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

LI Hongbiao; GAO Dengke; SHI Linlong; ZHENG Fei; YANG Bo

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

更新时间：2026-07-17

- Intelligent parameter identification of PEMFC based on feedforward neural network-teaching and learning algorithm
- Vol. 63, Issue 7, (2026)
- 作者机构：
  
  1. 上海科梁信息科技股份有限公司,上海,201103
  2. 昆明理工大学电力工程学院,昆明,650500
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2026
- 稿件说明：
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LI Hongbiao, GAO Dengke, SHI Linlong, et al. Intelligent parameter identification of PEMFC based on feedforward neural network-teaching and learning algorithm[J]. 2026, 63(7).
DOI：

LI Hongbiao, GAO Dengke, SHI Linlong, et al. Intelligent parameter identification of PEMFC based on feedforward neural network-teaching and learning algorithm[J]. 2026, 63(7). DOI：

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。

Abstract

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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