多尺度分解下GRU-MLR组合的锂电池剩余使用寿命预测方法

doi:10.19799/j.cnki.2095-4239.2023.0298

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (7): 2220-2228.doi: 10.19799/j.cnki.2095-4239.2023.0298

• 储能锂离子电池系统关键技术专刊 • 上一篇下一篇

多尺度分解下GRU-MLR组合的锂电池剩余使用寿命预测方法

武明虎¹^,²(), 岳程鹏², 张凡¹^,²(), 李俊晓³, 黄伟², 胡胜¹^,², 唐靓²

^1.湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室
^2.湖北工业大学电气与电子工程学院，湖北武汉 430068
^3.郑州日产汽车有限公司，河南郑州 450046

收稿日期:2023-05-04 修回日期:2023-05-21 出版日期:2023-07-05 发布日期:2023-07-25
通讯作者: 张凡 E-mail:18239368097@163.com;15938907139@163.com
作者简介:武明虎（1975—），男，博士，教授，研究方向为智能电网、动力电池安全管理，E-mail：18239368097@163.com；
基金资助:
湖北省重点研发计划项目(2021BGD013);湖北省科技计划项目(2022BEC017);湖北省自然科学基金项目(2022CFA007)

Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition

Minghu WU¹^,²(), Chengpeng YUE², Fan ZHANG¹^,²(), Junxiao LI³, Wei HUANG², Sheng HU¹^,², Jing TANG²

^1.Hubei University of Technology, Hubei Provincial Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control
^2.School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, Hubei, China
^3.Zhengzhou Nissan Automobile Co. , Ltd. , Zhengzhou 450046, Henan, China

Received:2023-05-04 Revised:2023-05-21 Online:2023-07-05 Published:2023-07-25
Contact: Fan ZHANG E-mail:18239368097@163.com;15938907139@163.com

摘要/Abstract

摘要：

准确预测锂电池的剩余使用寿命(remaining useful life，RUL)可以及时了解电池内部的性能退化情况，降低电池的使用风险并为日常维护提供可靠的理论依据。为了提高预测结果的准确性和稳定性，提出了一种基于集合经验模态分解(ensemble empirical mode decomposition，EEMD)、门控循环单元网络(gated recurrent unit，GRU)和多元线性回归(multiple linear regression，MLR)相结合的锂电池RUL预测模型。该模型首先采用EEMD算法将锂电池容量数据分解为若干个高频分量和低频分量，以此减少容量数据中的噪声干扰，然后针对各个分量的特点，分别利用GRU和MLR网络基于获得的高频和低频序列建立预测子模型，最后叠加融合各个子模型的预测值得到锂电池的RUL结果，通过使用NASA和Oxford提供的锂电池公开数据，并采用不同的预测起点与其他单一模型和组合模型进行对比。实验结果表明，EEMD-GRU-MLR预测模型能够提供准确的RUL结果，相比于LSTM、GRU和EEMD-GRU预测模型，最大平均绝对误差分别降低了0.0311、0.0234、0.0182，最大均方根误差分别降低了0.0235、0.0153、0.0098，证明了本模型具有较好的锂电池RUL预测能力。

关键词: 锂电池, 剩余使用寿命, 集合经验模态分解, 门控循环单元网络, 多元线性回归

Abstract:

Accurately predicting the remaining useful life (RUL) of lithium batteries can ensure timely understanding of the internal performance degradation of the battery, reduce the risks associated with battery use, and provide a reliable theoretical basis for routine maintenance. To improve the accuracy and stability of prediction results, a lithium-battery RUL prediction model based on the combination of ensemble empirical mode decomposition (EEMD) and gated recurrent unit (GRU) with multiple linear regression (MLR) is proposed. First, the model decomposes the lithium-battery capacity data into several high-frequency and low-frequency components using the EEMD algorithm to reduce noise interference in the capacity data. Then, based on the characteristics of each component, the model builds prediction submodels based on the obtained high-frequency and low-frequency sequences using the GRU and MLR networks, respectively. Finally, the predicted values of each submodel are superimposed and fused to obtain the RUL of the battery based on the public data on lithium batteries provided by NASA and Oxford; furthermore, using different prediction starting points, the obtained results are compared with those of other single and combined models. The experimental results show that the EEMD-GRU-MLR prediction model can provide accurate RUL results, compared with LSTM, GRU, and EEMD-GRU prediction models, with the maximum mean absolute error decreased by 0.0311, 0.0234, and 0.0182, respectively, and the maximum root mean square error decreased by 0.0235, 0.0153, and 0.0098, respectively, This proves the satisfactory ability of the proposed EEMD-GRU-MLR model to predict the RUL of lithium batteries.

