基于多特征量分析和LSTM-XGBoost模型的锂离子电池SOH估计方法

doi:10.19799/j.cnki.2095-4239.2024.0289

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (9): 2972-2982.doi: 10.19799/j.cnki.2095-4239.2024.0289

• AI辅助先进电池设计与应用专刊 • 上一篇下一篇

基于多特征量分析和LSTM-XGBoost模型的锂离子电池SOH估计方法

陆继忠¹(), 彭思敏²(), 李晓宇³

^1.江苏省盐城技师学院电气工程学院，江苏盐城 224000
^2.盐城工学院电气工程学院，江苏盐城 224051
^3.深圳大学物理与光电工程学院，广东深圳 518060

收稿日期:2024-04-01 修回日期:2024-04-11 出版日期:2024-09-28 发布日期:2024-09-20
通讯作者: 彭思敏 E-mail:120322268@qq.com;psmsteven@163.com
作者简介:陆继忠（1974—），男，硕士，高级讲师，研究方向为电气自动化控制技术的应用研究，E-mail：120322268@qq.com；
基金资助:
国家自然科学基金(52177219);江苏高校“青蓝工程”(2021-11);盐城工学院校级科研(xjr2021052)

State-of-health estimation of lithium-ion batteries based on multifeature analysis and LSTM-XGBoost model

Jizhong LU¹(), Simin PENG²(), Xiaoyu LI³

^1.School of Electrical Engineering, Jiangsu Yancheng Technician College, Yancheng 224000, Jangsu, China
^2.School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, Jangsu, China
^3.School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China

Received:2024-04-01 Revised:2024-04-11 Online:2024-09-28 Published:2024-09-20
Contact: Simin PENG E-mail:120322268@qq.com;psmsteven@163.com

摘要/Abstract

摘要：

准确评估锂离子电池健康状态(state of health, SOH)对保证电动汽车的安全稳定运行至关重要。然而，传统SOH估计方法在有效提取健康特征(health features, HFs)和依赖大量特征测试数据上面临一些挑战。为此，本文提出一种基于多特征量分析和长短期记忆(long short-term memory, LSTM)-极端梯度提升(eXtreme gradient boosting, XGBoost)模型的锂离子电池SOH估计方法。首先，为准确描述电池的老化机理，从电池充电数据中提取关于时间、能量、IC三大类共6个HFs。考虑到同类型HFs之间存在大量冗余信息，采用一种基于双相关性的特征处理方法，筛选出可准确表征电池退化趋势的组合HFs。其次，针对传统SOH估计模型需要大量HFs测试数据的问题，提出一种基于LSTM-XGBoost的SOH估计模型。在该模型中，采用LSTM算法来预测电池剩余循环次数的HFs数据。同时，为解决LSTM模型进行HFs预测时计算效率不高的问题，采用LSTM-XGBoost模型进行电池SOH估计。利用NASA电池数据集进行验证，结果表明，所提出方法在不同测试数据量下能准确估计锂电池的SOH，且均方根误差保持在1%以内，具有较高的估计精度和鲁棒性。

关键词: 锂离子电池, 健康状态, 特征分析, 长短期记忆神经网络, 极端梯度提升

Abstract:

An accurate assessment of the state-of-health (SOH) of lithium-ion batteries is critical to ensure the safe and stable operation of electric vehicles. However, traditional SOH estimation methods face challenges in effectively extracting health features (HFs) and relying on large amounts of HF test data. This paper proposes an SOH estimation method for lithium-ion batteries based on multifeature analysis and the long short-term memory (LSTM)-eXtreme gradient boosting (XGBoost) model. First, to accurately describe the aging mechanism of a battery, six HFs were extracted from the battery charging data in three categories: time, energy, and IC. Considering a lot of redundant information exists among the same types of HFs, a feature-processing method based on double correlation was presented to screen out the combined HFs that can accurately characterize the trend of battery degradation. Second, to solve the problem that the traditional SOH estimation model requires a large amount of HF test data, an SOH estimation model based on the LSTM-XGBoost was proposed. In this model, the LSTM algorithm was used to predict the HF data of the number of battery remaining cycles. At the same time, to solve the problem of low computational efficiency in HF prediction using the LSTM model, the LSTM-XGBoost model was developed to estimate the SOH of batteries. The results show that the proposed method can accurately estimate the SOH of lithium batteries under different test data amounts, and the root-mean-square error is kept within 1%, which has high estimation accuracy and robustness.

