电源学报

基于数据驱动的锂离子电池剩余寿命预测综述

PDF下载

李银中国电源学会会员 ¹^,² , 王建峰 ¹^,² , 莫伟权 ¹ , 张西丽 ¹^,²

电源学报 | 电池与储能 2025,23(7): 253-265

收起

电源学报 |电池与储能 2025 , 23 (7) : 253 -265

基于数据驱动的锂离子电池剩余寿命预测综述

全屏

李银中国电源学会会员¹^,², 王建峰¹^,², 莫伟权¹, 张西丽¹^,²

作者信息

^1.中国计量大学理学院，杭州　310018

^2.浙江省智能制造质量大数据溯源与应用重点实验室，杭州　310018

通讯作者:

王建峰（1983—　），男，通信作者，博士，副教授。研究方向：电池健康管理。E-mail：jfwang@cjlu.edu.cn。

作者简介:

李银（1995—　），男，中国电源学会会员，硕士研究生。研究方向：锂离子电池剩余寿命预测。E-mail：p20080854005@cjlu.edu.cn。

莫伟权（1999—　），男，硕士研究生。研究方向：锂离子电池健康状态估计。E-mail：923227676@qq.com。

张西丽（1998—　），女，硕士研究生。研究方向：锂离子电池健康状态估计。E-mail：993098323@qq.com。

Review of Data-driven Remaining Useful Life Prediction for Lithium-ion Batteries

Yin LI Member, CPSS¹^,², Jianfeng WANG¹^,², Weiquan MO¹, Xili ZHANG¹^,²

Affiliations

^1.College of Sciences, China Jiliang University, Hangzhou 310018, China

^2.Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, Hangzhou 310018, China

出版时间: 2025-11-30 doi: 10.13234/j.issn.2095-2805.2025.7.253

文章导航

摘要

收起

准确预测锂离子电池剩余使用寿命对新能源汽车的安全可靠运行至关重要。首先，针对数据驱动方法预测锂离子电池剩余使用寿命的研究现状进行分析，归纳总结了6种常用数据驱动方法的研究进展；然后，总结归纳了锂离子电池寿命预测目前在实际应用中存在的3个问题，同时较为全面地讨论了电池数据集的收集问题，阐述了电池数据集对数据驱动方法发展的重要性；最后，对未来的发展趋势进行了展望。

关键词

锂离子电池 / 剩余寿命预测 / 容量再生 / 电池数据集

Abstract

收起

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable operation of new energy vehicles. First, the research status of data-driven methods for predicting the RUL of lithium-ion batteries is analyzed in this paper, and the research progress in six commonly used data-driven methods is reviewed. Then, three problems existing in the practical applications of RUL prediction of lithium-ion batteries at present are summarized. At the same time, the issue of battery dataset collection is discussed comprehensively, and the importance of battery datasets to the development of data-driven methods is also elaborated upon. Finally, the development trend in the future is prospected.

Key words

Lithium-ion battery / remaining useful life (RUL) prediction / capacity regeneration / battery dataset

引用本文

李银中国电源学会会员, 王建峰, 莫伟权, 张西丽. 基于数据驱动的锂离子电池剩余寿命预测综述. 电源学报, 2025 , 23 (7) : 253 -265 . DOI: 10.13234/j.issn.2095-2805.2025.7.253

Yin LI, Jianfeng WANG, Weiquan MO, Xili ZHANG. Review of Data-driven Remaining Useful Life Prediction for Lithium-ion Batteries[J]. Journal of Power Supply, 2025 , 23 (7) : 253 -265 . DOI: 10.13234/j.issn.2095-2805.2025.7.253

基金

收起

国家自然科学基金资助项目(11504356)
浙江省自然科学基金资助项目(LY19A040006)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

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 Fang, Zhang Nan, Huang Kai. Review of state estimation and life prediction for lithiumion batteries[J]. Journal of Power Supply, 2020, 18(3): 175-183. (in Chinese)

[2]

李祥瑞, 兰欣, 唐清林. 车用锂离子电池一致性分选方法研究[J]. 电源学报, 2021, 19(5): 172-177. Li Xiangrui, Lan Xin, Tang Qinglin. Research on uniform sorting method for automotive lithium-ion batteries[J]. Journal of Power Supply, 2021, 19(5): 172-177. (in Chinese)

[3]

Hu Xiaosong, Xu Le, Lin Xianke, et al. Battery lifetime prognostics[J]. Joule, 2020, 4(2): 310-346.

[4]

Schuster S F, Bach T, Fleder E, et al. Nonlinear aging characteristics of lithium-ion cells under different operational conditions[J]. Journal of Energy Storage, 2015, 1: 44-53.

[5]

Yang Zhanshe, Wang Yunhao, Kong Chenzai. Remaining useful life prediction of lithium-ion batteries based on a mixture of ensemble empirical mode decomposition and GWO-SVR model[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2517011.

[6]

Han Xuebing, Lu Languang, Zheng Yuejiu, et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle[J]. eTransportation, 2019, 1: 100005.

