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International Journal of Frontiers in Engineering Technology, 2022, 4(10); doi: 10.25236/IJFET.2022.041003.

A Review on State of Health Estimation for Lithium-ion Batteries


Yapeng Zhou1, Biao Guo2

Corresponding Author:
​Yapeng Zhou

1China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing, China

2Information Engineering Institute, Chongqing Vocational and Technical University of Mechatronics, Chongqing, China


As an important technology of battery management system, state of health estimation of lithium-ion battery is the basis of electric vehicle range estimation and predictive maintenance, and also an important parameter to help correct and improve the accuracy of state of charge estimation. The state of health estimation technique for lithium-ion batteries is reviewed and classified into direct and indirect methods, the advantages and disadvantages of different categories is described as well. What’s more, health indicators for state of health estimation and their practicality is analyzed. Finally, it is pointed out that state of health estimation for lithium-ion batteries on electric vehicles should possess on-board practicality while ensure accuracy at varying wide temperature window.


Electric vehicle, lithium-ion battery, state of health estimation, health indicator

Cite This Paper

Yapeng Zhou, Biao Guo. A Review on State of Health Estimation for Lithium-ion Batteries. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 10: 14-21. https://doi.org/10.25236/IJFET.2022.041003.


[1] 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: 321-339.

[2] Berecibar M, Gandiaga I, Villarreal I, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications[J]. Renewable and Sustainable Energy Reviews, 2016, 56: 572-587.

[3] Bao Y, Dong W, Wang D. Online Internal resistance measurement application in Lithium ion battery capacity and state of charge estimation[J]. Energies, 2018, 11(5): 1073.

[4] 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: 7-14.

[5] Ng K S, Moo C S, Chen Y P, et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of Lithium-ion batteries[J]. Applied Energy, 2009, 86(9): 1506-1511.

[6] Liu D T, Wang H, Peng Y, et al. Satellite Lithium-ion battery remaining cycle life prediction with novel indirect health indicator extraction[J]. Energies, 2013, 6(8): 3654-3668.

[7] Liu D T, Zhou J B, Liao H T, et al. A health indicator extraction and optimization framework for Lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems Man Cybernetics-Systems, 2015, 45(6): 915-928.

[8] Zhao Q, Qin X, Zhao H, 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.

[9] Zhang Y, Guo B. Online capacity estimation of Lithium-ion batteries based on novel feature extraction and adaptive multi-kernel relevance vector machine[J]. Energies, 2015, 8(11): 12439-12457.

[10] Feng X, Li J, Ouyang M G, et al. Using probability density function to evaluate the state of health of Lithium-ion batteries[J]. Journal of Power Sources, 2013, 232: 209-218.

[11] Chen Z, Sun M, Shu X, et al. Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine[J]. Applied Sciences, 2018, 8(6): 925.

[12] Hu C, Jain G, Zhang P, et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of Lithium-ion battery[J]. Applied Energy, 2014, 129: 49-55.

[13] Kim S. A technique for estimating the state of health of Lithium batteries through a dual-sliding-mode observer[J]. IEEE Transactions on Power Electronics, 2010, 25(4): 1013-1022.

[14] Guo Z, Qiu X, Hou G, et al. State of health estimation for Lithium ion batteries based on charging curves[J]. Journal of Power Sources, 2014, 249: 457-462.

[15] Bi J, Zhang T, Yu H, et al. State-of-health estimation of Lithium-ion battery packs in electric vehicles based on genetic resampling particle filter[J]. Applied Energy, 2016, 182: 558-568.

[16] Hu C, Youn B D, Chung J. A multiscale framework with extended Kalman filter for Lithium-ion battery SOC and capacity estimation[J]. Applied Energy, 2012, 92: 694-704.

[17] Xiong R, Sun F C, He H W. Model-based health condition monitoring method for multi-cell series-connected battery pack: 2016 IEEE Transportation Electrification Conference and Expo (Itec), Dearborn, 2016[C]. Piscataway: IEEE, 1-5.

[18] Yang R, Xiong R, He H, et al. A novel method on estimating the degradation and state of charge of Lithium-ion batteries used for electrical vehicles[J]. Applied Energy, 2017.

[19] Remmlinger J, Buchholz M, Soczka-Guth T, et al. On-board state-of-health monitoring of Lithium-ion batteries using linear parameter-varying models[J]. Journal of Power Sources, 2013, 239: 689-695.

[20] Chaoui H, Gualous H. Online parameter and state estimation of Lithium-ion batteries under temperature effects[J]. Electric Power Systems Research, 2017, 145: 73-82.

[21] Ma Z, Wang Z, Xiong R, et al. A mechanism identification model based state-of-health diagnosis of Lithium-ion batteries for energy storage applications[J]. Journal of Cleaner Production, 2018, 193: 379-390.

[22] Widodo A, Shim M C, Caesarendra W, et al. Intelligent prognostics for battery health monitoring based on sample entropy[J]. Expert Systems with Applications, 2011, 38(9): 11763-11769.

[23] Li Y, Chattopadhyay P, Ray A, et al. Identification of the battery state-of-health parameter from input–output pairs of time series data[J]. Journal of Power Sources, 2015, 285: 235-246.

[24] Li J, Lyu C, Wang L, et al. Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter[J]. Journal of Power Sources, 2014, 268: 895-903.

[25] Lu C, Tao L, Fan H. Li-ion battery capacity estimation: A geometrical approach[J]. Journal of Power Sources, 2014, 261: 141-147.

[26] Cai Y, Yang L, Deng Z, et al. Online identification of Lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine[J]. Energy, 2018, 147: 621-635.

[27] Hu X, Li S E, Jia Z, et al. Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles[J]. Energy, 2014, 64: 953-960.

[28] Piao C H, Hu Z H, Su L, et al. A novel battery state of health estimation method based on outlier detection algorithm[J]. Journal of Electrical Engineering & Technology, 2016, 11(6): 1802-1811.

[29] Wu B, Yufit V, Merla Y, et al. Differential thermal voltammetry for tracking of degradation in Lithium-ion batteries[J]. Journal of Power Sources, 2015, 273: 495-501.

[30] Goh T, Park M, Seo M, et al. Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes[J]. Energy, 2017, 135: 257-268.

[31] Weng C, Cui Y, Sun J, et al. On-board state of health monitoring of Lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235: 36-44.

[32] Wang L, Pan C, Liu L, et al. On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis[J]. Applied Energy, 2016, 168: 465-472.

[33] Prasad G. K, Rahn C D. Model based identification of aging parameters in Lithium ion batteries[J]. Journal of Power Sources, 2013, 232: 79-85.

[34] Lee J, Sung W, Choi J H. Metamodel for efficient estimation of capacity-fade uncertainty in Li-ion batteries for electric vehicles[J]. Energies, 2015, 8(6): 5538-5554.

[35] Yang J F, Xia B, Huang W X, et al. On-board state-of-health estimation based on charging current analysis for LiFePO4 batteries: 2017 IEEE Energy Conversion Congress and Exposition (Ecce), Cincinnati, 2017[C]. Piscataway: IEEE, 5229-5233.

[36] Zhang C, Zhang Y, Li Y. A novel battery state-of-health estimation method for hybrid electric vehicles [J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(5): 2604-2612.