Data-driven online adaptive diagnosis algorithm towards vehicle fuel cell fault diagnosis

被引：0
|
作者：
Wang K.-Y. [1 ,2 ]
Bao D.-T. [1 ]
Zhou S. [1 ]
机构：
[1] School of Automotive Studies, Tongji University, Shanghai
[2] Shanghai Hydrogen Propulsion Technology Co.，Ltd., Shanghai
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 09期
关键词：
LSTM multi-classification; model parameters; online intelligent diagnosis; self-adaptation; vehicle engineering;
D O I：
10.13229/j.cnki.jdxbgxb20220062
中图分类号：
学科分类号：
摘要：
Long short-term memory (LSTM） multi-classification algorithm can effectively realize the online intelligent fault diagnosis of vehicle fuel cells. However，in practical applications，the internal characteristics of vehicle fuel cells will decline with the increase of operating time，and the initial diagnosis model may not be able to meet long-term fault conditions. Aiming at this problem, PEMFC original and decay models based on AVL CURISE M software were built, and fault data was generated using the models. Then，an adaptive algorithm was designed，and the data generated by the model was used for adaptive training，so that the diagnosis model can adapt to the decline of the stack and ensure the accuracy of the online intelligent diagnosis of vehicle fuel cells. Based on this scheme，the actual fuel cell system has been tested and verified，which proves the effectiveness of the scheme. This scheme can adaptively update the weight of the diagnosis algorithm of the fuel cell system based on the "vehicle-cloud" platform and complete the aging of the stack. It has a good application prospect. © 2022 Editorial Board of Jilin University. All rights reserved.
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页码：2107 / 2118
页数：11
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