基于差分电压和Elman神经网络的锂离子电池RUL预测方法

doi:10.19799/j.cnki.2095-4239.2021.0158

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (6): 2373-2384.doi: 10.19799/j.cnki.2095-4239.2021.0158

基于差分电压和Elman神经网络的锂离子电池RUL预测方法

李练兵¹(), 李思佳², 李洁¹(), 孙坤², 王正平³, 杨海跃³, 高冰³, 杨少波⁴

^1.河北工业大学省部共建电工装备可靠性与智能化国家重点实验室
^2.河北工业大学人工智能与数据科学学院，天津 300130
^3.国网河北省电力有限公司衡水供电公司，河北衡水 053099
^4.国网河北省电力有限公司电力科学研究院，河北石家庄 050021

收稿日期:2021-04-14 修回日期:2021-06-16 出版日期:2021-11-05 发布日期:2021-11-03
通讯作者: 李洁 E-mail:lilianbing@hebut.edu.cn;lij@hebut.edu.cn
作者简介:李练兵（1972—），男，博士，教授，研究方向为储能系统监控与运维管理、分布式发电和微电网，E-mail：lilianbing@hebut.edu.cn；
基金资助:
河北省重点研发计划项目(20312102D)

RUL prediction of lithium-ion battery based on differential voltage and Elman neural network

Lianbing LI¹(), Sijia LI², Jie LI¹(), Kun SUN², Zhengping WANG³, Haiyue YANG³, Bing GAO³, Shaobo YANG⁴

^1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology
^2.School of Artificial Intelligence, Hebei University of Technology, 300130, Tianjin, China
^3.Hengshui power supply company of State Grid Hebei Electric Power Co. Ltd. , Hengshui 053099, Hebei, China
^4.State Grid Hebei Electric Power Co. , Ltd. Electric Power Research Institute, Shijiazhuang 050021, Hebei, China

Received:2021-04-14 Revised:2021-06-16 Online:2021-11-05 Published:2021-11-03
Contact: Jie LI E-mail:lilianbing@hebut.edu.cn;lij@hebut.edu.cn

摘要/Abstract

摘要：

锂离子电池剩余使用寿命(remaining useful life，RUL)预测对电池的使用维护极为重要，提出一种基于差分电压和Elman神经网络预测锂离子电池RUL的方法。首先，根据美国国家航天航空局(National Aeronautics and Space Administration，NASA)的锂离子电池数据集，分析电池差分电压曲线和充放电曲线，提取电池容量退化特征量；其次，通过Pearson法分析特征量之间的相关性，将充电差分电压曲线初始拐点值、放电差分电压曲线峰值、放电时间、静置时间作为电池RUL预测的间接健康因子；最后，建立以上述间接健康因子为输入，电池容量为输出的Elman神经网络，进行锂离子电池的RUL预测。基于不同间接健康因子和不同神经网络的四种电池容量预测对比实验表明，在间接健康因子中加入充电差分电压曲线初始拐点值和放电差分电压曲线峰值可以提高电池寿命预测精度，Elman神经网络可准确预测电池容量。基于不同循环次数预测电池RUL，预测的平均均方根误差为1.55%。

关键词: 锂离子电池, RUL, 差分电压, Elman神经网络, 相关系数

Abstract:

The prediction of the Remaining Useful Life (RUL) of lithium-ion battery has significant value for the study of the usage and maintenance of batteries. A method for RUL prediction of lithium-ion batteries based on differential voltage and Elman neural network is proposed. Firstly, based on the National Aeronautics and Space Administration' (NASA) lithium-ion battery data set, the differential voltage curve and charge-discharge curve of the battery are analyzed, and the characteristic quantity of battery capacity degradation is extracted. Secondly, the correlation between characteristic parameters is studied by Pearson method. The inflexion point of charging differential voltage curve, peak value of discharging differential voltage curve, as well as the discharging time and resting time are determined as indirect health factors of battery RUL prediction. Finally, an Elman neural network employed the inputs of above indirect health factors and the output of battery capacity is established to predict RUL of lithium-ion batteries. The comparative experiments of four kinds of battery capacity prediction based on different indirect health factors and different neural networks show that the prediction accuracy of battery life can be improved by adding the initial inflection point of charging differential voltage curve and the peak value of discharge differential voltage curve into the indirect health factors, and the Elman neural network can accurately predict the battery capacity. The mean root mean square error (RMSE) of the prediction of battery RUL based on different cycle times is 1.55%.

