基于迁移学习的锂电池不可逆析锂监测方法

doi:10.19799/j.cnki.2095-4239.2025.0049

摘要/Abstract

摘要：

析锂是引发储能锂离子电池内短路故障的重要原因之一，对析锂过程的在线监测是当前保障电池安全的主流研究方向。然而在工程应用中，现有方法局限于对可逆析锂的监测，并且由于缺乏电池的析锂数据，基于机器学习的在线监测算法存在模型训练上的困难。为解决这些问题，本工作提出了一种基于无监督领域自适应迁移学习的不可逆析锂监测方法。首先通过基于锂离子电池电化学-热-老化耦合模型的仿真和锂离子电池低温析锂老化实验生成不可逆析锂监测模型的源域数据和目标域数据，然后从放电曲线中提取析锂数据特征，在无监督领域自适应迁移学习的框架下构建了基于多层感知机的不可逆析锂监测模型，最后实现了将电池不可逆析锂监测模型从源域数据到目标域数据的迁移。结果表明，对于仿真数据，算法对是否发生不可逆析锂的判断准确率超过99%，对于实验数据，算法对电池不可逆析理的定性判断与实际情况相符，说明本工作提出的方法能够有效监测电池的不可逆析锂，为储能锂离子电池不可逆析锂的在线监测方法提供了新思路。

关键词: 锂离子电池, 析锂监测, 伪二维模型, 多层感知机, 迁移学习

Abstract:

Lithium plating is one of the main contributors to internal short-circuit failures in energy storage lithium-ion batteries. The online monitoring of the lithium plating process is a key direction in research for ensuring battery safety. However, existing methods are limited to monitoring reversible lithium plating in engineering applications, and machine learning-based online monitoring algorithms have difficulty with model training due to the lack of lithium plating data from actual batteries. To address these problems, this work proposes a method for monitoring irreversible lithium plating using unsupervised domain adaptive transfer learning. The source and target domain data for the irreversible lithium plating monitoring model were generated through simulations using an electrochemical-thermal-aging model and low-temperature lithium plating aging experiments. Features related to lithium plating were extracted from the discharge curves, and an irreversible lithium plating monitoring model based on a multilayer perceptron was developed within an unsupervised unsupervised domain adaptive transfer learning framework. This allowed for the transfer of the irreversible lithium plating monitoring model from the source domain data to the target domain data. The results showed that on the simulated data, the algorithm achieved an accuracy rate above 99% in detecting the occurrence of irreversible lithium plating. On the experimental data, the qualitative judgements by the algorithm about the irreversible lithium plating were consistent with the actual conditions. This demonstrates that the proposed method can effectively monitor irreversible lithium plating in batteries and offers a new approach to the online monitoring of irreversible lithium plating in energy storage lithium-ion batteries.

Key words: lithium-ion batteries, lithium plating monitoring, pseudo two-dimensional model, multilayer perceptron, transfer learning

中图分类号:

TM 911

王薇, 梁惠施, 李棉刚, 周奎, 王薇, 王姿尧, 史梓男. 基于迁移学习的锂电池不可逆析锂监测方法[J]. 储能科学与技术, 2025, 14(7): 2698-2706.

Wei WANG, Huishi LIANG, Miangang LI, Kui ZHOU, Wei WANG, Ziyao WANG, Zinan SHI. Method for monitoring irreversible lithium plating in lithium batteries using transfer learning[J]. Energy Storage Science and Technology, 2025, 14(7): 2698-2706.

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温度	充电倍率	放电倍率
-10 ℃、-5 ℃、0 ℃、10 ℃、25 ℃	2C	1C
-10 ℃、-5 ℃、0 ℃、10 ℃、25 ℃	1C	0.05C
-10 ℃、-5 ℃、0 ℃、10 ℃、25 ℃	2C	0.5C

组号	电池编号	温度	充电倍率	放电倍率
1	1～3	25 ℃	2C	1C
2	4～6	35 ℃	2C	1C
3	7～9	-11 ℃	0.2C	0.1C
4	10～12	-16 ℃	0.1C	0.05C

电池编号	温度	充电倍率	放电倍率	循环老化圈数	容量保持率	常温容量保持率
1	25 ℃	2C	1C	1000	72.99%	94.10%
2	25 ℃	2C	1C	1000	85.92%	97.29%
3	25 ℃	2C	1C	1000	85.12%	96.92%
4	35 ℃	2C	1C	1000	82.52%	94.28%
5	35 ℃	2C	1C	1000	83.82%	94.75%
6	35 ℃	2C	1C	1000	83.34%	92.31%
7	-11 ℃	0.2C	0.1C	83	53.41%	73.12%
8	-11 ℃	0.2C	0.1C	178	33.22%	65.83%
9	-11 ℃	0.2C	0.1C	182	38.75%	72.52%
10	-16 ℃	0.1C	0.05C	67	54.38%	63.98%
11	-16 ℃	0.1C	0.05C	76	60.65%	88.64%
12	-16 ℃	0.1C	0.05C	68	59.46%	84.10%

算法	准确率	精确率	召回率
SVM	98.31%	96.98%	96.98%
KNN	99.16%	99.13%	97.84%
MLP	99.28%	98.29%	99.14%