Key words: lithium battery, remaining useful life, ensemble empirical mode decomposition, gated recurrent unit network, multiple linear regression

中图分类号:

TM 912

武明虎, 岳程鹏, 张凡, 李俊晓, 黄伟, 胡胜, 唐靓. 多尺度分解下GRU-MLR组合的锂电池剩余使用寿命预测方法[J]. 储能科学与技术, 2023, 12(7): 2220-2228.

Minghu WU, Chengpeng YUE, Fan ZHANG, Junxiao LI, Wei HUANG, Sheng HU, Jing TANG. Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition[J]. Energy Storage Science and Technology, 2023, 12(7): 2220-2228.

图/表 11

图1

图2

图3

图4

表1

图5

表2

图6

表3

图7

表4

参考文献 20

1	胡天中, 余建波. 基于多尺度分解和深度学习的锂电池寿命预测[J]. 浙江大学学报(工学版), 2019, 53(10): 1852-1864.
	HU T Z, YU J B. Life prediction of lithium-ion batteries based on multiscale decomposition and deep learning[J]. Journal of Zhejiang University (Engineering Science), 2019, 53(10): 1852-1864.
2	梁海峰, 袁芃, 高亚静. 基于CNN-Bi-LSTM网络的锂离子电池剩余使用寿命预测[J]. 电力自动化设备, 2021, 41(10): 213-219.
	LIANG H F, YUAN P, GAO Y J. Remaining useful life prediction of lithium-ion battery based on CNN-Bi-LSTM network[J]. Electric Power Automation Equipment, 2021, 41(10): 213-219.
3	李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
	LI C R, XIAO F, FAN Y X, et al. An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
4	KHODADADI SADABADI K, JIN X, RIZZONI G. Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health[J]. Journal of Power Sources, 2021, 481: doi: 10.1016/j.jpowsour. 2020.228861.
5	严干贵, 蔡长兴, 段双明, 等. 锂离子储能电池成组方式优化[J]. 电力自动化设备, 2021, 41(4): 148-153.
	YAN G G, CAI C X, DUAN S M, et al. Grouping mode optimization of lithium-ion energy storage battery[J]. Electric Power Automation Equipment, 2021, 41(4): 148-153.
6	杨新波, 郑岳久, 高文凯, 等. 基于改进等效电路模型的高比能量储能锂电池系统功率状态估计[J]. 电网技术, 2021, 45(1): 57-66.
	YANG X B, ZHENG Y J, GAO W K, et al. Power state estimation of high specific energy storage lithium battery system based on extended equivalent circuit model[J]. Power System Technology, 2021, 45(1): 57-66.
7	谢滟馨, 王顺利, 史卫豪, 等. 一种用于高保真锂电池SOC估计的无迹粒子滤波新方法[J]. 储能科学与技术, 2021, 10(2): 722-731.
	XIE Y X, WANG S L, SHI W H, et al. A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation[J]. Energy Storage Science and Technology, 2021, 10(2): 722-731.
8	焦自权, 范兴明, 张鑫, 等. 基于改进粒子滤波算法的锂离子电池状态跟踪与剩余使用寿命预测方法[J]. 电工技术学报, 2020, 35(18): 3979-3993.
	JIAO Z Q, FAN X M, ZHANG X, et al. State tracking and remaining useful life predictive method of Li-ion battery based on improved particle filter algorithm[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3979-3993.
9	姚芳, 张楠, 黄凯. 锂离子电池状态估算与寿命预测综述[J]. 电源学报, 2020, 18(3): 175-183.
	YAO F, ZHANG N, HUANG K. Review of state estimation and life prediction for lithiumion batteries[J]. Journal of Power Supply, 2020, 18(3): 175-183.
10	杨彦茹, 温杰, 史元浩, 等. 基于CEEMDAN和SVR的锂离子电池剩余使用寿命预测[J]. 电子测量与仪器学报, 2020, 34(12): 197-205.
	YANG Y R, WEN J, SHI Y H, et al. Remaining useful life prediction for lithium-ion battery based on CEEMDAN and SVR[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(12): 197-205.
11	李练兵, 李思佳, 李洁, 等. 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.
	LI L B, LI S J, LI J, et al. 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.
12	肖浩逸, 何晓霞, 梁佳佳, 等. 一种基于模态分解和机器学习的锂电池寿命预测方法[J]. 储能科学与技术, 2022, 11(12): 3999-4009.
	XIAO H Y, HE X X, LIANG J J, et al. A lithium battery life-prediction method based on mode decomposition and machine learning[J]. Energy Storage Science and Technology, 2022, 11(12): 3999-4009.
13	YU J B. State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble[J]. Reliability Engineering & System Safety, 2018, 174: 82-95.
14	LI X Y, ZHANG L, WANG Z P, et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks[J]. Journal of Energy Storage, 2019, 21: 510-518.
15	黄凯, 丁恒, 郭永芳, 等. 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测[J]. 电工技术学报, 2022, 37(15): 3753-3766.
	HUANG K, DING H, GUO Y F, et al. Prediction of remaining useful life of lithium-ion battery based on adaptive data preprocessing and long short-term memory network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766.
16	张少凤, 张清勇, 杨叶森, 等. 基于滑动窗口和LSTM神经网络的锂离子电池建模方法[J]. 储能科学与技术, 2022, 11(1): 228-239.
	ZHANG S F, ZHANG Q Y, YANG Y S, et al. Lithium-ion battery model based on sliding window and long short term memory neural network[J]. Energy Storage Science and Technology, 2022, 11(1): 228-239.
17	刘芊彤, 邢远秀. 基于VMD-PSO-GRU模型的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2023, 12(1): 236-246.
	LIU Q T, XING Y X. Remaining life prediction of lithium-ion battery based on VMD-PSO-GRU model[J]. Energy Storage Science and Technology, 2023, 12(1): 236-246.
18	WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
19	SAHA B, GOEBEL K. Battery data set[R]. NASA Ames Prognostics Data Repository, 2007.
20	BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries[D]. Oxford, South East England, UK: University of Oxford, 2017.