Key words: lithium-ion batteries, RUL, differential voltage, Elman neural network, correlation coefficient

中图分类号:

TM 911.3

李练兵, 李思佳, 李洁, 孙坤, 王正平, 杨海跃, 高冰, 杨少波. 基于差分电压和Elman神经网络的锂离子电池RUL预测方法[J]. 储能科学与技术, 2021, 10(6): 2373-2384.

Lianbing LI, Sijia LI, Jie LI, Kun SUN, Zhengping WANG, Haiyue YANG, Bing GAO, Shaobo YANG. RUL prediction of lithium-ion battery based on differential voltage and Elman neural network[J]. Energy Storage Science and Technology, 2021, 10(6): 2373-2384.

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电池型号	恒流充电截止电压/V	恒压充电截止电流/A	恒流放电电流/A	恒流放电截止电压/V	电池容量/(A·h)	EOL/次
B0005	4.2	0.02	2	2.7	2	123
B0006	4.2	0.02	2	2.5	2	119
B0007	4.2	0.02	2	2.2	2	167
B0018	4.2	0.02	2	2.5	2	122

特征量	充电阶段		放电阶段				电池容量
特征量	D_C	T_C	V_D	D_D	T_D	T_S	Q
D_C	1	-0.37	-0.68	-0.69	0.75	-0.52	0.75
T_C	-0.37	1	0.59	0.66	-0.68	0.63	-0.68
V_D	-0.68	0.59	1	0.80	-0.88	0.69	-0.88
D_D	-0.69	0.66	0.80	1	-0.96	0.94	-0.96
T_D	0.75	0.68	-0.88	-0.96	1	-0.88	1
T_S	-0.52	0.63	0.69	0.94	-0.88	1	-0.89
Q	0.75	0.68	-0.88	-0.96	1	-0.89	1

方案号	间接健康因子	RMSE/%				MAE/%				时间/s
方案号	间接健康因子	B0005	B0006	B0007	B0018	B0005	B0006	B0007	B0018	B0005	B0006	B0007	B0018
1	D_C、V_D、T_D、T_S	2.930	1.838	1.723	1.344	2.441	1.615	1.311	1.126	395	422	409	389
2	T_D、T_S	1.825	2.824	2.473	2.014	1.483	2.126	1.902	1.742	992	1085	1337	1264
3	T_C、D_D、T_D、T_S	1.225	1.908	1.992	1.474	0.874	1.576	1.726	1.265	243	258	270	267
4	D_C、D_D	1.939	2.932	3.079	3.108	1.571	2.721	2.321	2.693	1211	1960	1826	1794
5	D_C、D_D、T_D、T_S	0.510	0.907	0.868	0.448	0.430	0.767	0.618	0.345	302	367	324	415

电池数据集	预测模型	RMSE/%	MAE/%	MSE/%
B0005	BP	2.729	2.508	0.007
B0005	Elman	0.410	0.350	0.002
B0006	BP	5.865	4.859	0.344
B0006	Elman	0.300	0.731	0.011
B0007	BP	3.303	3.007	0.109
B0007	Elman	0.575	0.469	0.003
B0018	BP	1.193	0.918	0.014
B0018	Elman	0.651	0.533	0.004

电池型号	预测起始点	预测周期数	MSE/%	RMSE/%	MAE/%	AE/周期数
B0005	90	77	0.195	4.420	3.360	3
	100	67	0.003	0.510	0.430	1
	110	57	0.003	0.550	0.450	1
	120	47	0.002	0.410	0.350	0
B0006	90	76	0.124	3.521	2.555	0
	100	66	0.955	3.090	2.450	0
	110	57	0.002	0.458	0.408	0
	120	46	0.001	0.300	0.731	—
B0007	90	77	0.038	1.957	1.668	0
	100	67	0.007	0.865	0.618	0
	110	57	0.002	0.424	0.347	0
	120	47	0.003	0.575	0.469	0
B0018	70	62	0.099	3.145	2.494	9
	80	52	0.058	2.399	2.029	9
	90	42	0.024	1.553	1.382	4
	100	32	0.004	0.651	0.533	0

基于差分电压和Elman神经网络的锂离子电池RUL预测方法

RUL prediction of lithium-ion battery based on differential voltage and Elman neural network

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