daohang fenxiangbox searchbox qikanlogonew daohangnew searchboxnew navrightzone footerzone paper
中
Home > Journals > Chain
‹ Previous Article

Decoding lithium-ion battery aging: state of health assessment and lifecycle regulation via artificial intelligence AITranslate

Yao Cheng1; Jiaqiang Tian1; Mince Li1; Xiang Dong1; Tianhong Pan1; Duo Yang2; Kuijie Li3; Jilei Liu4;
1.School of Electrical Engineering and Automation, Anhui University, Anhui 230601, China
2.School of Electrical and Information Engineering, Zhengzhou University, Henan 450001, China
3.College of Electrical Engineering and Automation, Fuzhou University, Fujian 350108, China
4.Anhui Eagoal New Energy Group Co., Ltd., Anhui 230012, China
AITranslate
Publisher: Youke Publish Co., Ltd.
Share Citation Information Add to Favorites Download PDF

    Scan to share on WeChat or Moments

Use WeChat scan.
Share with WeChat friends or Moments

Abstract AITranslate

Amid the global energy transition, lithium-ion batteries (LIBs) serve as a cornerstone of clean energy storage systems. Robust state of health (SOH) monitoring is critical for cost reduction, safety hazard mitigation, and predictive maintenance. A systematic review of artificial intelligence (AI)-driven SOH assessment technologies is presented, with a comprehensive framework established covering feature mining, state estimation, and health management. Multi-physics health features across electrical, thermal, and mechanical domains are categorized, and corresponding feature extraction methods and dimensionality reduction techniques are classified. The modeling strengths of conventional machine learning for small-to-medium datasets are elaborated, and deep learning's automatic feature extraction capability under complex operating conditions is highlighted. Applications of reinforcement learning in charge-discharge optimization, energy scheduling, and battery health management are reviewed. Key challenges are identified, including multi-physics feature decoupling difficulties, limited model generalization and interpretability, inconsistent evaluation benchmarks, and industrial deployment barriers. Future research directions include constructing multi-physics coupled feature systems, fusing electrochemical mechanisms with data-driven models, developing lightweight reinforcement learning algorithms, and advancing engineering applications through cloud-edge collaboration and digital twin technologies.

KeyWords AITranslate

deep learning feature engineering health optimization management lithium-ion batteries machine learning multi-physics features reinforcement learning state of health estimation

1.J. Q. Tian, Y. Fan, T. H. Pan, X. Zhang, J. N. Yin, and Q. P. Zhang, "A Critical Review on Inconsistency Mechanism, Evaluation Methods and Improvement Measures for Lithium-Ion Battery Energy Storage Systems," Renewable and Sustainable Energy Reviews 189, Part B (2024): 113978, https://doi.org/10.1016/j.rser.2023.113978.

2.Z. Ren and C. Q. Du, "A Review of Machine Learning State-of-Charge and State-of-Health Estimation Algorithms for Lithium-Ion Batteries," Energy Reports 9 (2023): 2993–3021, https://doi.org/10.1016/j.egy.2023.01.108.

3.X. Sui, S. He, S. B. Vilsen, J. H. Meng, R. Teodorescu, and D. I. Stroe, "A Review of Non-Probabilistic Machine Learning-Based State of Health Estimation Techniques for Lithium-Ion Battery," Applied Energy 300 (2021): 117346, https://doi.org/10.1016/j.apenergy.2021.117346.

4.K. Das and R. Kumar, "Electric Vehicle Battery Capacity Degradation and Health Estimation Using Machine-Learning Techniques: A Review," Clean Energy 7, no. 6 (2023): 1268–1281, https://doi.org/10.1093/ce/zkad054.

5.X. Shu, S. Q. Shen, J. W. Shen, et al., "State of Health Prediction of Lthium-Ion Batteries Based on Machine Learning: Advances and Perspectives," iScience 24, no. 11 (2021): 103265, https://doi.org/10.1016/j.isci.2021.103265.

6.A. Haraz, K. Abualsaud, and A. M. Massoud, "State-of-Health and State-of-Charge Estimation in Electric Vehicles Batteries: A Survey on Machine Learning Approaches," IEEE Access 12 (2024): 158110–158139, https://doi.org/10.1109/ACCESS.2024.3486989.

7.P. Eleftheriadis, M. Gangi, S. Leva, A. V. Rey, E. Groppo, and L. Grande, "Comparative Study of Machine Learning Techniques for the State of Health Estimation of Li-Ion Batteries," Electric Power Systems Research 235 (2024): 110889, https://doi.org/10.1016/j.epsr.2024.110889.

