Hierarchical alignment transfer learning for lithium-ion battery capacity estimation

doi:10.19799/j.cnki.2095-4239.2022.0706

Abstract

Abstract:

Accurate estimation of capacity plays an important role in the health management and predictive maintenance of lithium-ion batteries. In recent years, data-driven methods have been widely used in the capacity estimation of lithium-ion battery. However, most of these methods assume that the training and test data obey the same distribution, resulting in a rapid decline in accuracy when the test conditions change. The existing transfer learning methods for lithium-ion battery capacity estimation aim to align the global distribution of source and target domains, while ignoring the transferability of features within different layers. Thus, this study proposes a hierarchical alignment transfer learning method for lithium-ion battery capacity estimation and examines the feature transferability among different layers of the deep transfer learning. First, a feature extractor based on a convolutional neural network was designed. Considering the feature transferability within different layers, the maximum mean discrepancy constraint and channel attention consistency constraint were imposed at different layers of the feature extractor. Thus, the features extracted from the source and target domain are similar, and the feature extractor focuses more on domain-invariant features. A predictor then obtains a capacity estimation value. The experimental results were validated on a public lithium-ion battery dataset and compared with other methods. Our findings show that the proposed method has higher estimation accuracy and is significantly better than other methods. In addition, the findings demonstrated the effectiveness and superiority of the transfer learning method in cross-domain capacity estimation.

Key words: lithium-ion battery, capacity estimation, hierarchical alignment transfer learning, maximum mean discrepancy, channel attention consistency

CLC Number:

TM 912

Zhi ZHAI, Fujin WANG, Yi DI, Peiyu MA, Zhibin ZHAO, Xuefeng CHEN. Hierarchical alignment transfer learning for lithium-ion battery capacity estimation[J]. Energy Storage Science and Technology, 2023, 12(4): 1223-1233.

Figures/Tables 10

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域	充电策略	电池编号(通道)
源域	4.4-5.6-5.2-4.252 C^①	8, 15, 18, 32, 48
目标域	7-4.8-4.8-3.652 C	3, 25, 26, 28, 44

方法	误差	电池编号
方法	误差	3	25	26	28	44
HATL	MSE/%	0.064	0.080	0.091	0.125	0.076
HATL	MAE/%	1.786	2.278	2.340	2.325	2.016
ResNet	MSE/%	0.611	1.161	1.306	0.399	0.435
ResNet	MAE/%	6.546	10.075	10.723	5.186	5.119
MMD	MSE/%	0.082	0.174	0.166	0.206	0.162
MMD	MAE/%	2.504	2.704	3.600	2.659	2.911
CORAL	MSE/%	0.701	0.983	0.557	0.270	0.152
CORAL	MAE/%	8.093	9.337	7.148	3.892	1.811
DANN	MSE/%	0.562	0.516	1.170	0.334	0.259
DANN	MAE/%	6.798	5.221	10.524	6.798	4.263

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