实验方法	模型名称
M1	LSTM
M2	GRU
M3	EEMD-GRU
M4	EEMD-GRU-MLR

电池编号	预测方法	RUL预测值	AE	MAE	RMSE
B0005	M1	138	13	0.0402	0.0428
	M2	133	8	0.0325	0.0346
	M3	131	6	0.0273	0.0291
	M4	124	1	0.0091	0.0149
B0006	M1	114	5	0.0342	0.0395
	M2	113	4	0.0255	0.0307
	M3	112	3	0.0180	0.0245
	M4	109	0	0.0079	0.0193
B0007	M1	—	—	0.0361	0.0388
	M2	—	—	0.0277	0.0300
	M3	167	7	0.0103	0.0145
	M4	158	2	0.0075	0.0142

电池编号	预测起点	RUL预测值	AE	MAE	RMSE
B0005	60	118	7	0.0128	0.0178
	80	124	1	0.0091	0.0149
	100	124	1	0.0059	0.0099
B0006	60	108	1	0.0122	0.0200
	80	109	0	0.0079	0.0193
	100	109	0	0.0070	0.0118
B0007	60	156	4	0.0106	0.0158
	80	158	2	0.0075	0.0142
	100	159	1	0.0048	0.0080

电池编号	预测方法	RUL预测值	AE	MAE	RMSE
Cell1	M1	69	6	0.0054	0.0058
	M2	66	3	0.0034	0.0036
	M3	64	1	0.0018	0.0022
	M4	62	1	0.0013	0.0014
Cell2	M1	62	5	0.0059	0.0065
	M2	60	3	0.0045	0.0052
	M3	59	2	0.0034	0.0043
	M4	56	1	0.0018	0.0033