8.U. Saleem, W. L. Li, H. U. Lateef, et al., "Investigation of Deep Learning Based Techniques for Prognostic and Health Management of Lithium-Ion Battery," paper presented at the European Conference on Artificial Intelligence, June 29, 2023, https://doi.org/10.1109/ECAI58194.2023.10194122.

9.C. F. Li, L. Yang, Q. Li, et al., "SOH Estimation Method for Lithium-Ion Batteries Based on an Improved Equivalent Circuit Model via Electrochemical Impedance Spectroscopy," Journal of Energy Storage 86, Part A (2024): 111167, https://doi.org/10.1016/j.est.2024.111167.

10.K. K. Sadabadi, X. Jin, and G. Rizzoni, "Prediction of Remaining Useful Life for a Composite Electrode Lithium Ion Battery Cell Using an Electrochemical Model to Estimate the State of Health," Journal of Power Sources 481 (2021): 228861, https://doi.org/10.1016/j.jpowsour.2020.228861.

11.G. Molina-Abril, L. Calvet, A. A. Juan, and D. Riera, "Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization," Computation 13, no. 7 (2025): 173, https://doi.org/10.3390/computation13070173.

12.I. Marri, E. Petkovski, L. Cristaldi, and M. Faifer, "Comparing Machine Learning Strategies for SOH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach," Energies 16, no. 11 (2023): 4423, https://doi.org/10.3390/en16114423.

13.M. Massaoudi, H. Abu-Rub, and A. Ghrayeb, "Advancing Lithium-Ion Battery Health Prognostics with Deep Learning: A Review and Case Study," IEEE Open Journal of Industry Applications 5 (2024): 43–62, https://doi.org/10.1109/ojia.2024.3354899.

14.W. H. Li, H. Cui, T. Nemeth, et al., "Deep Reinforcement Learning-Based Energy Management of Hybrid Battery Systems in Electric Vehicles," Journal of Energy Storage 36 (2021): 102355, https://doi.org/10.1016/j.est.2021.102355.

15.K. B. Kwon and H. Zhu, "Reinforcement Learning-Based Optimal Battery Control under Cycle-Based Degradation Cost," IEEE Transactions on Smart Grid 13, no. 6 (2022): 4909–4917, https://doi.org/10.1109/TSG.2022.3180674.

16.Z. Chen, H. Q. Zhao, Y. J. Zhang, S. Q. Shen, J. W. Shen, and Y. G. Liu, "State of Health Estimation for Lithium-Ion Batteries Based on Temperature Prediction and Gated Recurrent Unit Neural Network," Journal of Power Sources 521 (2022): 230892, https://doi.org/10.1016/j.jpowsour.2021.230892.

17.J. Y. Wang, C. P. Zhang, L. J. Zhang, et al., "A Novel Aging Characteristics-Based Feature Engineering for Battery State of Health Estimation," Energy 273 (2023): 127169, https://doi.org/10.1016/j.energy.2023.127169.

18.J. L. Liu, Q. L. Duan, K. X. Qi, et al., "Capacity Fading Mechanisms and State of Health Prediction of Commercial Lithium-Ion Battery in Total Lifespan," Journal of Energy Storage 46 (2022): 103910, https://doi.org/10.1016/j.est.2021.103910.

19.X. J. Tan, Y. Q. Tan, D. Zhan, et al., "Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance," IEEE Access 8 (2020): 56811–56822, https://doi.org/10.1109/ACCESS.2020.2979570.

20.H. H. Goh, Z. T. Lan, D. D. Zhang, W. Dai, T. A. Kurniawan, and K. C. Goh, "Estimation of the State of Health (SOH) of Batteries Using Discrete Curvature Feature Extraction," Journal of Energy Storage 50 (2022): 104646, https://doi.org/10.1016/j.est.2022.104646.

21.J. Sun, C. Q. Fan, and H. Y. Yan, "SOH Estimation of Lithium-Ion Batteries Based on Multi-Feature Deep Fusion and XGBoost," Energy 306 (2024): 132429, https://doi.org/10.1016/j.energy.2024.132429.

22.M. Q. Lin, C. H. Yan, W. Wang, G. Z. Dong, J. H. Meng, and J. Wu, "A Data-Driven Approach for Estimating State-of-Health of Lithium-Ion Batteries Considering Internal Resistance," Energy 277 (2023): 127675, https://doi.org/10.1016/j.energy.2023.127675.

23.A. Gismero, K. Nørregaard, B. Johnsen, L. Stenhøj, D. I. Stroe, and E. Schaltz, "Electric Vehicle Battery State of Health Estimation Using Incremental Capacity Analysis," Journal of Energy Storage 64 (2023): 107110, https://doi.org/10.1016/j.est.2023.107110.

24.F. Xia, K. G. Wang, and J. J. Chen, "State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Disturbance-Free Incremental Capacity and Differential Voltage Analysis Method," Journal of Energy Storage 64 (2023): 107161, https://doi.org/10.1016/j.est.2023.107161.

