Battery high-voltage fault early warning based on improved online migration learning algorithm

doi:10.19799/j.cnki.2095-4239.2023.0119

Abstract

Abstract:

Analyzing the characteristics of lithium batteries before failure is a complex task owing to their nature as electrochemical devices. Moreover, the limited number of failure samples in the production environment and the severe imbalance between positive and negative samples pose considerable challenges. To address these issues, this study proposes an improved online migration learning algorithm for early warning of high-voltage battery faults in batteries. First, to solve the problem of sample imbalance and reduce the use of computing resources, down-sampling technology is introduced. Specifically, a subsection down-sampling strategy is designed for the battery high-voltage fault early warning scenario. This strategy allows the algorithm model to learn more detailed features before the fault occurs. Second, an online transfer learning method based on batch incremental learning (homogeneous online transfer learning under incremental training, HomOTL-UIT) is proposed. This method addresses the need to update the offline classifier, which is trained in the source domain, at an appropriate time to adapt to the constantly changing data distribution in the target domain. Furthermore, it solves data distribution deviation issues and prevents the degeneration of online transfer learning into online learning. Batch incremental learning reduces the computing resource cost of multiple training, enabling continuous learning from the target domain and continuously improving the accuracy of offline classifiers over time. Then, a sliding window-based F₁-score scoring method is designed to solve the problem of the slow and unbalanced weight of the model to improve its accuracy. Finally, the validity and accuracy of the proposed method are verified using the operation data from an energy storage container. The results demonstrate its effectiveness, particularly when dealing with severely unbalanced positive and negative samples, thereby achieving an impressive F₁-score of 0.88.

Key words: battery failure warning, online transfer learning, lower sampling, incremental learning

CLC Number:

TM 912

Chenchen DONG, Dashuai SUN, Jinglong WANG. Battery high-voltage fault early warning based on improved online migration learning algorithm[J]. Energy Storage Science and Technology, 2023, 12(8): 2575-2584.

Figures/Tables 13

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符号	描述
X	源域
Y	目标域
v	离线分类器的预测函数
w	在线分类器的预测函数
Π	映射函数
B	在线分类器的批大小
τ	学习率
C	τ 的下限值
α₁、 α₂	分类器的权重系数
F_1,1	离线分类器的F₁-score
F_1,2	在线分类器的F₁-score

混淆矩阵		真实值
混淆矩阵		故障	正常
预测值	故障	TP	FP
预测值	正常	FN	TN

电芯	簇架	Pack	电芯数	温度传感器	电芯总数	高压阈值/V	电芯类型
电芯A	4	20	12	4	960	3.55	LiFePO₄
电芯B	2	16	15	4	480	3.55	LiFePO₄

混淆矩阵		真实值
混淆矩阵		故障	正常
预测值	故障	37	8
预测值	正常	2	26479
总计		39	26487

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