基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计

doi:10.19799/j.cnki.2095-4239.2024.0964

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (4): 1585-1595.doi: 10.19799/j.cnki.2095-4239.2024.0964

基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计

申江卫¹^,²(), 折亦鑫¹, 舒星², 刘永刚³, 魏福星¹, 夏雪磊¹, 陈峥¹()

^1.昆明理工大学交通工程学院，云南昆明 650500
^2.汽车零部件先进制造技术教育部重点实验室(重庆理工大学)，重庆 400054
^3.重庆大学机械与运载学院，重庆 400030

收稿日期:2024-10-14 修回日期:2024-11-15 出版日期:2025-04-28 发布日期:2025-05-20
通讯作者: 陈峥 E-mail:shenjiangwei6@kust.edu.cn;chen@kust.edu.cn
作者简介:申江卫（1984—），男，博士，高级实验师，研究方向为新能源汽车动力电池管理，E-mail：shenjiangwei6@kust.edu.cn；
基金资助:
国家自然科学基金(52162051);云南省基础研究计划项目(202301AT070423);昆明理工大学自然科学研究基金项目(KK23202202021);汽车零部件先进制造技术教育部重点实验室开放课题基金(2023KLMT02);重庆市自然科学基金(CSTB2024NSCQ-MSX0389)

State of health estimation for lithium batteries based on short-term random charging data and optimized convolutional neural network

Jiangwei SHEN¹^,²(), Yixin SHE¹, Xing SHU², Yonggang LIU³, Fuxing WEI¹, Xuelei XIA¹, Zheng CHEN¹()

^1.School of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
^2.Key Laboratory of Advanced Manufacture Technology for Automobile Parts (Chongqing University of Technology) Ministry of Education, Chongqing 400054, China
^3.College of Mechanical Engineering, Chongqing University, Chongqing 400030, China

Received:2024-10-14 Revised:2024-11-15 Online:2025-04-28 Published:2025-05-20
Contact: Zheng CHEN E-mail:shenjiangwei6@kust.edu.cn;chen@kust.edu.cn

摘要/Abstract

摘要：

用户充电过程较强的随机性，导致很难获得完整且固定的充电段用于精确表征电池健康状态的变化。针对充电行为的无序性，提出了一种基于随机健康指标和卷积神经网络的电池健康状态估计方法。对锂电池的原始充电电压时序数据进行分割作为随机充电数据，使用单一卷积神经网络架构从中自适应提取老化特征，并采用蜣螂优化算法对其参数寻优，建立了多阶段模型。仅使用短时随机原始充电电压数据即可实现电池健康状态估计，且有效适用于不同充电模式和充电速率。实验测试验证结果表明，使用连续5 s(100个数据点)的原始电压时序数据，在恒流-恒压充电模式下，锂电池健康状态估计结果平均绝对误差小于2.07%，在多阶段恒流充电模式下，锂电池健康状态估计结果平均绝对误差小于1.22%。

关键词: 健康状态, 随机充电, 数据分割, 卷积神经网络, 锂离子电池

Abstract:

In practical applications, complete charge-discharge curves are often unavailable. To address this issue, this study proposes a lithium-ion battery state of health (SOH) estimation method using short-term stochastic charging data and an optimized convolutional neural network (CNN). The goal is to develop an efficient technique that accommodates the random and disordered nature of charging processes in real-world scenarios. The proposed method segments the original charging voltage time-series data of lithium batteries to generate randomized charging data. A shallow CNN comprising four convolutional layers is then constructed to adaptively extract aging-related features from the data. In addition, the dung beetle optimization algorithm is employed to optimize the model parameters, resulting in a multistage model. The experimental results demonstrate that the proposed method can accurately estimate the SOH of Li-ion batteries during stochastic charging, even when using only five consecutive seconds (100 data points) of raw voltage time-series data. The practicality and accuracy of the proposed model were validated under various charging conditions and rates. The results demonstrate that the model continues to exhibit a low prediction error. The average absolute error of the SOH estimation is less than 2.07% under constant current-voltage charging mode and less than 1.22% under multistage constant current charging mode. In the model comparison, the proposed CNN-based method achieved an average mean absolute error of 1.17%, outperforming integrated prediction models, such as the CNN-Long Short-Term Memory Network and CNN-Gated Recurrent Unit, in terms of estimation accuracy and stability. In addition, the randomized voltage segment used in the CNN accounted for 88.9% of the total charging time, is higher than the other two prediction models. The experimental results demonstrate the effectiveness of the proposed method in addressing the stochastic nature of battery charging data, demonstrating excellent accuracy and high adaptability in the context of charging rates, charging modes, and random charging voltage data over short periods. This results of this study provide a crucial technical reference for future advancement in battery health monitoring and battery management systems.

