基于数据驱动的车用燃料电池故障在线自适应诊断算法

doi:10.13229/j.cnki.jdxbgxb20220062

Abstract

Abstract:

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.

Key words: vehicle engineering, LSTM multi-classification, online intelligent diagnosis, model parameters, self-adaptation

CLC Number:

TK91

Ke-yong WANG,Da-tong BAO,Su ZHOU. Data-driven online adaptive diagnosis algorithm towards vehicle fuel cell fault diagnosis[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2107-2118.

Figures/Tables 20

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信号	相关系数	能在实际系统中获得	选择作为数据集
空压机转速	-0.0305	√	√
冷却泵转速	0.0035	√
背压阀开度	-0.1382	√	√
空气计量比	-0.1068	√	√
电堆电压	-0.0221	√	√
电堆电流	0.0009	√	√
氢气流阻	0.0775
氢气出压力	0.0085
氢气流阻	-0.1208
空气出口压力	0.0289
空气入口压力	0.0074	√	√
氢气入口压力	0.0082	√	√
空气流量	0.0006	√
氢气流量	-0.0036

工况	训练样本数量	测试样本数量	标签
正常	12 353	3 008	0
轻微膜干	11 393	2 848	1
严重膜干	11 393	2 848	2
轻微水淹	12 353	3 008	3
严重水淹	12 353	3 008	4
轻微欠气	11 039	2 759	5
严重欠气	11 039	2 759	6

结构层	网络参数	输出
长短时记忆网络层（LSTM）	步长（timestep）：100 隐藏节点（hidden size）：24	100×24
全连接层1（FC1）	24	24
激活层（Relu）	24	24
全连接层2（FC2）	7	7

	衰退项目	原始模型	衰退模型
催化层	催化层厚度/mm	0.01	0.009 5
	催化层电子迁移率	0.757	0.719
	参考交换电流密度	3.397	3.227
	催化层活化能量/ （J·mol^-1）	66 000	62 700
	氧含量	0.75	0.7125
	水含量	1	0.95
质子交换膜	膜厚度/mm	0.025	0.023
	热导率/［W·（m·K）^-1］	0.2	0.19
	电导率（阿伦尼乌斯公式首项）/（S·m^-1）	0.51	0.48
	电导率（阿伦尼乌斯公式尾项）/（S·m^-1）	10 542	10 014.9
气体扩散层	气体扩散层厚度/mm	0.34	0.323
	气体扩散层相对穿透率	1	0.95
	气体扩散层孔隙率	0.447	0.424
	阳极双极板流动效率	0.5	0.475
	阳极双极板摩擦因数	6.3	6.615
	阴极双极板流动效率	0.5	0.475
	阴极双极板摩擦因数	15.4	16.17

超参数	数值
批处理数量（Batch size）	150
训练轮数（Epochs）	4096
最大迭代次数	6
自适应置信度	0.95
初始学习率（Initial learning rate）	5×10^-4

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

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