25.B. Jiang, J. G. Zhu, X. Y. Wang, X. Z. Wei, W. L. Shang, and H. F. Dai, "A Comparative Study of Different Features Extracted from Electrochemical Impedance Spectroscopy in State of Health Estimation for Lithium-Ion Batteries," Applied Energy 322 (2022): 119502, https://doi.org/10.1016/j.apenergy.2022.119502.

26.L. Wang, J. Liu, G. B. Liu, Y. Q. Huang, X. L. Yu, and L. W. Fan, "Anisotropic Thermal Conductivity of Lithium-Ion Batteries at the Full-Cell Level: A Review of Methodology Advances, Data Analytics, and Future Applications," Advanced Materials 38, no. 5 (2026): e11928, https://doi.org/10.1002/adma.202511928.

27.L. Tendera, D. Wycisk, C. Gonzalez, J. Stadler, and K. P. Birke, "Influence of State of Health and Individual Aging Mechanisms on the Thermal Conductivity of Lithium-Ion Cells," Journal of Energy Storage 62 (2023): 106940, https://doi.org/10.1016/j.est.2023.106940.

28.J. P. Tian, R. Xiong, and W. X. Shen, "State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries," IEEE Transactions on Power Electronics 35, no. 10 (2020): 10363–10373, https://doi.org/10.1109/TPEL.2020.2978493.

29.Z. P. Wang, C. G. Yuan, and X. Y. Li, "Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry with Gaussian Process Regression," IEEE Transactions on Transportation Electrification 7, no. 1 (2021): 16–25, https://doi.org/10.1109/TTE.2020.3028784.

30.H. L. Feng and N. J. Li, "A Multi-Feature Fusion Model Based on Differential Thermal Capacity for Prediction of the Health Status of Lithium-Ion Batteries," Journal of Energy Storage 72, Part C (2023): 108419, https://doi.org/10.1016/j.est.2023.108419.

31.M. A. P. Estevez, F. V. Conte, C. Tremonti, and M. Renzi, "Aging Estimation of Lithium Ion Cells under Real-World Conditions through Mechanical Stress Measurements," Journal of Energy Storage 64 (2023): 107186, https://doi.org/10.1016/j.est.2023.107186.

32.J. Peng, S. H. Jia, S. M. Yang, X. L. Kang, H. Q. Yu, and Y. W. Yang, "State Estimation of Lithium-Ion Batteries Based on Strain Parameter Monitored by Fiber Bragg Grating Sensors," Journal of Energy Storage 52, Part B (2022): 104950, https://doi.org/10.1016/j.est.2022.104950.

33.S. J. Hou, Y. Fan, B. W. Dou, H. Li, Q. Zhang, and H. S. Chen, "Strain Feature-Assisted State of Health Estimation for Lithium-Ion Batteries," Energy 326 (2025): 136058, https://doi.org/10.1016/j.energy.2025.136058.

34.Y. C. Zhang, X. Lai, X. Zhang, et al., "Fusion of Stress and Electrical Signals for High-Accuracy Joint Estimation of SOC and SOH in Lithium-Ion Batteries," Energy 331 (2025): 137063, https://doi.org/10.1016/j.energy.2025.137063.

35.Q. H. Wu, W. H. Ye, R. J. Liang, R. Pan, T. T. Ma, and X. Quan, "A New State-of-Health Estimation Method for Retired Lithium-Ion Battery Based on Expansion," Journal of The Electrochemical Society 172, no. 6 (2025): 060511, https://doi.org/10.1149/1945-7111/add0d52.

36.Q. Xu, X. Y. Wang, H. Ye, L. L. Gong, P. Tan, and T. R. Pan, "An Accurate State of Health Estimation Method for Lithium-Ion Batteries Based on Expansion Force Analysis," Energy 325 (2025): 136155, https://doi.org/10.1016/j.energy.2025.136155.

37.K. Zhong, Z. H. Liu, P. Wang, et al., "Simultaneous Prediction of SOH and RUL for Lithium-Ion Batteries Using Transferable Knowledge Sharing Network," IEEE Transactions on Power Electronics 40, no. 9 (2025): 13741–13751, https://doi.org/10.1109/tpel.2025.3565730.

38.Z. Chen, H. Q. Zhao, Y. J. Zhang, S. Q. Shen, J. W. Shen, and Y. G. Liu, "State of Health Estimation for Lithium-Ion Batteries Based on Temperature Prediction and Gated Recurrent Unit Neural Network," Journal of Power Sources 521 (2022): 230892, https://doi.org/10.1016/j.jpowsour.2021.230892.

