变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法

doi:10.19799/j.cnki.2095-4239.2024.0732

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (2): 659-670.doi: 10.19799/j.cnki.2095-4239.2024.0732

变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法

李嘉波¹^,²(), 王志璇¹, 田迪¹(), 孙中麟¹

^1.西安石油大学机械工程学院，陕西西安 710065
^2.长安大学，高速公路筑养装备与技术教育部工程研究中心，陕西西安 710064

收稿日期:2024-08-02 修回日期:2024-08-22 出版日期:2025-02-28 发布日期:2025-03-18
通讯作者: 田迪 E-mail:jianbool72@foxmail.com;tiandi@xsyu.edu.cn
作者简介:李嘉波（1992—），男，博士，讲师，从事智能驾驶和电池管理系统，E-mail：jianbool72@foxmail.com；
基金资助:
陕西省教育厅科研计划项目(23JK0599);陕西省自然科学基础研究计划资助项目(2023-JC-QN-0658);高速公路筑养装备与技术教育部工程研究中心(300102253513);“咸阳市二〇二三年重点研发计划”项目动力电池过充电故障诊断研究(L2023-ZDYF-0YCX-032);陕西省教育厅科研计划项目(23JK0599)

Prediction method for remaining service life of lithium batteries using SSA-LSTM combination under variable mode decomposition

Jiabo LI¹^,²(), Zhixuan WANG¹, Di TIAN¹(), Zhonglin SUN¹

^1.School of Mechanical Engineering, Xi'an University of Petroleum, Xi'an 710065, Shaanxi, China
^2.Engineering Research Center for Highway Construction and Maintenance Equipment and Technology of Chang'an University, Ministry of Education, Xi'an 710064, Shaanxi, China

Received:2024-08-02 Revised:2024-08-22 Online:2025-02-28 Published:2025-03-18
Contact: Di TIAN E-mail:jianbool72@foxmail.com;tiandi@xsyu.edu.cn

摘要/Abstract

摘要：

锂离子电池在电动汽车、可再生能源等领域广泛应用，对其剩余使用寿命(remaining useful life，RUL)进行精确预测，能够实时把握电池的内在性能退化状态，降低电池使用风险。本工作提出了一种基于变模态分解(variational mode decomposition，VMD)、麻雀优化算法(sparrow search algorithm，SSA)和长短期记忆网络(long short-term memory，LSTM)的组合预测算法对锂离子电池剩余寿命进行预测。首先，基于锂离子电池电流、电压以及温度曲线，提取等压差充电时间、等压差充电能量、放电温度峰值和恒流充电时间作为预测RUL的间接健康因子。其次，采用变模态分解法分解容量以避免容量回升的局部波动和测试噪声对RUL预测结果造成干扰。针对传统LSTM模型超参数设置易受到经验和随机性的影响，提出了麻雀优化算法对LSTM模型参数进行优化，以提升模型的预测能力。最后，应用NASA和CALCE数据集，将所提模型与其他模型进行对比。实验结果表明，锂离子电池RUL预测均方根误差控制在2%以内，所提方法具有较高的预测性能。

关键词: 锂离子电池, 剩余使用寿命, 变模态分解, 麻雀优化算法, 长短期记忆网络

Abstract:

The widespread application of lithium-ion batteries in electric vehicles, renewable energy, and other fields necessitates accurate prediction of their remaining useful life (RUL). Such predictions enable real-time monitoring of the battery's internal performance degradation, thereby reducing the risk associated with battery usage. We propose a combined prediction algorithm utilizing variational mode decomposition, sparrow search algorithm (SSA), and long short-term memory (LSTM) for predicting the remaining life of lithium-ion batteries. Initially, indirect health indicators for predicting RUL were extracted from the current, voltage, and temperature curves of the batteries. These indicators included isobaric charging time, isobaric charging energy, peak discharge temperature, and constant-current charging time. Subsequently, the VMD method was employed to decompose the capacity, aiming to avoid local fluctuations in capacity recovery and interference from test noise that could affect RUL prediction results. To address the susceptibility of traditional LSTM model hyperparameter settings toward experience and randomness, an SSA was proposed to optimize the parameters of the LSTM model, thereby enhancing the model's predictive capabilities. Ultimately, by utilizing NASA and CALCE datasets, a comparison was conducted between the proposed model and other models. Experimental results demonstrate that the proposed method achieves high predictive performance, with the root mean square error for RUL prediction of lithium-ion batteries consistently maintained within 2%.

Key words: lithium-ion batteries, remaining useful life, variational mode decomposition (VMD), sparrow search algorithm (SSA), long short-term memory (LSTM)

中图分类号:

TM 912

李嘉波, 王志璇, 田迪, 孙中麟. 变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法[J]. 储能科学与技术, 2025, 14(2): 659-670.

Jiabo LI, Zhixuan WANG, Di TIAN, Zhonglin SUN. Prediction method for remaining service life of lithium batteries using SSA-LSTM combination under variable mode decomposition[J]. Energy Storage Science and Technology, 2025, 14(2): 659-670.