[7]

徐波, 雷敏, 王钋. 基于TRNN和FA-PF融合的锂离子电池RUL预测[J]. 电源学报, 2023, 21(2): 138-145. Xu Bo, Lei Min, Wang Po. RUL prediction of lithium-ion battery based on fusion of TRNN and FA-PF[J]. Journal of Power Supply, 2023, 21(2): 138-145. (in Chinese)

[8]

宋健正, 刘洋, 崔来熙, 等. 基于IGWO-MKELM的锂离子电池剩余使用寿命预测[J]. 电源学报, 2023, 21(1): 168-176. Song Jianzheng, Liu Yang, Cui Laixi, et al. Remaining useful life prediction of lithium-ion batteries based on improved grey wolf optimization multiple kernel extreme learning machine[J]. Journal of Power Supply, 2023, 21(1): 168-176. (in Chinese)

[9]

赵沁峰, 蔡艳平, 王新军. 锂离子电池全生命周期剩余使用寿命预测[J]. 电源学报, 2024, 22(2): 197-204. Zhao Qinfeng, Cai Yanping, Wang Xinjun. Remaining useful life prediction for full life cycle of lithium-ion battery[J]. Journal of Power Supply, 2024, 22(2): 197-204. (in Chinese)

[10]

王瀛洲, 倪裕隆, 郑宇清, 等. 基于ALO-SVR的锂离子电池剩余使用寿命预测[J]. 中国电机工程学报, 2021, 41(4): 1445-1457, 1550. Wang Yingzhou, Ni Yulong, Zheng Yuqing, et al. Remaining useful life prediction of lithium-ion batteries based on support vector regression optimized and ant lion optimizations[J]. Proceedings of the CSEE, 2021, 41(4): 1445-1457, 1550. (in Chinese)

[11]

杨战社, 王云浩, 孔晨再. 基于GWO-SVR的锂电池剩余使用寿命预测[J]. 电源学报, 2023, 21(2): 154-162. Yang Zhanshe, Wang Yunhao, Kong Chenzai. Prediction of remaining useful life of lithium-ion batteries based on GWO-SVR method[J]. Journal of Power Supply, 2023, 21(2): 154-162. (in Chinese)

[12]

Chen L, Zhang Yong, Zheng Ying, et al. Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation[J]. Neurocomputing, 2020, 414: 245-254.

[13]

Patil M A, Tagade P, Hariharan K S, et al. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation[J]. Applied Energy, 2015, 159: 285-297.

[14]

Zhao Qi, Qin Xiaoli, Zhao Hongbo, et al. A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries[J]. Microelectronics Reliability, 2018, 85: 99-108.

[15]

Li Xiaoyu, Zhang Lei, Wang Zhenpo, 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.

[16]

Wang Dong, Miao Qiang, Pecht M. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model[J]. Journal of Power Sources, 2013, 239: 253-264.

[17]

Yao Fang, He Wenxuan, Wu Youxi, et al. Remaining useful life prediction of lithium-ion batteries using a hybrid model[J]. Energy, 2022, 248: 123622.

[18]

Li Xiaoyu, Yuan Changgui, Wang Zhenpo. Multi-timescale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression[J]. Journal of Power Sources, 2020, 467: 228358.

[19]

Li Lingling, Wang Pengchong, Chao K H, et al. Remaining useful life prediction for lithium-ion batteries based on Gaussian processes mixture[J]. PLoS One, 2016, 11(9): e0163004.

[20]

Pang Xiaoqiong, Liu Xiaoyan, Jia Jianfang, et al. A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression[J]. Microelectronics Reliability, 2021, 127: 114405.

[21]

Chen Dan, Meng Jinhao, Huang Huanyang, et al. An empirical-data hybrid driven approach for remaining useful life prediction of lithium-ion batteries considering capacity diving[J]. Energy, 2022, 245: 123222.

[22]

Ma Yanying, Wu Lifeng, Guan Yong, et al. The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach[J]. Journal of Power Sources, 2020, 476: 228581.

[23]

Sun Wei, Zhang Chongchong. Analysis and forecasting of the carbon price using multi-resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm[J]. Applied Energy, 2018, 231: 1354-1371.

[24]

Zhao Yongping, Hu Qiankun, Xu Jianguo, et al. A robust extreme learning machine for modeling a small-scale turbojet engine[J]. Applied Energy, 2018, 218: 22-35.

[25]

Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning[J]. Electronic Markets, 2021, 31(3): 685-695.

[26]

Wei Meng, Gu Hairong, Ye Min, et al. Remaining useful life prediction of lithium-ion batteries based on Monte Carlo dropout and gated recurrent unit[J]. Energy Reports, 2021, 7: 2862-2871.

[27]

Xu Xiaodong, Yu Chuanqiang, Tang Shengjin, et al. Remaining useful life prediction of lithium-ion batteries based on Wiener processes with considering the relaxation effect[J]. Energies, 2019, 12(9): 1685.