39.Q. W. Li and W. L. Xue, "A Review of Feature Extraction Toward Health State Estimation of Lithium-Ion Batteries," Journal of Energy Storage 112 (2025): 115453, https://doi.org/10.1016/j.est.2025.115453.

40.J. Z. Kong, F. F. Yang, X. Zhang, E. S. Pan, Z. K. Peng, and D. Wang, "Voltage-Temperature Health Feature Extraction to Improve Prognostics and Health Management of Lithium-Ion Batteries," Energy 223 (2021): 120114, https://doi.org/10.1016/j.energy.2021.120114.

41.C. Lei, J. H. Meng, D. I. Stroe, et al., "Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation with Short-Term Feature," IEEE Transactions on Power Electronics 35, no. 11 (2020): 11855–11864, https://doi.org/10.1109/TPEL.2020.2987383.

42.L. P. Chen, S. Q. Xie, A. M. Lopes, et al., "A New SOH Estimation Method for Lithium-Ion Batteries Based on Model-Data-Fusion," Energy 286 (2024): 129597, https://doi.org/10.1016/j.energy.2023.129597.

43.G. Vennam and A. Sahoo, "A Dynamic SOH-Coupled Lithium-Ion Cell Model for State and Parameter Estimation," IEEE Transactions on Energy Conversion 38, no. 2 (2023): 1186–1196, https://doi.org/10.1109/TEC.2022.3218344.

44.W. W. Xu, H. Y. Cao, X. Y. Lin, et al., "Data-Driven Semi-Empirical Model Approximation Method for Capacity Degradation of Retired Lithium-Ion Battery Considering SOC Range," Applied Sciences 13, no. 21 (2023): 11943, https://doi.org/10.3390/app132111943.

45.J. Q. Tian, X. H. Liu, S. Q. Li, et al., "Lithium-Ion Battery Health Estimation with Real-World Data for Electric Vehicles," Energy 270 (2023): 126855, https://doi.org/10.1016/j.energy.2023.126855.

46.D. Zhou, J. L. Liang, F. X. Li, et al., "SOH Prediction of Lithium-Ion Batteries Using a Hybrid Model Approach Integrating Single Particle Model and Neural Networks," Journal of Energy Storage 104, Part B (2024): 114579, https://doi.org/10.1016/j.est.2024.114579.

47.J. C. Hong, H. X. Yang, F. W. Liang, et al., "State of Health Prediction for Proton Exchange Membrane Fuel Cells Combining Semi-Empirical Model and Machine Learning," Energy 291 (2024): 130364, https://doi.org/10.1016/j.energy.2024.130364.

48.M. Q. Lin, C. H. Yan, W. Wang, G. Z. Dong, J. H. Meng, and J. Wu, "A Data-Driven Approach for Estimating State-of-Health of Lithium-Ion Batteries Considering Internal Resistance," Energy 277 (2023): 127675, https://doi.org/10.1016/j.energy.2023.127675.

49.K. Luo, H. R. Zheng, and Z. C. Shi, "A Simple Feature Extraction Method for Estimating the Whole Life Cycle State of Health of Lithium-Ion Batteries Using Transformer-Based Neural Network," Journal of Power Sources 576 (2023): 233139, https://doi.org/10.1016/j.jpowsour.2023.233139.

50.T. Etem, "Interpretable Machine Learning for Battery Health Insights: A LIME and SHAP-Based Study on EIS-Derived Features," Bulletin of the Polish Academy of Sciences, Technical Sciences 73, no. 5 (2025): e155033, https://doi.org/10.24425/bpasts.2025.155033.

51.W. C. Zhang, T. T. Li, W. X. Wu, et al., "Data-Driven State of Health Estimation in Retired Battery Based on Low and Medium-Frequency Electrochemical Impedance Spectroscopy," Measurement 211 (2023): 112597, https://doi.org/10.1016/j.measurement.2023.112597.

52.Y. X. Li, L. H. Jiang, N. J. Zhang, et al., "Early Warning Method for Thermal Runaway of Lithium-Ion Batteries under Thermal Abuse Condition Based on Online Electrochemical Impedance Monitoring," Journal of Energy Chemistry 92 (2024): 74–86, https://doi.org/10.1016/j.jechem.2023.12.049.

53.X. L. Xia, Y. Chen, J. W. Shen, et al., "State of Health Estimation for Lithium-Ion Batteries Based on Impedance Feature Selection and Improved Support Vector Regression," Energy 326 (2025): 136135, https://doi.org/10.1016/j.energy.2025.136135.

54.J. P. Wen, X. Chen, X. H. Li, and Y. K. Li, "SOH Prediction of Lithium Battery Based on IC Curve Feature and BP Neural Network," Energy 261, Part A (2022): 125234, https://doi.org/10.1016/j.energy.2022.125234.