Key words: lithium-ion battery, state of health, characteristic analysis, long short-term memory neural network, extreme gradient boosting

中图分类号:

TM 912

陆继忠, 彭思敏, 李晓宇. 基于多特征量分析和LSTM-XGBoost模型的锂离子电池SOH估计方法[J]. 储能科学与技术, 2024, 13(9): 2972-2982.

Jizhong LU, Simin PENG, Xiaoyu LI. State-of-health estimation of lithium-ion batteries based on multifeature analysis and LSTM-XGBoost model[J]. Energy Storage Science and Technology, 2024, 13(9): 2972-2982.

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特征		灰色关联度
特征		B0005	B0006	B0007
时间特征	HF₁	0.9271	0.9096	0.9443
时间特征	HF₂	0.8080	0.7867	0.7866

能量特征	HF₃	0.9106	0.9601	0.9411
能量特征	HF₄	0.8235	0.7550	0.7943

IC曲线特征	HF₅	0.9500	0.9409	0.9259
IC曲线特征	HF₆	0.8878	0.8512	0.7729

电池	特征	主要HFs	灰色关联度	次要HFs
B0005	HF₁	HF₅	0.9817	HF₁
	HF₂		0.7763	HF₁
	HF₃		0.9513	HF₃
	HF₄		0.7892	HF₃

B0006	HF₁	HF₃	0.9208	HF₁
	HF₂		0.7838	HF₁
	HF₅		0.9657	HF₅
	HF₆		0.8593	HF₅

B0007	HF₃	HF₁	0.9814	HF₃
	HF₄		0.8114	HF₃
	HF₅		0.9715	HF₅
	HF₆		0.7948	HF₅

电池	特征	MAE/%	MAPE/%	RMSE/%
B0005	HF₅	0.2184	1.1698	0.3386
	HF₁	0.5214	1.7601	0.6033
	HF₃	0.6072	1.9265	0.7928

B0006	HF₃	0.4523	2.2077	0.6111
	HF₁	0.3546	2.2734	0.6049
	HF₅	0.4164	1.4824	0.8212

B0007	HF₁	0.2247	0.8649	0.4459
	HF₃	0.3771	1.1927	0.4801
	HF₅	0.4371	1.7686	0.5080

电池	预测起点	MAE/%	MAPE/%	RMSE/%
B0005	80	0.5621	0.7936	0. 8677
	100	0.3826	0.5609	0.4585
	120	0.3246	0.4868	0. 3638

B0006	80	0. 9228	1.3509	1.1205
	100	0.6338	0.9663	0.7988
	120	0.6220	0.9928	0.8434

B0007	80	0.4201	0.5475	0.7471
	100	0.3868	0.5277	0.5044
	120	0.3526	0.4851	0.4395

电池	模型	MAE/%	MAPE/%	RMSE/%	计算耗时/s
B0005	RBF	0.8735	1.8355	1.3354	2.87
	SVM	0.6938	1.0219	0.7567	2.45
	LSTM	0.5733	0.8433	0.6317	12.78
	本文模型	0.3826	0.5609	0.4585	4.30

B0006	RBF	1.0926	1.5557	1.2837	2.93
	SVM	1.2394	1.9945	1.6654	2.52
	LSTM	0.7645	1.1567	0.9451	13.67
	本文模型	0.6338	0.9663	0.7988	4.41

B0007	RBF	0.7312	1.0245	0.8934	3.17
	SVM	0.7155	0.9761	0.8099	2.98
	LSTM	0.6477	0.8829	0.7364	13.04
	本文模型	0.3868	0.5277	0.5044	4.24

基于多特征量分析和LSTM-XGBoost模型的锂离子电池SOH估计方法

State-of-health estimation of lithium-ion batteries based on multifeature analysis and LSTM-XGBoost model

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