基于膨胀应力的锂离子电池剩余使用寿命预测*

doi:10.11985/2024.01.005

摘要/Abstract

摘要：

准确快速预测锂离子电池剩余使用寿命(Remaining useful life, RUL)对系统安全稳定运行至关重要。然而，电池内部退化机理复杂，外部运行工况多变，给RUL预测带来了极大挑战。为此，提出了一种基于电池膨胀应力的RUL预测方法。提取电池膨胀应力信息，分别分析可逆膨胀和不可逆膨胀与容量之间的关系，并计算相关性。将可逆膨胀和不可逆膨胀作为特征参数，构建并训练长短期记忆(Long short-term memory, LSTM)神经网络，实现RUL精准快速预测。通过在UMBL公开数据集上验证，利用膨胀应力特征能更好地学习电池老化状态，捕捉电池容量下降趋势。结果表明，在不同循环起点和多种老化条件下，RMSE和MAE分别小于0.82%和0.70%，所提出的方法能够精准快速预测RUL，鲁棒性强。

关键词: 锂离子电池, 剩余使用寿命, 电池膨胀, LSTM网络

Abstract:

Accurate and fast prediction of the remaining useful life(RUL) of lithium-ion batteries is crucial for safe and stable system operation. However, the complex internal degradation mechanism and the changeable external operating conditions of the battery bring great challenges to RUL prediction. Therefore, a RUL prediction method based on battery expansion stress is proposed in this paper. The battery expansion stress information is extracted, the relationship between reversible expansion as well as irreversible expansion and capacity is analyzed respectively, and the correlation is calculated. The reversible expansion and irreversible expansion are used as feature parameters, and long short-term memory(LSTM) neural network is constructed and trained to achieve accurate and fast RUL prediction. Through the verification on UMBL public dataset, the use of expansion stress features enables better learning of the battery aging state and captures the battery capacity degradation trend. The results show that the RMSE and MAE are within 0.82% and 0.70%, respectively, under different cycle starting points and various aging conditions. The proposed method can predict RUL with strong robustness accurately and quickly.

Key words: Lithium-ion battery, remaining useful life, battery expansion, long short-term memory network

中图分类号:

TM912

于淼, 朱昱豪, 顾鑫, 商云龙. 基于膨胀应力的锂离子电池剩余使用寿命预测^*[J]. 电气工程学报, 2024, 19(1): 49-56.

YU Miao, ZHU Yuhao, GU Xin, SHANG Yunlong. Remaining Useful Life Prediction of Lithium-ion Batteries Based on Expansion Stress[J]. Journal of Electrical Engineering, 2024, 19(1): 49-56.

