Machine learning-enhanced electrochemical impedance spectroscopy for lithium-ion battery research

doi:10.19799/j.cnki.2095-4239.2024.0708

Abstract

Abstract:

The rapid proliferation of electrification has driven a global surge in the demand for power and energy storage batteries. This rise has intensified concerns regarding battery safety and reliability, emphasizing the need for accurate methods for diagnosing and predicting battery aging, making this a notable area of research in the battery domain. Electrochemical impedance spectroscopy (EIS) is widely used to analyze the complex aging processes of batteries because it can effectively decouple various frequency-domain processes. The integration of machine learning methods not only facilitates the acquisition and analysis of EIS data but also offers deeper insights into battery aging and failure mechanisms. This paper reviews the latest applications of machine learning methods in EIS technique, focusing on machine learning-based acquisition and analysis of EIS data for battery life assessment and prediction. In addition, this paper explores the potential of data fusion methods for analyzing the aging behavior of batteries and predicting their lifespan, discusses the current limitations of applying machine learning to EIS research, and describes the future prospects of EIS-based battery life prediction.

Key words: lithium-ion batteries, electrochemical impedance spectroscopy, machine learning, lifetime prediction, data-driven

CLC Number:

TM 911

Zhifeng HE, Yuanzhe TAO, Yonggang HU, Qicong Wang, Yong YANG. Machine learning-enhanced electrochemical impedance spectroscopy for lithium-ion battery research[J]. Energy Storage Science and Technology, 2024, 13(9): 2933-2951.

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输入	模型	结果	预测范围	文献
特征频率	CNN	2.4 Ah 18650电池RMSE约1 mΩ 45 mAh LCO max RMSE 约0.11 Ω	0.1 Hz～10 kHz	[33]

CC曲线	CNN	2.4 Ah 18650电池RMSE ＜2 mΩ	0.1 Hz～100 kHz	[21]
	GPR	2.2 Ah 18650电池RMSE约1.2 mΩ	10 mHz～6.5 kHz	[22]
	LR	2.2 Ah 18650电池RMSE约3.4 mΩ	10 mHz～6.5 kHz
	RF	2.2 Ah 18650电池RMSE约1.6 mΩ	10 mHz～6.5 kHz
	XGBoost	2.2 Ah 18650电池RMSE约2.1 mΩ	10 mHz～6.5 kHz
	KNN	2.2 Ah 18650电池RMSE约1.4 mΩ	10 mHz～6.5 kHz
	ANN	2.2 Ah 18650电池RMSE约2.2 mΩ	10 mHz～6.5 kHz

CC曲线	LSTM	2.4 Ah 18650电池maxRMSE= 1.48 mΩ	100 mHz～10 kHz	[24]

CV曲线	GPR	2.6 Ah 18650电池max RMSE=0.87 mΩ	10 mHz～10 kHz	[23]
	LR	2.6 Ah 18650电池max RMSE=5.18 mΩ	10 mHz～10 kHz
	RF	2.6 Ah 18650电池max RMSE=1.04 mΩ	10 mHz～10 kHz
	XGBoost	2.6 Ah 18650电池max RMSE=1.01 mΩ	10 mHz～10 kHz
	KNN	2.6 Ah 18650电池max RMSE=0.96 mΩ	10 mHz～10 kHz

RV曲线	GPR	2.6 Ah 18650电池max RMSE=0.72 mΩ	10 mHz～10 kHz	[23]
	LR	2.6 Ah 18650电池max RMSE=1.95 mΩ	10 mHz～10 kHz
	RF	2.6 Ah 18650电池max RMSE=0.77 mΩ	10 mHz～10 kHz
	XGBoost	2.6 Ah 18650电池max RMSE=0.80 mΩ	10 mHz～10 kHz
	KNN	2.6 Ah 18650电池max RMSE=0.87 mΩ	10 mHz～10 kHz

99 s脉冲(1 Hz)	LSTM	2.4 Ah 18650电池 RMSE约1.0 mΩ	100 mHz～10 kHz	[29]

10 s脉冲(10 Hz)	ANN	2.5 Ah SONY US18650VTC5电池，RMSE为2.1 mΩ	1.15 Hz～11.5 kHz	[30]