55.Y. M. Li, X. J. Qin, M. Chai, et al., "SOH Evaluation and RUL Estimation of Lithium-Ion Batteries Based on MC-CNN-TimesNet Model," Reliability Engineering & System Safety 261 (2025): 111125, https://doi.org/10.1016/j.ress.2025.111125.

56.R. Quan, Y. L. Zhou, W. Li, and H. Wan, "A Hybrid Data-Driven Approach for SOH Estimation of Lithium-Ion Batteries Based on Health Index Extraction and QHDBO-BiTCN-BiGRU," Journal of Energy Storage 125 (2025): 116788, https://doi.org/10.1016/j.est.2025.116788.

57.X. Shu, J. W. Shen, F. X. Guo, et al., "Towards Real-World Battery Health Intelligence: A Review of Machine Learning Advances and Challenges in SOH Estimation," eTransportation 26 (2025): 100509, https://doi.org/10.1016/j.etan.2025.100509.

58.H. F. Guo, L. B. Yu, Y. P. Ren, A. W. Sun, and J. Yang, "A Dual Dimensionality Reduction and Feature Weighting Integration Transfer Learning Method for Lithium-Ion Battery State of Health Estimation," Journal of Energy Storage 130 (2025): 117475, https://doi.org/10.1016/j.est.2025.117475.

59.S. M. Peng, J. C. Zhu, T. Z. Wu, A. H. Tang, J. R. Kan, and M. Pecht, "SOH Early Prediction of Lithium-Ion Batteries Based on Voltage Interval Selection and Features Fusion," Energy 308 (2024): 132993, https://doi.org/10.1016/j.energy.2024.132993.

60.W. W. Qu, W. X. Shen, and J. Liu, "A Joint Grey Relational Analysis Based State of Health Estimation for Lithium Ion Batteries Considering Temperature Effects," Journal of Energy Storage 42 (2021): 103102, https://doi.org/10.1016/j.est.2021.103102.

61.Y. Y. Li, H. M. Sheng, Y. H. Cheng, D. I. Stroe, and R. Teodorescu, "State-of-Health Estimation of Lithium-Ion Batteries Based on Semi-Supervised Transfer Component Analysis," Applied Energy 277 (2020): 115504, https://doi.org/10.1016/j.apenergy.2020.115504.

62.S. Y. Li, Y. H. Liu, C. L. Liu, G. J. Chen, and S. C. Wang, "Enhanced State of Health Estimation for Lithium-Ion Batteries Using Statistical Feature Extraction and Feature Selection," International Journal of Electrochemical Science 20, no.6 (2025): 101012, https://doi.org/10.1016/j.ijoes.2025.101012.

63.Y. He, W. Y. Bai, L. L. Wang, H. B. Wu, and M. Ding, "SOH Estimation for Lithium-Ion Batteries: An Improved GPR Optimization Method Based on the Developed Feature Extraction," Journal of Energy Storage 83 (2024): 110678, https://doi.org/10.1016/j.est.2024.110678.

64.S. S. Su, D. Guo, Vandana, et al., "Enhancing Battery Health Estimation Using Model Selection Criteria-Based Genetic Programming," Journal of Energy Storage 102, Part A (2024): 114077, https://doi.org/10.1016/j.est.2024.114077.

65.P. Y. Lee, S. Kwon, D. Kang, I. Cho, and J. Kim, "Principle Component Analysis-Based Optimized Feature Extraction Merged with Nonlinear Regression Model for Improved State-of-Health Prediction," Journal of Energy Storage 48 (2022): 104026, https://doi.org/10.1016/j.est.2022.104026.

66.I. Marri, E. Petkovski, L. Cristaldi, and M. Faifer, "Comparing Machine Learning Strategies for SOH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach," Energies 16, no.11 (2023): 4423, https://doi.org/10.3390/en16114423.

67.T. Chen, S. H. Zheng, L. J. Xie, et al., "Energy Storage Battery State of Health Estimation Based on Singular Value Decomposition for Noise Reduction and Improved LSTM Neural Network," AIP Advances 14, no. 9 (2024): 095003, https://doi.org/10.1063/5.0217697.

68.X. Zhang, B. Chen, Y. Li, et al., "Mining and Analysis of Electric Vehicles Charging Behavior Based on the t-SNE-AP Algorithm," Journal of Electrical Engineering & Technology 21 (2026): 2343–2368, https://doi.org/10.1007/s42835-026-02596-0.

69.W. T. Wang, G. Y. Yang, M. X. Li, et al., "State-of-Health Estimation for Lithium-Ion Batteries Based on Bi-LSTM-AM and LLE Feature Extraction," Frontiers in Energy Research 11 (2023): 1205165, https://doi.org/10.3389/fenrg.2023.1205165.