图/表 13

图1

表1

图2

图3

图4

表2

图5

图6

图7

图8

表3

图9

表4

参考文献 23

[1]	梅简, 张杰, 刘双宇, 等. 电池储能技术发展现状[J]. 浙江电力, 2020, 39(3):75-81.
	MEI Jian, ZHANG Jie, LIU Shuangyu, et al. Development status of battery energy storage technology[J]. Zhejiang Electric Power, 2020, 39(3):75-81.
[2]	陈琳, 陈静, 王惠民, 等. 基于小波包能量熵的电池剩余寿命预测[J]. 电工技术学报, 2020, 35(8):1827-1835.
	CHEN Lin, CHEN Jing, WANG Huimin, et al. Prediction of battery remaining useful life based on wavelet packet energy entropy[J]. Transactions of China Electrotechnical Society, 2020, 35(8):1827-1835.
[3]	LI Junfu, LYU Chao, WANG Lixin, et al. Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter[J]. Journal of Power Sources, 2014, 268(5):895-903. doi: 10.1016/j.jpowsour.2014.06.133
[4]	XING Yinjiao, MA E W M, TSUI K L, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries[J]. Microelectronics Reliability, 2013, 53(6):811-820. doi: 10.1016/j.microrel.2012.12.003
[5]	LYU Chao, LAI Qingzhi, GE Tengfei, et al. A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework[J]. Energy, 2017, 120(1):975-984. doi: 10.1016/j.energy.2016.12.004
[6]	LIU Zhenbao, SUN Gaoyuan, BU Shuhui, et al. Particle learning framework for estimating the remaining useful life of lithium-ion batteries[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(2):280-293. doi: 10.1109/TIM.2016.2622838
[7]	XU Xin, CHEN Nan. A state-space-based prognostics model for lithium-ion battery degradation[J]. Reliability Engineering & System Safety, 2017,159:47-57.
[8]	WAAG W, FLEISCHER C, SAUER D U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles[J]. Journal of Power Sources, 2014, 258(14):321-339. doi: 10.1016/j.jpowsour.2014.02.064
[9]	NUHIC A, TERZIMEHIC T, SOCZKA-GUTH T, et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods[J]. Journal of Power Sources, 2013, 239(1):680-688. doi: 10.1016/j.jpowsour.2012.11.146
[10]	ZHANG Yongzhi, XIONG Rui, HE Hongwen, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7):5695-5705. doi: 10.1109/TVT.25
[11]	尤贺泽, 戴海峰, 于臣臣, 等. 软包锂离子电池应力特性及其建模[J]. 同济大学学报, 2020, 48(2):231-240.
	YOU Heze, DAI Haifeng, YU Chenchen, et al. Stress properties and modeling of lithium-ion pouch batteries[J]. Journal of Tongji University, 2020, 48(2):231-240.
[12]	CANNARELLA J, ARNOLD C B. Stress evolution and capacity fade in constrained lithium-ion pouch cells[J]. Journal of Power Sources, 2014, 245(1):745-751. doi: 10.1016/j.jpowsour.2013.06.165
[13]	CANNARELLA J, ARNOLD C B. State of health and charge measurements in lithium-ion batteries using mechanical stress[J]. Journal of Power Sources, 2014, 269(10):7-14. doi: 10.1016/j.jpowsour.2014.07.003
[14]	XU Jun, LIU Binghe, HU Dayong. State of charge dependent mechanical integrity behavior of 18650 lithium-ion batteries[J]. Scientific Reports, 2016, 6(1):21829. doi: 10.1038/srep21829
[15]	MOHTAT P, LEE S, SIEGEL J, et al. Reversible and irreversible expansion of lithium-ion batteries under a wide range of stress factors[J]. Journal of the Electrochemical Society, 2021,168:100520.
[16]	WILLIAMS R J, ZIPSER D. A learning algorithm for continually running fully recurrent neural networks[J]. Neural Computation, 1998, 1(2):270-280. doi: 10.1162/neco.1989.1.2.270
[17]	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[18]	WANG Xianming, SONE Y, SEGAMI G, et al. Understanding volume change in lithium-ion cells during charging and discharging using in situ measurements[J]. Journal of the Electrochemical Society, 2007, 154(1):A14.
[19]	牛少军, 吴凯, 朱国斌, 等. 锂离子电池硅基负极循环过程中的膨胀应力[J]. 储能科学与技术, 2022, 11(9):2989-2994. doi: 10.19799/j.cnki.2095-4239.2022.0194
	NIU Shaojun, WU Kai, ZHU Guobin, et al. Studies on the swelling force during cycling of Si-based anodes in lithium ion batteries[J]. Energy Storage Science and Technology, 2022, 11(9):2989-2994. doi: 10.19799/j.cnki.2095-4239.2022.0194
[20]	耿鑫月, 胡昌华, 郑建飞, 等. 双时间尺度下基于Transformer的锂电池剩余寿命预测[J]. 空间控制技术与应用, 2023, 49(4):119-126.
	GENG Xinyue, HU Changhua, ZHENG Jianfei, et al. Remaining life prediction of lithium batteries based on transformer at dual time scales[J]. Aerospace Control and Application, 2023, 49(4):119-126.
[21]	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 Kai, DING Heng, GUO Yongfang, 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.
[22]	李练兵, 祝亚尊, 田永嘉, 等. 基于Elman神经网络的锂离子电池RUL间接预测研究[J]. 电源技术, 2019, 43(6):1027-1031.
	LI Lianbing, ZHU Yazun, TIAN Yongjia, et al. RUL indirect prediction of lithium-ion battery based on Elman neural network[J]. Chinese Journal of Power Sources, 2019, 43(6):1027-1031.
[23]	叶林峰, 石元博, 黄越洋. 基于BiGRU网络的锂电池寿命预测[J]. 电源技术, 2021, 45(5):598-601.
	YE Linfeng, SHI Yuanbo, HUANG Yueyang. Lithium battery life prediction based on BiGRU network[J]. Chinese Journal of Power Sources, 2021, 45(5):598-601.

电池编号	测试温度/℃	充电倍率	放电倍率
01	25	C/5	C/5
05	-5	1.5C	1.5C
09	45	2C	2C
10	25	C/5	1.5C

特征参数	01	05	09	10
可逆膨胀	0.983	0.996	0.982	0.988
不可逆膨胀	-0.999	-0.993	-0.993	-0.996
内阻	-0.881	-0.894	-0.974	-0.947
充电时间	0.908	0.824	0.984	0.908

电池编号	训练集占比(%)	预测方法	RUL 预测值	RMSE (%)	MAE (%)	AE
01	45	A	154	0.554	0.412	7
	45	B	161	0.896	0.634	14
	55	A	110	0.358	0.298	2
	55	B	124	0.778	0.516	10
05	45	A	122	0.474	0.410	10
	45	B	112	0.892	0.750	20
	55	A	100	0.420	0.384	3
	55	B	112	0.660	0.532	15
09	45	A	62	0.818	0.699	4
	45	B	74	2.182	1.860	16
	55	A	47	0.490	0.458	2
	55	B	52	1.176	1.015	7
10	45	A	195	0.480	0.368	4
	45	B	242	1.486	1.026	51
	55	A	150	0.296	0.268	3
	55	B	157	0.501	0.394	10

预测方法	RUL预测值	RMSE(%)	MAE(%)	AE
文献[21]	156	0.664	0.525	9
文献[22]	135	0.734	0.516	12
文献[23]	160	0.931	0.652	13
本文	155	0.554	0.412	8