[28]

Xu Xiaodong, Tang Shengjin, Yu Chuanqiang, et al. Remaining useful life prediction of lithium-ion batteries based on Wiener process under time-varying temperature condition[J]. Reliability Engineering & System Safety, 2021, 214: 107675.

[29]

Liu Datong, Zhou Jianbao, Pan Dawei, et al. Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning[J]. Measurement, 2015, 63: 143-151.

[30]

Jia Shun, Ma Bo, Guo Wei, et al. A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine[J]. Journal of Manufacturing Systems, 2021, 61: 773-781.

[31]

Liu Kailong, Hu Xiaosong, Wei Zhongbao, et al. Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries[J]. IEEE Transactions on Transportation Electrification, 2019, 5(4): 1225-1236.

[32]

Chinomona B, Chung C, Chang L K, et al. Long shortterm memory approach to estimate battery remaining useful life using partial data[J]. IEEE Access, 2020, 8: 165419-165431.

[33]

Hong J, Lee D, Jeong E R, et al. Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning[J]. Applied Energy, 2020, 278: 115646.

[34]

Rouhi Ardeshiri R, Ma Chengbin. Multivariate gated recurrent unit for battery remaining useful life prediction: a deep learning approach[J]. International Journal of Energy Research, 2021, 45(11): 16633-16648.

[35]

Chen Zewang, Shi Na, Ji Yufan, et al. Lithium-ion batteries remaining useful life prediction based on BLS-RVM[J]. Energy, 2021, 234: 121269.

[36]

Wang Yujie, Pan Rui, Yang Duo, et al. Remaining useful life prediction of lithium-ion battery based on discrete wavelet transform[J]. Energy Procedia, 2017, 105: 2053-2058.

[37]

Lü Guangzheng, Zhang Heng, Zhang Yujie, et al. An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration[J]. Microelectronics Reliability, 2022, 138: 114625.

[38]

Li Fan, Xu Jiuping. A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter[J]. Microelectronics Reliability, 2015, 55(7): 1035-1045.

[39]

Li Sai, Fang Huajing, Shi Bing. 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.

[40]

Chang Yang, Fang Huajing. A hybrid prognostic method for system degradation based on particle filter and relevance vector machine[J]. Reliability Engineering & System Safety, 2019, 186: 51-63.

[41]

Song Yuchen, Liu Datong, Hou Yandong, et al. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm[J]. Chinese Journal of Aeronautics, 2018, 31(1): 31-40.

[42]

Xue Zhiwei, Zhang Yong, Cheng Cheng, et al. Remaining useful life prediction of lithium-ion batteries with adaptive unscented Kalman filter and optimized support vector regression[J]. Neurocomputing, 2020, 376: 95- 102.

[43]

Zheng Xiujuan, Fang Huajing. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and shortterm capacity prediction[J]. Reliability Engineering & System Safety, 2015, 144: 74-82.

[44]

Wang F K, Mamo T. A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries[J]. Journal of Power Sources, 2018, 401: 49-54.

[45]

Ren Lei, Dong Jiabao, Wang Xiaokang, et al. A data-driven auto-CNN-LSTM prediction model for lithium-ion battery remaining useful life[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3478-3487.

[46]

Gao Dong, Huang Miaohua. Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization[J]. Journal of Power Electronics, 2017, 17(5): 1288- 1297.

[47]

He Wei, Williard N, Osterman M, et al. Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23): 10314-10321.

[48]

Severson K A, Attia P M, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4(5): 383-391.

[49]

dos Reis G, Strange C, Yadav M, et al. Lithium-ion battery data and where to find it[J]. Energy and AI, 2021, 5: 100081.

[50]

Wang Yujie, Liu Chang, Pan Rui, et al. Experimental data of lithium-ion battery and ultracapacitor under DST and UDDS profiles at room temperature[J]. Data in Brief, 2017, 12: 161-163.

[51]

Zhang Shuzhi, Guo Xu, Dou Xiaoxin, et al. A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery[J]. Sustainable Energy Technologies and Assessments, 2020, 40: 100752.

[52]

Zhang Yunwei, Tang Qiaochu, Zhang Yao, et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning[J]. Nature Communications, 2020, 11: 1706.

2025年第23卷第7期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.13234/j.issn.2095-2805.2025.7.253

接收时间：2022-11-24
首发时间：2026-10-26
出版时间：2025-11-30

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2022-11-24
修回日期：2023-04-10
录用日期：2023-05-15

基金

National Natural Science Foundation of China(11504356)

国家自然科学基金资助项目(11504356)

Natural Science Foundation of Zhejiang Province(LY19A040006)

浙江省自然科学基金资助项目(LY19A040006)

作者信息

^1.中国计量大学理学院，杭州　310018

^2.浙江省智能制造质量大数据溯源与应用重点实验室，杭州　310018

通讯作者:

王建峰（1983—　），男，通信作者，博士，副教授。研究方向：电池健康管理。E-mail：jfwang@cjlu.edu.cn。

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.7.253

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT