Review of key technology research on the reliability of power lithium batteries based on big data

doi:10.19799/j.cnki.2095-4239.2023.0316

Abstract

Abstract:

Lithium-ion batteries are the mainstream energy storage component for electric vehicles. The reduced reliability of lithium-ion batteries leads to abnormal performance degradation or frequent failures for electric vehicles, resulting in accidents that threaten safety. The study of battery fault diagnosis and the state of health estimation technology has become a research hotspot in the field of lithium-ion battery reliability. The deep integration of big data and electric vehicles has provided new insights into the development of key technologies for improving the reliability of lithium-ion batteries. Herein, the data characteristics of the big data platform for new energy vehicles and the data cleaning methods they utilize are first introduced. The application of key reliability technologies based on the findings from big data in electric vehicles and big data platforms is briefly reviewed. Furthermore, the previous research on battery fault diagnosis and state of health estimation analyzing the reliability of lithium-ion batteries is reviewed. Considering a data-driven model as the core method of inquiry, the research status and methods used to analyze big data pertaining to the fault diagnosis and state of health estimation of lithium-ion batteries are discussed. The advantages and disadvantages of machine learning, statistics, signaling, and fusion models in battery fault diagnosis are discussed. The theoretical basis for extracting features based on historical operating data and incremental capacity analysis is reviewed, and the battery state of health estimation models are sorted appropriately. Finally, the limitations and challenges of the current research in data cleaning, fault diagnosis, and health status prediction of lithium-ion batteries are summarized. Thus, this paper provides the future direction for the development of key reliability technologies for estimating the reliability of lithium-ion batteries.

Key words: big data, lithium-ion battery, reliability, fault diagnosis, state of health

CLC Number:

TM 912

Fang LI, Yongjun MIN, Yong ZHANG. Review of key technology research on the reliability of power lithium batteries based on big data[J]. Energy Storage Science and Technology, 2023, 12(6): 1981-1994.

Figures/Tables 8

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References 73

1	SANGUESA J A, TORRES-SANZ V, GARRIDO P, et al. A review on electric vehicles: Technologies and challenges[J]. Smart Cities, 2021, 4(1): 372-404.
2	HASAN M K, MAHMUD M, AHASAN HABIB A K M, et al. Review of electric vehicle energy storage and management system: Standards, issues, and challenges[J]. Journal of Energy Storage, 2021, 41: doi:10.1016/j.est.2021.102940.
3	GANDOMAN F H, AHMADI A, VAN DEN BOSSCHE P, et al. Status and future perspectives of reliability assessment for electric vehicles[J]. Reliability Engineering & System Safety, 2019, 183: 1-16.
4	GANDOMAN F H, JAGUEMONT J, GOUTAM S, et al. Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: Basics, progress, and challenges[J]. Applied Energy, 2019, 251: doi: 10.1016/j.apenergy.2019.113343.
5	ZHANG X H, LI Z, LUO L G, et al. A review on thermal management of lithium-ion batteries for electric vehicles[J]. Energy, 2022, 238: doi: 10.1016/j.energy.2021.121652.
6	王震坡, 袁昌贵, 李晓宇. 新能源汽车动力电池安全管理技术挑战与发展趋势分析[J]. 汽车工程, 2020, 42(12): 1606-1620.
	WANG Z P, YUAN C G, LI X Y. An analysis on challenge and development trend of safety management technologies for traction battery in new energy vehicles[J]. Automotive Engineering, 2020, 42(12): 1606-1620.
7	佘承其, 张照生, 刘鹏, 等. 大数据分析技术在新能源汽车行业的应用综述——基于新能源汽车运行大数据[J]. 机械工程学报, 2019, 55(20): 3-16.
	SHE C Q, ZHANG Z S, LIU P, et al. Overview of the application of big data analysis technology in new energy vehicle industry: Based on operating big data of new energy vehicle[J]. Journal of Mechanical Engineering, 2019, 55(20): 3-16.
8	何文轩, 耿磊, 姚芳. 电动汽车动力锂离子电池可靠性关键技术综述[J/OL]. 电源学报: 1-21[2022-08-29]. http://kns.cnki.net/kcms/detail/12.1420.tm.20220216.1529.002.html.
9	PEVEC D, VDOVIC H, GACE I, et al. Distributed data platform for automotive industry: A robust solution for tackling big challenges of big data in transportation science[C]//2019 15th International Conference on Telecommunications (ConTEL). Graz, Austria. IEEE, 2019: 1-8.
10	LV Z H, QIAO L, CAI K, et al. Big data analysis technology for electric vehicle networks in smart cities[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1807-1816.
11	顾荣. 大数据处理技术与系统研究[D]. 南京: 南京大学, 2016.
	GU R. Research on big data processing technology and system[D]. Nanjing: Nanjing University, 2016.
12	ZHOU L T, ZHAO Y, LI D, et al. State-of-health estimation for LiFePO₄ battery system on real-world electric vehicles considering aging stage[J]. IEEE Transactions on Transportation Electrification, 2022, 8(2): 1724-1733.
13	中华人民共和国工业与信息化部. GB/T 32960—2016电动汽车远程服务与管理系统技术规范[S]. 北京: 中国标准出版社, 2016.
	Ministry of Industry and Information Technology in China. GB/T 32960—2016 Technical Specifications of Remote Service and Management System for Electric Vehicles[S]. Beijing: Standards Press of China, 2016.
14	WU X G, LI M Z, DU J Y, et al. SOC prediction method based on battery pack aging and consistency deviation of thermoelectric characteristics[J]. Energy Reports, 2022, 8: 2262-2272.
15	SUN Z Y, WANG Z P, CHEN Y, et al. Modified relative entropy-based lithium-ion battery pack online short-circuit detection for electric vehicle[J]. IEEE Transactions on Transportation Electrification, 2022, 8(2): 1710-1723.
16	HOU Y K, ZHANG Z S, LIU P, et al. Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles[J]. Advances in Mechanical Engineering, 2021, 13(7): doi: 10.1177/16878140211027735.
17	王安晨. 基于多源信息融合的车载电池健康状态评估方法研究[D]. 南京: 南京林业大学, 2021.
	WANG A C. Research on evaluation method of vehicle battery health based on multi-source information fusion[D]. Nanjing: Nanjing Forestry University, 2021.
18	梁丹阳, 程相, 郗建国, 等. 基于多特征融合的动力电池RUL预测[J]. 中国测试, 2021, 47(12): 149-156.
	LIANG D Y, CHENG X, XI J G, et al. RUL prediction of power battery based on multi-feature fusion[J]. China Measurement & Testing Technology, 2021, 47(12): 149-156.
19	HUO Q, MA Z K, ZHAO X S, et al. Bayesian network based state-of-health estimation for battery on electric vehicle application and its validation through real-world data[J]. IEEE Access, 2021, 9: 11328-11341.
20	LI S Q, HE H W, LI J W. Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology[J]. Applied Energy, 2019, 242: 1259-1273.
21	LI S Q, HE H W, ZHAO P F, et al. Data cleaning and restoring method for vehicle battery big data platform[J]. Applied Energy, 2022, 320: doi: 10.1016/j.apenergy.2022.119292.
22	HU X S, ZHANG K, LIU K L, et al. Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures[J]. IEEE Industrial Electronics Magazine, 2020, 14(3): 65-91.
23	XIONG R, SUN W Z, YU Q Q, et al. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles[J]. Applied Energy, 2020, 279: doi: 10.1016/j.apenergy. 2020.115855.
24	HUA Y, LIU X H, ZHOU S D, et al. Toward sustainable reuse of retired lithium-ion batteries from electric vehicles[J]. Resources, Conservation and Recycling, 2021, 168: doi: 10.1016/j.resconrec. 2020.105249.
25	LI D, ZHANG Z S, LIU P, et al. DBSCAN-based thermal runaway diagnosis of battery systems for electric vehicles[J]. Energies, 2019, 12(15): 2977.
26	LIU P, WANG J, WANG Z P, et al. High-dimensional data abnormity detection based on improved Variance-of-Angle (VOA) algorithm for electric vehicles battery[C]//2019 IEEE Energy Conversion Congress and Exposition (ECCE). September 29-October 3, 2019, Baltimore, MD, USA. IEEE, 2019: 5072-5077.
27	向兆军, 胡凤玲, 罗明华, 等. 基于电池组模型和聚类算法的锂离子电池组SOC不一致估计[J]. 机械工程学报, 2020, 56(18): 154-163.
	XIANG Z J, HU F L, LUO M H, et al. Estimation of SOC inconsistencies in lithium-ion battery packs based on battery pack modeling and clustering algorithm[J]. Journal of Mechanical Engineering, 2020, 56(18): 154-163.
28	HONG J C, WANG Z P, YAO Y T. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks[J]. Applied Energy, 2019, 251: doi: 10.1016/j.apenergy.2019.113381.
29	LI D, LIU P, ZHANG Z S, et al. Battery thermal runaway fault prognosis in electric vehicles based on abnormal heat generation and deep learning algorithms[J]. IEEE Transactions on Power Electronics, 2022, 37(7): 8513-8525.
30	LIU Z C, ZHANG Z S, LI D, et al. Battery fault prognosis for electric vehicles based on AOM-ARIMA-LSTM in real time[C]//2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE). April 22-24, 2022, Chongqing, China. IEEE, 2022: 476-483.
31	GAN N F, SUN Z Y, ZHANG Z S, et al. Data-driven fault diagnosis of lithium-ion battery overdischarge in electric vehicles[J]. IEEE Transactions on Power Electronics, 2022, 37(4): 4575-4588.
32	WANG Z P, SONG C B, ZHANG L, et al. A data-driven method for battery charging capacity abnormality diagnosis in electric vehicle applications[J]. IEEE Transactions on Transportation Electrification, 2022, 8(1): 990-999.
33	QIN W Y, SUN W, YUAN X M, et al. Comparative analysis of battery diagnostic methodologies considering parameter learning process[C]//2021 IEEE 4th International Electrical and Energy Conference (CIEEC). May 28-30, 2021, Wuhan, China. IEEE, 2021: 1-6.
34	YIN H, WANG Z P, LIU P, et al. Voltage fault diagnosis of power batteries based on boxplots and gini impurity for electric vehicles[C]//2019 Electric Vehicles International Conference (EV). October 3-4, 2019, Bucharest, Romania. IEEE, 2019: 1-5.
35	LIU P, SUN Z Y, WANG Z P, et al. Entropy-based voltage fault diagnosis of battery systems for electric vehicles[J]. Energies, 2018, 11(1): 136.
36	LI X Y, WANG Z P. A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles[J]. Measurement, 2018, 116: 402-411.
37	HONG J C, WANG Z P, MA F, et al. Thermal runaway prognosis of battery systems using the modified multiscale entropy in real-world electric vehicles[J]. IEEE Transactions on Transportation Electrification, 2021, 7(4): 2269-2278.
38	WANG Z P, HONG J C, LIU P, et al. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles[J]. Applied Energy, 2017, 196: 289-302.
39	LI X Y, DAI K W, WANG Z P, et al. Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method[J]. Journal of Energy Storage, 2020, 27: doi: 10.1016/j.est.2019.101121.
40	HONG J C, WANG Z P, QU C H, et al. Fault prognosis and isolation of lithium-ion batteries in electric vehicles considering real-scenario thermal runaway risks[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 88-99.
41	JIANG L L, DENG Z W, TANG X L, et al. Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data[J]. Energy, 2021, 234: doi: 10.1016/j.energy. 2021.121266.
42	ZHAO Y, LIU P, WANG Z P, et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods[J]. Applied Energy, 2017, 207: 354-362.
43	SUN Z Y, HAN Y, WANG Z P, et al. Detection of voltage fault in the battery system of electric vehicles using statistical analysis[J]. Applied Energy, 2022, 307: doi: 10.1016/j.apenergy.2021.118172.
44	LI F, MIN Y J, ZHANG Y. A novel method for lithium-ion battery fault diagnosis of electric vehicle based on real-time voltage[J]. Wireless Communications and Mobile Computing, 2022, 2022: 1-17.
45	CONG X W, ZHANG C P, JIANG J C, et al. A comprehensive signal-based fault diagnosis method for lithium-ion batteries in electric vehicles[J]. Energies, 2021, 14(5): 1221.
46	JIANG J C, LI T Y, CHANG C, et al. Fault diagnosis method for lithium-ion batteries in electric vehicles based on isolated forest algorithm[J]. Journal of Energy Storage, 2022, 50: doi: 10.1016/j.est.2022.104177.
47	SUN Z Y, WANG Z P, LIU P, et al. An online data-driven fault diagnosis and thermal runaway early warning for electric vehicle batteries[J]. IEEE Transactions on Power Electronics, 2022, 37(10): 12636-12646.
48	LI Y, LIU K L, FOLEY A M, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review[J]. Renewable and Sustainable Energy Reviews, 2019, 113: doi: 10.1016/j.rser.2019. 109254.
49	TIAN H X, QIN P L, LI K, et al. A review of the state of health for lithium-ion batteries: Research status and suggestions[J]. Journal of Cleaner Production, 2020, 261: doi: 10.1016/j.jclepro.2020.120813.
50	YANG S J, ZHANG C P, JIANG J C, et al. Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications[J]. Journal of Cleaner Production, 2021, 314: doi: 10.1016/j.jclepro.2021.128015.
51	SONG L J, ZHANG K Y, LIANG T Y, et al. Intelligent state of health estimation for lithium-ion battery pack based on big data analysis[J]. Journal of Energy Storage, 2020, 32: doi: 10.1016/j.est. 2020.101836.
52	HONG J C, WANG Z P, CHEN W, et al. Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered[J]. Journal of Cleaner Production, 2021, 294: doi: 10.1016/j.jclepro.2021.125814.
53	HE Z G, SHEN X Y, SUN Y Y, et al. State-of-health estimation based on real data of electric vehicles concerning user behavior[J]. Journal of Energy Storage, 2021, 41: doi: 10.1016/j.est.2021.102867.
54	LIANG K Z, ZHANG Z S, LIU P, et al. Data-driven ohmic resistance estimation of battery packs for electric vehicles[J]. Energies, 2019, 12(24): 4772.
55	HUANG B X, LIAO H Y, WANG Y Q, et al. Prediction and evaluation of health state for power battery based on Ridge linear regression model[J]. Science Progress, 2021, 104(4): doi: 10.1177/00368504211059047.
56	周頔, 宋显华, 卢文斌, 等. 基于日常片段充电数据的锂电池健康状态实时评估方法研究[J]. 中国电机工程学报, 2019, 39(1): 105-111.
	ZHOU D, SONG X H, LU W B, et al. Real-time SOH estimation algorithm for lithium-ion batteries based on daily segment charging data[J]. Proceedings of the CSEE, 2019, 39(1): 105-111.
57	SHE C Q, LI Y, ZOU C F, et al. Offline and online blended machine learning for lithium-ion battery health state estimation[J]. IEEE Transactions on Transportation Electrification, 2022, 8(2): 1604-1618.
58	TIAN Z Y, TU L, TIAN C, et al. Understanding battery degradation phenomenon in real-life electric vehicle use based on big data[C]//2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). August 10-11, 2017, Chengdu, China. IEEE, 2017: 334-339.
59	WENG C H, FENG X N, SUN J, et al. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking[J]. Applied Energy, 2016, 180: 360-368.
60	WENG C H, SUN J, PENG H E. A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring[J]. Journal of Power Sources, 2014, 258: 228-237.
61	SCHALTZ E, STROE D I, NØRREGAARD K, et al. Incremental capacity analysis applied on electric vehicles for battery state-of-health estimation[J]. IEEE Transactions on Industry Applications, 2021, 57(2): 1810-1817.
62	XU Z C, WANG J, LUND P D, et al. Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data[J]. Energy, 2021, 225: doi: 10.1016/j.energy.2021.120160.
63	LI X Y, WANG T Y, WU C X, et al. Battery pack state of health prediction based on the electric vehicle management platform data[J]. World Electric Vehicle Journal, 2021, 12(4): 204.
64	SHE C Q, ZHANG L, WANG Z P, et al. Battery state-of-health estimation based on incremental capacity analysis method: Synthesizing from cell-level test to real-world application[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 214-223.
65	叶俊涛. 电动汽车电池健康状态在线评估研究[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	YE J T. On-line evaluation of battery health status of electric vehicle[D]. Harbin: Harbin Institute of Technology, 2020.
66	CHANG C, ZHOU X P, JIANG J C, et al. Micro-fault diagnosis of electric vehicle batteries based on the evolution of battery consistency relative position[J]. Journal of Energy Storage, 2022, 52: doi: 10.1016/j.est.2022.104746.
67	LIU P, WU Y Z, SHE C Q, et al. Comparative study of incremental capacity curve determination methods for lithium-ion batteries considering the real-world situation[J]. IEEE Transactions on Power Electronics, 2022, 37(10): 12563-12576.
68	ZHENG L F, ZHU J G, LU D D C, et al. Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries[J]. Energy, 2018, 150: 759-769.
69	SHE C Q, WANG Z P, SUN F C, et al. Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network[J]. IEEE Transactions on Industrial Informatics, 2020, 16(5): 3345-3354.
70	STROE D I, SCHALTZ E. Lithium-ion battery state-of-health estimation using the incremental capacity analysis technique[J]. IEEE Transactions on Industry Applications, 2020, 56(1): 678-685.
71	胡杰, 朱雪玲, 何陈, 等. 基于实车数据的电动汽车电池健康状态预测[J]. 汽车工程, 2021, 43(9): 1291-1299, 1313.
	HU J, ZHU X L, HE C, et al. Prediction on battery state of health of electric vehicles based on real vehicle data[J]. Automotive Engineering, 2021, 43(9): 1291-1299, 1313.
72	肖伟, 钟卫东, 舒小农, 等. 基于大数据的电池健康状态(SoH)的估算及应用[J]. 汽车安全与节能学报, 2019, 10(1): 101-105.
	XIAO W, ZHONG W D, SHU X N, et al. Battery state of health (SoH) estimation method and application based on big data[J]. Journal of Automotive Safety and Engergy, 2019, 10(1): 101-105.
73	龚贤武, 丁璐, 穆邱倩, 等. 基于实车数据的电动汽车电池健康状态估计[J]. 电源技术, 2021, 45(12): 1577-1580.
	GONG X W, DING L, MU Q Q, et al. SOH estimation of electric vehicle battery based on real segment charging data[J]. Chinese Journal of Power Sources, 2021, 45(12): 1577-1580.

数据类型	数据内容	数据类型	数据内容
数据时间	2022/3/13 5:48	电机转速/(r/min)	1900
车辆状态	启动	电机转矩/(N·m)	114
充电状态	未充电	电机温度/℃	29
车速/(km/h)	49.8	电机控制器输入电压/V	667.9
累计里程/km	151287.3	电机控制器直流母线电流/A	33
总电压/V	653.5	最高电压单体代号	10
总电流/A	36.6	最高单体电压/V	3.308
SOC/%	97	最低电压单体代号	77
DC-DC状态	工作	最低单体电压/V	3.303
绝缘电阻/kΩ	19012	探针最高温度/℃	35
加速踏板行程/%	22	探针最低温度/℃	32
制动踏板状态	制动关	最高报警等级	无故障
定位状态	有效定位(东经/北纬)	通用报警标志	—
定位信息(经纬度)	120.53°/31.30°	单体电压(0～335号)/V	3.303～3.308
电机控制器温度/℃	29	探针温度(0～63号)/℃	32～35

故障诊断方法	算法类型	参考文献	优点	缺点
机器学习	聚类方法	[25-27]	避免了单一阈值引起的虚警，准确定位故障	聚类参数的选取无明确规则
	神经网络	[28-30]	预测精准度高，可实现未来状态预测	需求数据多，易陷入过拟合，调参复杂
	集成学习	[31-32]	预测精准度高，可实现未来状态预测	需求数据多，易陷入过拟合，调参复杂
统计学和信号学	统计指标	[33-34]	方法简单，移植性好	阈值选择困难，易产生虚警或预警滞后
	熵理论	[15]、[35]、 [37-39]	数据波动时能有效检测异常	数据波动达到一定程度故障才能被检测
	相关系数	[36]	算法复杂度低，占用内存小	受噪声影响较大
	信号分解	[40]	故障诊断灵敏，能识别早期细微故障征兆	在线应用受限
	状态表示法	[41]	故障诊断灵敏，能识别早期细微故障征兆	在线应用受限
融合模型	统计指标+神经网络	[42]	统计学与神经网络的故障诊断结果互为验证	单体参数的分布情况未知
	统计指标/信号分解+聚类方法	[43]、[45]、[47]	分级诊断节约平台内存，可在早期提前发现潜在故障	融合诊断模型构建复杂
	信号分解+孤立森林	[46]	分级诊断节约平台内存，可在早期提前发现潜在故障	融合诊断模型构建复杂

SOH预估方法	算法类型/IC曲线提取方法	参考文献	优点	缺点	量化指标(SOH预估)
基于运行数据	神经网络	[51-53]、[57]	预测精度高，对序列数据有良好的拟合跟踪能力	需求数据多，易陷入过拟合，调参复杂	最大相对误差为4.5%^[51]，0.053%^[53]；最大RMSE为0.232%^[52]，2%^[57]
	贝叶斯网络	[19]	概率模型实现了泛化与训练的平衡	核函数的选取对模型性能影响较大	最大绝对误差(mean absolute error，MAE)为4%^[19]；平均相对误差为1.75%^[56]
	高斯过程回归	[56]	概率模型实现了泛化与训练的平衡	核函数的选取对模型性能影响较大	最大绝对误差(mean absolute error，MAE)为4%^[19]；平均相对误差为1.75%^[56]
	箱线图	[58]	易于实现，移植性好	无法量化电池SOH	—
基于增量容量分析法	离散IC提取	[62]	IC提取更简单，减少内存消耗	计算易受噪声影响	—
	结合等效电路	[12]、[63]	提高了SOH预估精度	参数辨识复杂	最大MAE为1.5%^[63]
	结合单体串并联的电路结构	[64]	考虑了单体与电池模组的关系	单体不一致会影响模型性能	平均RMSE为2.04%
	结合插值法	[67]	弥补了离散数据的缺点	IC曲线光滑性不高	最大RMSE为1.61%
	结合支持向量回归	[69]	改善了由于精度导致无法提取IC曲线的情况	支持向量回归处理大数据运行时间过长	平均相对误差为4%

[1]	Yuxin CHEN, Jiamu YANG, Dongbo LI, Cheng LIAN, Honglai LIU. Numerical simulation of the vacuum drying process of cylindrical lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(6): 1957-1967.
[2]	Yongli YI, Ran YU, Wu LI, Yi JIN, Zheren DAI. Preparation of Mo， Al-doped Li₇La₃Zr₂O₁₂-based composite solid electrolyte and performance of all-solid-state batterys [J]. Energy Storage Science and Technology, 2023, 12(5): 1490-1499.
[3]	Linze LI, Xiangwen ZHANG. SOH estimation for lithium-ion batteries based on combination of frequency impedance characteristics [J]. Energy Storage Science and Technology, 2023, 12(5): 1705-1712.
[4]	Luhao HAN, Ziyang WANG, Xiaolong HE, Chunshan HE, Xiaolong SHI, Bin YAO. The effect of water mist strategies on thermal runaway fire suppression of large-capacity NCM lithium-ion battery [J]. Energy Storage Science and Technology, 2023, 12(5): 1664-1674.
[5]	Jintao LI, Yue MU, Jing WANG, Jingyi QIU, Hai MING. Investigation of the structural evolution and interface behavior in cathode materials for Li-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(5): 1636-1654.
[6]	Ni YANG, Yuefeng SU, Lian WANG, Ning LI, Liang MA, Chen ZHU. Research progress of focused ion beam microscopy in lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(4): 1283-1294.
[7]	Xueli CHENG, Weifu ZHANG, Chengcheng LUO, Xiaoya YUAN. Preparation of three-dimensional graphene/Fe₃O₄composites by one-step hydrothermal method and their lithium storage performance [J]. Energy Storage Science and Technology, 2023, 12(4): 1066-1074.
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[9]	Liyue HU, Xingyan YAO. Thermal runaway of lithium-ion batteries based on orthogonal test [J]. Energy Storage Science and Technology, 2023, 12(4): 1268-1277.
[10]	Pengkai WANG, Xinyan ZHANG, Guanghao ZHANG. Remaining useful life prediction of lithium-ion batteries based on ResNet-Bi-LSTM-Attention model [J]. Energy Storage Science and Technology, 2023, 12(4): 1215-1222.
[11]	Zhi ZHAI, Fujin WANG, Yi DI, Peiyu MA, Zhibin ZHAO, Xuefeng CHEN. Hierarchical alignment transfer learning for lithium-ion battery capacity estimation [J]. Energy Storage Science and Technology, 2023, 12(4): 1223-1233.
[12]	Yiming YAO, Weiling LUAN, Ying CHEN, Min SUN. Recent progress in aging degradation of lithium-ion battery materials via in-situ optical microscopy [J]. Energy Storage Science and Technology, 2023, 12(3): 777-791.
[13]	Kuijie LI, Ping LOU, Minyuan GUAN, Jinlong MO, Weixin ZHANG, Yuancheng CAO, Shijie CHENG. A review of multi-dimensional signal evolution and coupling mechanism of lithium-ion battery thermal runaway [J]. Energy Storage Science and Technology, 2023, 12(3): 899-912.
[14]	Xinhao ZHAO, Liang XU. Improved firefly optimization algorithm to optimize back propagation neural network for state of health estimation of power lithium ion batteries [J]. Energy Storage Science and Technology, 2023, 12(3): 934-940.
[15]	Zhifu WANG, Wei LUO, Yuan YAN, Song XU, Wenmei HAO, Conglin ZHOU. Fault diagnosis of lithium-ion battery sensors using GAPSO-FNN [J]. Energy Storage Science and Technology, 2023, 12(2): 602-608.