70.Y. Tian, Q. Y. Dong, J. D. Tian, and X. Y. Li, "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies 16, no. 2 (2023): 674, https://doi.org/10.3390/en16020674.

71.Q. Q. Wang, M. Xie, R. Wang, and H. D. Mo, "FESD: Feature-Enhanced Structured-State-Space Diffusion Model for Battery SOH Prediction and Imputation," IEEE Transactions on Instrumentation and Measurement 74 (2025): 3600722, https://doi.org/10.1109/TIM.2025.3600722.

72.Y. J. Zhang, Y. J. Liu, J. Wang, and T. Zhang, "State-of-Health Estimation for Lithium-Ion Batteries by Combining Model-Based Incremental Capacity Analysis with Support Vector Regression," Energy 239, Part B (2022): 121986, https://doi.org/10.1016/j.energy.2021.121986.

73.Q. L. Li, D. Z. Li, K. Zhao, L. C. Wang, and K. Wang, "State of Health Estimation of Lithium-Ion Battery Based on Improved Ant Lion Optimization and Support Vector Regression," Journal of Energy Storage 50 (2022): 104215, https://doi.org/10.1016/j.est.2022.104215.

74.Y. Zhi, H. Q. Wang, and L. Wang, "A State of Health Estimation Method for Electric Vehicle Li-Ion Batteries Using GA-PSO-SVR," Complex & Intelligent Systems 8, no. 3 (2022): 2167–2182, https://doi.org/10.1007/s40747-021-00639-9.

75.L. K. Xing, X. Liu, W. F. Luo, and L. Wu, "State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR," World Electric Vehicle Journal 14, no. 5 (2023): 122, https://doi.org/10.3390/wevj14050122.

76.Y. F. Guo, K. Huang, X. Y. Yu, et al., "State-of-Health Estimation for Lithium-Ion Batteries Based on Historical Dependency of Charging Data and Ensemble SVR," Electrochimica Acta 428 (2022): 140940, https://doi.org/10.1016/j.electacta.2022.140940.

77.J. Q. Tian, R. L. Xu, Y. J. Wang, and Z. H. Chen, "Capacity Attenuation Mechanism Modeling and Health Assessment of Lithium-Ion Batteries," Energy 221 (2021): 119682, https://doi.org/10.1016/j.energy.2020.119682.

78.Y. Y. Xiao, Z. F. Lu, and F. F. Yang, "A Resnet-SVR Model for Lithium-Ion Battery State of Health Estimation," in 2023 5th International Conference on System Reliability and Safety Engineering (SRSE), 310–315, IEEE, 2023, https://doi.org/10.1109/SRSE59585.2023.10336069.

79.R. M. Alhazmi, "State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model," World Electric Vehicle Journal 15, no. 9 (2024): 385, https://doi.org/10.3390/wevj15090385.

80.G. J. Qian, Y. J. Zheng, X. Y. Li, Y. D. Sun, X. B. Han, and M. G. Ouyang, "State of Health Estimation for Lithium-Ion Batteries Using Impedance-Based Simplified Timescale Information," Applied Energy 382 (2025): 125272, https://doi.org/10.1016/j.apenergy.2025.125272.

81.Y. Reddy, B. Routh, A. Patra, and S. Mukhopadhyay, "Gaussian Process Regression Based State of Health Estimation of Lithium-Ion Batteries Using Indirect Battery Health Indicators," in 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–7, IEEE, 2021, https://doi.org/10.1109/ICPHM51084.2021.9486519.

82.Z. Chen, H. Q. Zhao, Y. J. Zhang, S. Q. Shen, J. W. Shen, and Y. G. Liu, "State of Health Estimation for Lithium-Ion Batteries Based on Temperature Prediction and Gated Recurrent Unit Neural Network," Journal of Power Sources 521 (2022): 230892, https://doi.org/10.1016/j.jpowsour.2021.230892.

83.J. Wang, R. Zhao, Q. A. Huang, et al., "High-Efficient Prediction of State of Health for Lithium-Ion Battery Based on AC Impedance Feature Tuned with Gaussian Process Regression," Journal of Power Sources 561 (2023): 232737, https://doi.org/10.1016/j.jpowsour.2023.232737.

84.Y. Zhou, G. Z. Dong, Q. Q. Tan, X. Y. Han, C. L. Chen, and J. W. Wei, "State of Health Estimation for Lithium-Ion Batteries Using Geometric Impedance Spectrum Features and Recurrent Gaussian Process Regression," Energy 262, Part B (2023): 125514, https://doi.org/10.1016/j.energy.2022.125514.

85.J. W. Wang, Z. W. Deng, T. Yu, et al., "State of Health Estimation Based on Modified Gaussian Process Regression for Lithium-Ion Batteries," Journal of Energy Storage 51 (2022): 104512, https://doi.org/10.1016/j.est.2022.104512.

86.Y. L. Yang, S. Y. Chen, T. Chen, and L. S. Huang, "State of Health Assessment of Lithium-Ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features," Journal of Energy Storage 61 (2023): 106797, https://doi.org/10.1016/j.est.2023.106797.

87.Z. Chen, H. Q. Zhao, Y. J. Zhang, S. Q. Shen, J. W. Shen, and Y. G. Liu, "State of Health Estimation for Lithium-Ion Batteries Based on Temperature Prediction and Gated Recurrent Unit Neural Network," Journal of Power Sources 521 (2022): 230892, https://doi.org/10.1016/j.jpowsour.2021.230892.

88.J. Zhao, X. B. Liang, W. J. Zhang, et al., "Lithium-Ion Battery State of Health Estimation Using Meta-Heuristic Optimization and Gaussian Process Regression," Journal of Energy Storage 58 (2023): 106319, https://doi.org/10.1016/j.est.2022.106319.

89.J. Yin, M. Zhang, and T. Feng, "State of Health Prediction for Lithium-Ion Batteries through Curve Compression and CatBoost," World Electric Vehicle Journal 14, no. 7 (2023): 180, https://doi.org/10.3390/wevj14070180.

90.M. Zhang, J. Yin, and W. L. Chen, "SOH Estimation and RUL Prediction of Lithium Batteries Based on Multidomain Feature Fusion and CatBoost Model," Energy Science & Engineering 11, no. 9 (2023): 3082–3101, https://doi.org/10.1002/ese3.1506.

91.K. S. R. Mawonou, A. Eddahech, D. Dumur, D. Beauvois, and E. Godoy, "State-of-Health Estimators Coupled to a Random Forest Approach for Lithium-Ion Battery Aging Factor Ranking," Journal of Power Sources 484 (2021): 229154, https://doi.org/10.1016/j.jpowsour.2020.229154.

92.Z. W. Chen, S. Y. Zhang, N. Shi, F. S. Li, Y. R. Wang, and J. Cui, "Online State-of-Health Estimation of Lithium-Ion Battery Based on Relevance Vector Machine with Dynamic Integration," Applied Soft Computing 129 (2022): 109615, https://doi.org/10.1016/j.asoc.2022.109615.

93.Z. Q. Lyu, G. Wang, and R. J. Gao, "Synchronous State of Health Estimation and Remaining Useful Lifetime Prediction of Li-Ion Battery through Optimized Relevance Vector Machine Framework," Energy 251 (2022): 123852, https://doi.org/10.1016/j.energy.2022.123852.

94.J. Wu, L. C. Fang, G. Z. Dong, and M. Q. Lin, "State of Health Estimation of Lithium-Ion Battery with Improved Radial Basis Function Neural Network," Energy 262, Part B (2023): 125380, https://doi.org/10.1016/j.energy.2022.125380.

95.Z. X. Sun, W. L. He, J. L. Wang, and X. He, "State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance," Energies 17, no. 11 (2024): 2487, https://doi.org/10.3390/en17112487.

96.T. Nguyen-Thoi, X. J. Cui, A. Garg, L. Gao, and T. T. Truong, "An Effective Deep Neural Network Method for Prediction of Battery State at Cell and Module Level," Energy Technology 9, no. 7 (2021): 2100048, https://doi.org/10.1002/ente.202100048.

97.J. H. Lu, R. Xiong, J. P. Tian, C. X. Wang, and F. C. Sun, "Deep Learning to Estimate Lithium-Ion Battery State of Health without Additional Degradation Experiments," Nature Communications 14 (2023): 2760, https://doi.org/10.1038/s41467-023-38458-w.

98.S. Maleki, A. Mahmoudi, and A. Yazdani, "Knowledge Transfer-Oriented Deep Neural Network Framework for Estimation and Forecasting the State of Health of the Lithium-Ion Batteries," Journal of Energy Storage 53 (2022): 105183, https://doi.org/10.1016/j.est.2022.105183.

99.X. Y. Gu, K. W. See, P. H. Li, et al., "A Novel State-of-Health Estimation for the Lithium-Ion Battery Using a Convolutional Neural Network and Transformer Model," Energy 262, Part B (2023): 125501, https://doi.org/10.1016/j.energy.2022.125501.

100.Y. Y. Soo, Y. J. Wang, H. X. Xiang, and Z. H. Chen, "Machine Learning Based Battery Pack Health Prediction Using Real-World Data," Energy 308 (2024): 132856, https://doi.org/10.1016/j.energy.2024.132856.