Key words: state of health, random charging, data segmentation, convolutional neural network, lithium-ion battery

中图分类号:

TM 911

申江卫, 折亦鑫, 舒星, 刘永刚, 魏福星, 夏雪磊, 陈峥. 基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计[J]. 储能科学与技术, 2025, 14(4): 1585-1595.

Jiangwei SHEN, Yixin SHE, Xing SHU, Yonggang LIU, Fuxing WEI, Xuelei XIA, Zheng CHEN. State of health estimation for lithium batteries based on short-term random charging data and optimized convolutional neural network[J]. Energy Storage Science and Technology, 2025, 14(4): 1585-1595.

图/表 14

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参考文献 28

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单体电池特性	电池单体参数
正极材料	磷酸铁锂
负极材料	石墨
额定容量	1.1 Ah
充电模式	CC-CV/C1-C2-CV
电压工作范围	2.0～3.6 V
额定电压	3.3 V
实验温度	30 ℃

架构设计	配置
输入层	“Z-score”规范化
卷积层	卷积核大小：[pop 3]，数量：[pop 1]，步长为1
卷积层	卷积核大小：[pop 3]，数量：[pop 2]，步长为1
卷积层	卷积核大小：[pop 5]，数量：[pop 2]，步长为1
卷积层	卷积核大小：[pop 5]，数量：[pop 2]，步长为1
丢弃层	丢弃比例：[pop 4]
平均池化层	步幅为1
全连接层	全连接第32个输出
全连接层	32个输入全连接1个输出
回归层	回归输出

起点电压 /V	窗口长度
	50		75		100
	R²	MAE/%	R²	MAE/%	R²	MAE/%
2.782	0.3642	4.56	0.5575	2.75	0.8719	1.44
3.036	0.8778	1.45	0.8492	1.59	0.8453	1.68
3.268	0.8017	1.79	0.9120	1.20	0.8805	1.43
3.422	0.8855	1.36	0.8554	1.59	0.8608	1.55
3.474	0.7945	1.93	0.7149	2.32	0.7671	2.07
3.500	0.7899	1.91	0.7332	2.20	0.8783	1.40
3.523	0.7917	1.93	0.8255	1.64	0.9611	0.84
3.553	0.8608	1.52	0.9615	0.80	0.9698	0.79
3.594	0.9211	1.29	0.9694	0.84	0.9752	0.73
3.392	0.9557	1.02	0.9787	0.69	0.9697	0.82
3.409	0.9556	1.02	0.9688	0.84	0.9727	0.77
3.422	0.9587	0.91	0.9640	0.84	0.9635	0.82
3.434	0.9023	1.38	0.9207	1.19	0.8834	1.33
平均误差	0.8495	1.71	0.8585	1.39	0.8954	1.17
覆盖率/%	61.1		77.7		88.9

窗口长度	评价	CNN	CNN-LSTM	CNN-GRU
100	R²	0.8954	0.8469	0.8427
	MAE/%	1.17	1.30	1.48
	覆盖率/%	88.9	77.7	77.7

电池16起点电压	R²	MAE/%	电池18起点电压	R²	MAE/%
2.825	0.8975	1.18	2.736	0.8572	1.00
3.062	0.9082	1.06	2.984	0.7186	1.22
3.306	0.9253	0.98	3.227	0.9705	0.57
3.481	0.9105	1.07	3.432	0.9708	0.57
3.535	0.8915	1.07	3.504	0.9748	0.54
3.468	0.9619	0.70	3.465	0.9741	0.52
3.466	0.9364	0.80	3.471	0.8702	0.87
3.482	0.9890	0.36	3.487	0.9813	0.39
3.503	0.9937	0.28	3.504	0.9795	0.45
3.533	0.9905	0.34	3.535	0.9839	0.42
3.583	0.9879	0.35	3.373	0.8924	0.95
			3.417	0.9011	0.81
			3.431	0.9256	0.81
平均误差	0.9448	0.74	平均误差	0.9231	0.70
覆盖率/%	87.5		88.9

基于短时随机充电数据和优化卷积神经网络的锂电池健康状态估计

State of health estimation for lithium batteries based on short-term random charging data and optimized convolutional neural network

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