[1]	王怡, 陈学兵, 王愿习, 郑杰允, 刘啸嵩, 李泓. 储能锂离子电池多层级失效机理及分析技术综述[J]. 储能科学与技术, 2023, 12(7): 2079-2094.
[2]	张贵萍, 闫筱炎, 王兵, 姚培新, 胡昌杰, 刘奕哲, 李纾黎, 薛建军. 长寿命循环的磷酸铁锂电池及材料、工艺[J]. 储能科学与技术, 2023, 12(7): 2134-2140.
[3]	马敬轩, 宋宇航, 石爽, 吕娜伟, 尹康涌, 王桂荣, 杜开源, 金阳. 基于气压信号突变探测的液冷型磷酸铁锂电池模组热失控预警研究[J]. 储能科学与技术, 2023, 12(7): 2246-2255.
[4]	乔荣涵, 朱璟, 申晓宇, 岑官骏, 郝峻丰, 季洪祥, 田孟羽, 金周, 詹元杰, 武怿达, 闫勇, 贲留斌, 俞海龙, 刘燕燕, 黄学杰. 锂电池百篇论文点评（2023.4.1—2023.5.31）[J]. 储能科学与技术, 2023, 12(7): 2333-2348.
[5]	刘宇龄, 孟锦豪, 彭乔, 刘天琪, 王扬, 蔡永翔. 基于NSGA-II遗传算法的锂电池均衡指标优化[J]. 储能科学与技术, 2023, 12(6): 1946-1956.
[6]	王海, 边煜华, 王佳东, 刘朝阳, 张杰, 姚健, 高宣雯, 刘朝孟, 骆文彬. 退役锂离子电池锂资源回收工艺[J]. 储能科学与技术, 2023, 12(5): 1453-1460.
[7]	易永利, 于冉, 李武, 金翼, 戴哲仁. Mo， Al掺杂的Li₇La₃Zr₂O₁₂ 基复合固态电解质的制备及全固态电池性能研究[J]. 储能科学与技术, 2023, 12(5): 1490-1499.
[8]	朱璟, 申晓宇, 岑官骏, 乔荣涵, 郝峻丰, 季洪祥, 田孟羽, 金周, 詹元杰, 武怿达, 闫勇, 贲留斌, 俞海龙, 刘燕燕, 黄学杰. 锂电池百篇论文点评（2023.2.1—2023.3.31）[J]. 储能科学与技术, 2023, 12(5): 1553-1569.
[9]	董渊昌, 庞晓琼, 贾建芳, 史元浩, 温杰, 李笑, 张鑫. 基于SVD-SAE-GPR的锂离子电池RUL预测[J]. 储能科学与技术, 2023, 12(4): 1257-1267.
[10]	王朋凯, 张新燕, 张光昊. 基于ResNet-Bi-LSTM-Attention的锂离子电池剩余使用寿命预测[J]. 储能科学与技术, 2023, 12(4): 1215-1222.
[11]	程志翔, 曹伟, 户波, 程云芳, 李鑫, 姜丽华, 金凯强, 王青松. 储能用大容量磷酸铁锂电池热失控行为及燃爆传播特性[J]. 储能科学与技术, 2023, 12(3): 923-933.
[12]	赵鑫浩, 许亮. 改进的萤火虫算法优化反向传播神经网络动力锂离子电池健康状态估计[J]. 储能科学与技术, 2023, 12(3): 934-940.
[13]	申晓宇, 朱璟, 岑官骏, 乔荣涵, 郝峻丰, 田孟羽, 季洪祥, 金周, 武怿达, 詹元杰, 闫勇, 贲留斌, 俞海龙, 刘燕燕, 黄学杰. 锂电池百篇论文点评（2022.12.1—2023.1.31）[J]. 储能科学与技术, 2023, 12(3): 639-653.
[14]	蒋龙进, 张顺, 乔羽, 刘臣臻, 饶中浩. 废旧锂电池负极石墨失效机制及回收利用研究进展[J]. 储能科学与技术, 2023, 12(3): 822-834.
[15]	周宇昊, 徐椤赟, 张钟平, 刘灵冲, 南斌, 赵海祺. 基于数字孪生的锂电池热电耦合模型构建与仿真分析[J]. 储能科学与技术, 2023, 12(2): 536-543.

多尺度分解下GRU-MLR组合的锂电池剩余使用寿命预测方法

Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价