Education, Science, Technology, Innovation and Life
Open Access
Sign In

A Review of Battery Aging Mechanisms and Health Status Estimation Methods under Wide Temperature Range

Download as PDF

DOI: 10.23977/fpes.2025.040107 | Downloads: 3 | Views: 178

Author(s)

Jiarong Ni 1

Affiliation(s)

1 Jiangsu Ocean University, Yangzhou, Jiangsu, China

Corresponding Author

Jiarong Ni

ABSTRACT

In the context of global energy transformation, the rapid development of new energy vehicles has put forward higher requirements, for the accuracy of lithium-ion battery state estimation. Ambient temperature changes will significantly affect the activity of internal battery materials and electrochemical reactions, resulting in increased errors in battery health status and state of charge estimation, threatening system safety. This paper reviews the attenuation mechanism of temperature in lithium-ion batteries, including electrolyte decomposition and SEI film thickening at high temperatures, lithium precipitation and interface degradation at low temperatures, and systematically analyzes the state of charge estimation methods based on model-driven (equivalent circuit, electrothermal coupling model) and data-driven (neural network, filtering algorithm). The research provides a theoretical reference for improving the adaptability of battery management systems in complex temperature environments.

KEYWORDS

Lithium-Ion Battery; SOC Estimation; Temperature

CITE THIS PAPER

Jiarong Ni, A Review of Battery Aging Mechanisms and Health Status Estimation Methods under Wide Temperature Range. Frontiers in Power and Energy Systems (2025) Vol. 4: 49-59. DOI: http://dx.doi.org/10.23977/fpes.2025.040107.

REFERENCES

[1] Wang Dengyi. Research and development status and future prospects of new energy vehicles in China[J]. China New Communications, 2019, 21(17): 236.
[2] Luo Fan, Huang Haihong, Wang Haixin. Rapid prediction of state of charge and health status of retired power batteries based on electrochemical impedance spectroscopy[J]. Chinese Journal of Scientific Instrument, 2021, 42(09): 172-180. 
[3] Fan Wenjie, Xu Guanghao, Yu Boning, et al. Research on online estimation method of internal temperature of lithium-ion batteries based on electrochemical impedance spectroscopy[J]. Proceedings of the CSEE, 2021, 41(09): 3283-3293.
[4] CHEN Lunguo, HU Minghui, CAO Kaibin, et al. Core temperature estimation based on electro-thermal model of lithium-ion batteries[J]. International Journal of Energy, 2020
[5] Huang Tengfei. State estimation and charging management of ternary lithium-ion batteries based on electro-thermal-mechanical coupling characteristics[D]. Jilin University, 2024. 
[6] Sun Yongkuan. Analysis of coupled heat dissipation characteristics of power batteries and optimization of thermal management strategies considering SOH[D]. East China Jiaotong University, 2024.
[7] Wangy, Tianj, Sunz, et al. A comprehensive review of attery modeling and state estimation approaches for advanced battery management systems [J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110015.
[8] Yang N, Zhang X, Shang B, Li G. Unbalanced discharging and aging due to temperature differences among the cells in a lithium-ion battery pack with parallel combination. Journal of Power Sources. 2016;306:733-741
[9] Yan Yukun. Automotive power battery degradation analysis and health status assessment[D]. Jilin University , 2024.
[10] Liang Haobin, Du Jianhua, Hao Xin, et al. Research status of the expansion formation mechanism of lithium batteries[J]. Energy Storage Science and Technology, 2021, 10(02): 647-657.
[11] Cao Xiaoyan, Li Ling, Wang Jia, et al. Study on the hydrolysis behavior of LiPF6[J]. Journal of Ocean University of China (Natural Science Edition), 2005,(06):148-150.
[12] Gan Lu. Construction and deposition behavior regulation of artificial SEI of lithium metal anode[D]. Zhejiang University, 2023.
[13] Huang Yusha. State of charge and health estimation of lithium-ion power batteries[D]. University of Science and Technology of China, 2022.
[14] Liu Tong. Research on numerical calculation and test method of electrochemical-mechanical coupling model of lithium iron phosphate battery[D]. Beijing Jiaotong University, 2022.
[15] Peng Ziran, Wang Shunhao, Xiao Shenping, et al. A cycle gating model for accurate estimation of SOC & SOH of electric vehicle power batteries[J]. Journal of Electronic Measurement and Instrumentation, 2024, 38(09): 11-23. 
[16] HU Xiaosong,TANG Xiaolin.Review of modeling techniques for lithium-ion traction batteries in electric vehicles Journal of Mechanical Engineering,2017,53(16):20-31
[17] JOHNSON V H.Battery performance models in ADVISOR [J].Journal of Power Sources, 2002, 110(2): 321-329. 
[18] XUJ,MICC,CAOB,etal.A new method to estimate the imstate of charpedance modelge of lithium-ion batteries based on the batter [J].JournalofPowerSources,2013,233:277-284
[19] Aung H, Low KS,Goh S T. State-of-charge estimation of Lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite [J]. IEEE Transactions on Power Electronics, 2015, 30(9): 4774-4783.
[20] Gu Kangwei. Lithium battery SOC estimation considering temperature influence[D]. Nanjing University of Posts and Telecommunications, 2023.
[21] Freeborntj, Maundyb,Elwakilas. Fractional order models of super capacitors, batteriesand fuel cells: Asurvey [J].Materials for Renewable and Sustainable Energy,2015,4(3):1-7.
[22] Zhao Xuan, Li Meiying, Yu Qiang, et al. A review of state estimation of lithium batteries for electric vehicles[J]. China Journal of Highway and Transport, 2023, 36(06):254-283.
[23] He Yeliang. Research on SOC estimation of lithium battery considering temperature influence [D]. Anhui University of Science and Technology, 2021.
[24] Kong Deyang, Ding Chenyang, Liu Jiaxin, et al. Prediction of real vehicle battery health at all times based on back propagation neural network improved by genetic algorithm [J/OL]. Journal of Tongji University (Natural Science Edition), 1-10 [2025-05-17]. 
[25] Lin Hao. Research on SOC and SOH estimation of lithium-ion batteries based on neural network [D]. Guangdong Polytechnic Normal University, 2024.
[26] Zhang Qichang. Research on SOC and SOH estimation algorithm for lithium-ion power batteries [D]. Nanjing University of Aeronautics and Astronautics, 2019.
[27] Wang Xindong, Dong Zheng, Wang Shuhua, et al. SOC estimation of lithium-ion batteries under wide temperature and multi-operating conditions based on improved open circuit voltage model and adaptive square root unscented Kalman filter[J]. Transactions of the Chinese Society of Electrotechnical Engineering, 2024, 39(24): 7950-7964.
[28] Wang Wen. Research on power battery health status assessment algorithm [D]. Qingdao University of Science and Technology, 2020.
[29] Wang Yuyuan, Li Jiabo, Zhang Fu. Battery state estimation based on least squares support vector machine with particle swarm optimization[J]. Energy Storage Science and Technology, 2020, 9(04): 1153-1158.
[30] Zhang Yue. Research on online joint estimation method of lithium power battery state based on GWO-SVM algorithm[D]. Harbin University of Science and Technology, 2023.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.