANG Bo.Intelligent parameter identification of PEMFC based on feedforward neural network-teaching and learning algorithm[J].Electrical Measurement & Instrumentation,2026,63(7):137-145.

基于前馈神经网络-教与学算法的质子交换膜燃料电池的智能参数辨识

Intelligent parameter identification of PEMFC based on feedforward neural network-teaching and learning algorithm

DOI：10.19753/ j.issn1001-1390.2026.07.013

中文关键词: 参数辨识数据预测前馈神经网络元启发式算法 SimuNPS

英文关键词:parameter identification, data prediction, feedforward neural network, metaheuristic algorithm, SimuNPS

基金项目:国家自然科学基金资助项目（62263014）；云南省自然科学基金资助项目（202301AT070443，202401AT070344）

作者中文名	作者英文名	单位
李鸿彪	LI Hongbiao	上海科梁信息科技股份有限公司, 上海 201103
郜登科	GAO Dengke	上海科梁信息科技股份有限公司, 上海 201103
石林龙	SHI Linlong	上海科梁信息科技股份有限公司, 上海 201103
郑飞	ZHENG Fei	上海科梁信息科技股份有限公司, 上海 201103
杨博	YANG Bo	昆明理工大学电力工程学院, 昆明 650500

摘要点击次数: 173

中文摘要:

参数辨识是质子交换膜燃料电池(proton exchange membrane fuel cell, PEMFC)研究中的一项关键任务,为建立准确可靠的 PEMFC模型提供了基础。然而, PEMFC模型的非线性特性以及不可避免的不充分的测量数据往往使传统的优化技术难以解决。特别是,不充分的测量数据会引入偏差或导致数据丢失。为了解决上述问题, 提出了一种新的混合优化策略。利用SimuNPS搭建PEMFC半经验模型,并采用前馈神经网络(feedforward neural network, FNN)对数据进行拓展预测处理。利用教与学算法(teaching-learning-based algorithm, TLBO)对拓展处理后的PEMFC参数进行识别。通过与两种辨识方法进行比较,验证了FNN-TLBO策略的有效性。例如,在高温低压下, 对FNN拓展处理数据进行辨识, TLBO比粒子群算法和哈里斯鹰优化算法分别提升了99.21%和85.93%。基于FNN-TLBO的PEMFC模型的参数辨识研究表明, 通过对比原始数据和预处理数据,在仿真结果中发现 FNN-TLBO 具有更高的鲁棒性和优化质量。

英文摘要:

Parameter identification is a key task in the research of proton exchange membrane fuel cell (PEMFC), which provides a basis for establishing an accurate and reliable PEMFC model. However, the nonlinear characteristics of the PEMFC model and the inevitable insufficient measurement data often make traditional optimization techniques difficult to solve. In particular, inadequate measurement data can introduce bias or lead to data loss. To solve the above problems, a new hybrid optimization strategy is proposed. Firstly, the semi-mechanism PEMFC model is established by SimuNPS, and the feedforward neural network (FNN) is used to expand and predict the data. Then, the teaching-learning-based algorithm (TLBO) is used to identify the expanded PEMFC parameters. Finally, the effectiveness of the FNN-TLBO strategy is verified by comparing the two identification methods. For example, under high temperature and low pressure, the identification of FNN expansion processing shows that TLBO is 99.21% and 85.93% higher than particle swarm optimization algorithm and Harris hawk algorithm respectively. The parameter identification of PEMFC model based on FNN-TLBO shows that FNN-TLBO has higher robustness and optimization quality in the simulation results by comparing the original data and the preprocessed data.

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