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参考文献 18

1	GE M F, LIU Y B, JIANG X X, et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries[J]. Measurement, 2021, 174: 109057.
2	LIU C, WANG Y J, CHEN Z H. Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system[J]. Energy, 2019, 166: 796-806.
3	刘月峰, 赵光权, 彭喜元. 多核相关向量机优化模型的锂电池剩余寿命预测方法[J]. 电子学报, 2019, 47(6): 1285-1292. DOI: 10.3969/j.issn.0372-2112.2019.06.015.
	LIU Y F, ZHAO G Q, PENG X Y. A lithium-ion battery remaining using life prediction method based on multi-kernel relevance vector machine optimized model[J]. Acta Electronica Sinica, 2019, 47(6): 1285-1292. DOI: 10.3969/j.issn.0372-2112.2019.06.015.
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: 228861. DOI:10.1016/j.jpowsour.2020.228861.
5	EL MEJDOUBI A, CHAOUI H, GUALOUS H, et al. Lithium-ion batteries health prognosis considering aging conditions[J]. IEEE Transactions on Power Electronics, 2019, 34(7): 6834-6844.
6	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.
7	RAZAVI-FAR R, CHAKRABARTI S, SAIF M, et al. An integrated imputation-prediction scheme for prognostics of battery data with missing observations[J]. Expert Systems with Applications, 2019, 115: 709-723. DOI:10.1016/j.eswa.2018.08.033.
8	ALI M U, ZAFAR A, NENGROO S H, et al. Online remaining useful life prediction for lithium-ion batteries using partial discharge data features[J]. Energies, 2019, 12(22): 4366.
9	BAI G X, WANG P F, HU C. A self-cognizant dynamic system approach for prognostics and health management[J]. Journal of Power Sources, 2015, 278: 163-174.
10	TANG X P, YAO K, ZOU C F, et al. Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method[J]. Energy Procedia, 2019, 158: 2456-2461.
11	WANG F K, HUANG C Y, MAMO T. Ensemble model based on stacked long short-term memory model for cycle life prediction of lithium-ion batteries[J]. Applied Sciences, 2020, 10(10): 3549.
12	ZHANG Y Z, XIONG R, HE H W, 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.2018.2805189.
13	魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874. DOI: 10.16182/j.issn1004731x.joss.20-0297.
	WEI T F, PAN T L. Short-term power load forecasting based on LSTM neural network optimized by improved PSO[J]. Journal of System Simulation, 2021, 33(8): 1866-1874.
14	LI S, FANG H J, SHI B. Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression[J]. Reliability Engineering & System Safety, 2021, 210: 107542. DOI:10.1016/j.ress.2021.107542.
15	常春, 王瑛琦, 姜久春, 等. 基于主动方波激励检测锂离子电池早期内短路[J]. 电池, 2024, 54(1): 24-28. DOI:10.19535/j.1001-1579. 2024.01.006.
	CHANG C, WANG Y Q, JIANG J C, et al. Detection of early internal short circuit of Li-ion battery based on active square wave excitation[J]. Dianchi(Battery Bimonthly), 2024, 54(1): 24-28. DOI:10.19535/j.1001-1579.2024.01.006.
16	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. DOI:10.1016/j.advengsoft.2016.01.008.
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.
18	SHI Y M, TIAN Y H, WANG Y W, et al. Learning long-term dependencies for action recognition with a biologically-inspired deep network[C]//2017 IEEE International Conference on Computer Vision (ICCV). October 22-29, 2017, Venice, Italy. IEEE, 2017: 716-725. DOI:10.1109/ICCV.2017.84.

电池型号	温度/℃	充电电流/A	放电电流/A	截止电压/V
B05	24	1.5	2	2.7
B06	24	1.5	2	2.7
B07	24	1.5	2	2.7
B18	24	1.5	2	2.7

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B05	RMSE	0.030042	0.026647	0.01666	0.027592	0.029616
	MAE	0.027629	0.023644	0.015732	0.025463	0.026963
	MAPE	0.02042	0.017437	0.01675	0.018838	0.01992

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B06	RMSE	0.042469	0.02555	0.016761	0.035789	0.037206
	MAE	0.035966	0.020792	0.014957	0.030624	0.031476
	MAPE	0.027604	0.0159	0.011712	0.023549	0.024178

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B07	RMSE	0.027232	0.020322	0.019234	0.024218	0.026187
	MAE	0.022484	0.018142	0.017187	0.022117	0.022796
	MAPE	0.015269	0.012363	0.011718	0.015089	0.015513

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B18	RMSE	0.022637	0.013673	0.010348	0.02948	0.02682
	MAE	0.018016	0.011538	0.0072894	0.02553	0.02385
	MAPE	0.01284	0.0082581	0.0053165	0.01824	0.01707

变模态分解下SSA-LSTM组合的锂离子电池剩余使用寿命预测方法

Prediction method for remaining service life of lithium batteries using SSA-LSTM combination under variable mode decomposition

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参考文献 18

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Metrics

本文评价

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B05	RMSE	0.041831	0.019567	0.01551	0.036081	0.024622
	MAE	0.040475	0.017543	0.014332	0.033884	0.023941
	MAPE	0.029623	0.012548	0.010707	0.025086	0.017367

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B06	RMSE	0.037432	0.019104	0.017656	0.026848	0.028956
	MAE	0.028546	0.01713	0.013457	0.019413	0.021737
	MAPE	0.019653	0.013257	0.010145	0.014177	0.016087

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B07	RMSE	0.03855	0.03105	0.01655	0.03737	0.026187
	MAE	0.03552	0.02731	0.01539	0.03346	0.022796
	MAPE	0.02380	0.01824	0.01040	0.02237	0.015513

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
B18	RMSE	0.02634	0.02398	0.01768	0.01930	0.02470
	MAE	0.02277	0.02114	0.01513	0.01661	0.02214
	MAPE	0.01611	0.01450	0.01082	0.01180	0.01572

电池	评价指标	LSTM	VMD-LSTM	VMD-SSA-LSTM	SSA-GPR	SSA-BP
CS2-35	RMSE	0.03411	0.01648	0.00613	0.01774	0.02928
	MAE	0.02363	0.01353	0.00457	0.01594	0.02578
	MAPE	0.02919	0.01577	0.00532	0.01806	0.02889

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