EIS数据频率、周期电位、温度和SOC	集成学习	30 mAh LIR203预测相角准确率99.9%	10^-2～10⁵ Hz	[31]
EIS数据频率、周期电位、温度和SOC	集成学习	实部阻抗98.54%置信区间虚部阻抗95.35%置信区间	20 mHz～100 kHz	[32]

类型	模型	内容	文献
ECM的选取和参数识别	贝叶斯统计法	预测仅含电阻和电容的ECM，超过90%准确率	[34]
	支持向量机	预测五种典型的ECM，预测准确度为78%	[35]
	CNN-LSTM	对最佳电路模型预测，在实验测量的阻抗数据验证其有效性，可用于快速筛选建模大型阻抗谱数据	[36]
	DNN	允许使用生成数据和真实EIS数据训练ECM模型，拟合失败率小于1%	[37]
	ANN	引入新的损失函数进行优化识别电池老化EIS产生的变化	[38]

不同机器学习模型识别ECM准确性比较	XGboost/RF/CNN	XGboost最佳，CNN可以用于ECM分类，但仍面临挑战	[39]

EIS的数据(数量/输入形式/输出标签)对机器学习识别ECM的影响	ANN	200条数据足以用于简单的ECM识别，准确率95%，对于复杂场景识别准确率仅为75%	[40]
	Resnet	讨论EIS的输入情况(Nyquist图/波特图以及三种归一化数据对于预测ECM的影响	[41]
	PCA/t-SNE/UMAP	在无监督条件下在面临复杂条件的EIS可能无法完全揭示EIS谱图的细微差别	[42]

输入	模型	预测结果	文献
宽频输入	DNN-TL	在25 ℃和35 ℃训练的DNN模型MSE为0.0266，R²为99.68%	[63]
	CNN	1C放电下RMSE为9.57%	[69]
	CNN	RMSE为1.974 SOH%和最大误差为4.935 SOH%	[82]
	GPR	RMSE为1.1096%， R²为0.9759，MSE为1.0374	[71]
	GPR	R²为0.88	[61]
	GPR	RMSE为1.168%，MAE为1.016%	[70]
	FL，SVM ANN，RF	R²为0.98，MAE为1.87	[83]

宽频+IC	SVR-Elman-ELM	R²为0.9957,MAE为0.0065，RMSE为0.109	[62]

特征频率	SVR	预测结果相对误差都在2%以内	[84]
	DR, lightGBM XGboost,Catboost	RMSE为3.16，MSE为9.96，R²为0.81	[73]
	ELM	估计误差小于2%	[72]
	GPR	RMSE为0.932%，MAE为0.750%	[70]

等效电路	IPSO-CNN-BILSTM	RMSE为1.76%	[85]
	RNN	容量估计MSE为0.462	[66]
	SVM	准确性可以达到80%	[76]
	GRU	RMSE, MAE, MAPE均小于 2 %.	[77]

宽频输入+ 等效电路	PIDL	RMSE为6.36%，R²为0.95	[75]

DRT	ARD-GPR	RMSE为0.6%,MAE为0.4%，R²为0.992	[80]
DRT	LSBoost	RMSE在不同电池工作状态下可以保持在2.08%以内	[79]

Nyquist图转换为图像后输入	VGG	RMSE小于2%，与基准模型相比，准确度提高了55.6%	[86]

内容	开源地址	文献
基于EIS和电池寿命数据	https://github.com/PenelopeJones/battery-forecasting	[106]
基于EIS和剩余使用寿命RUF数据	https://github.com/YunweiZhang/MLidentify-battery-degradation	[61]
基于等效电路EIS数据	https://data.mendeley.com/datasets/mbv3bx847g	[107]
基于不同SOC 不同温度EIS数据	https://github.com/battery-data-commons/mrs-sp22-tutorial/tree/main/predict_capacity_from_eis.	[81]
基于EIS的健康状态数据	EIS Dataset & NN code (figshare.com)	[82]
基于EIS的锂离子电池电化学诊断数据	www.github.com/NREL/battery_capacity_from_eis	[108]
基于EIS的电池容量预测数据	https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/	[85]

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