101.Y. H. Li, K. Li, X. Liu, et al., "A Hybrid Machine Learning Framework for Joint SOC and SOH Estimation of Lithium-Ion Batteries Assisted with Fiber Sensor Measurements," Applied Energy 325 (2022): 119787, https://doi.org/10.1016/j.apenergy.2022.119787.

102.P. H. Li, Z. J. Zhang, R. Grosu, et al., "An End-to-End Neural Network Framework for State-of-Health Estimation and Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries," Renewable and Sustainable Energy Reviews 156 (2022): 111843, https://doi.org/10.1016/j.rser.2021.111843.

103.J. P. Tian, R. Xiong, W. X. Shen, J. H. Lu, and F. C. Sun, "Flexible Battery State of Health and State of Charge Estimation Using Partial Charging Data and Deep Learning," Energy Storage Materials 51 (2022): 372–381, https://doi.org/10.1016/j.ensm.2022.06.053.

104.J. R. Chen, M. Manivanan, J. Duque, P. Kollmeyer, S. Panchal, and O. Gross, "A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation," in 2023 IEEE Transportation Electrification Conference & Expo (ITEC), 1–6, IEEE, 2023, https://doi.org/10.1109/ITEC55900.2023.10186914.

105.M. Raman, V. Champa, and V. Prema, "State of Health Estimation of Lithium Ion Batteries Using Recurrent Neural Network and Its Variants," paper presented at the 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, July 9–11, 2021, https://doi.org/10.1109/CONECCT52877.2021.9622557.

106.S. Pepe, J. P. Liu, E. Quattrocchi, and F. Ciucci, "Neural Ordinary Differential Equations and Recurrent Neural Networks for Predicting the State of Health of Batteries," ChemRxiv, February 15, 2022, https://doi.org/10.26434/chemrxiv-2022-rd73z-v2.

107.J. G. Wang, H. X. Yang, Z. P. Wang, Y. J. Zhou, P. Liu, and J. C. Hong, "State of Health Analysis of Batteries at Different Stages Based on Real-World Vehicle Data and Machine Learning," Journal of Energy Storage 88 (2024): 111616, https://doi.org/10.1016/j.est.2024.111616.

108.Z. P. Zhang, H. T. Min, H. G. Guo, et al., "State of Health Estimation Method for Lithium-Ion Batteries Using Incremental Capacity and Long Short-Term Memory Network," Journal of Energy Storage 64 (2023): 107063, https://doi.org/10.1016/j.est.2023.107063.

109.L. J. Zhang, T. Ji, S. H. Yu, and G. C. Liu, "Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method," Batteries 9, no. 3 (2023): 177, https://doi.org/10.3390/batteries9030177.

110.H. L. Sun, J. R. Sun, K. Zhao, L. C. Wang, and K. Wang, "Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation," Mathematical Problems in Engineering (2022): 9645892, https://doi.org/10.1155/2022/9645892.

111.T. T. Tao, C. Ji, J. D. Dai, et al., "Data-Based Health Indicator Extraction for Battery SOH Estimation via Deep Learning," Journal of Energy Storage 78 (2024): 109982, https://doi.org/10.1016/j.est.2023.109982.

112.P. H. Xu, C. C. Wang, J. L. Ye, and T. C. Ouyang, "State-of-Charge Estimation and Health Prognosis for Lithium-Ion Batteries Based on Temperature-Compensated Bi-LSTM Network and Integrated Attention Mechanism," IEEE Transactions on Industrial Electronics 71, no. 6 (2024): 5586–5596, https://doi.org/10.1109/TIE.2023.3292865.

113.L. Zhang, J. Y. Zhang, T. Gao, et al., "Improved LSTM Based State of Health Estimation Using Random Segments of the Charging Curves for Lithium-Ion Batteries," Journal of Energy Storage 74, Part B (2023): 109370, https://doi.org/10.1016/j.est.2023.109370.

114.M. Q. Lin, J. Wu, J. H. Meng, W. Wang, and J. Wu, "State of Health Estimation with Attentional Long Short-Term Memory Network for Lithium-Ion Batteries," Energy 268 (2023): 126706, https://doi.org/10.1016/j.energy.2023.126706.

115.Y. Ma, C. Shan, J. W. Gao, and H. Chen, "A Novel Method for State of Health Estimation of Lithium-Ion Batteries Based on Improved LSTM and Health Indicators Extraction," Energy 251 (2022): 123973, https://doi.org/10.1016/j.energy.2022.123973.

116.S. M. Peng, Y. X. Sun, D. D. Liu, Q. Q. Yu, J. R. Kan, and M. Pecht, "State of Health Estimation of Lithium-Ion Batteries Based on Multi-Health Features Extraction and Improved Long Short-Term Memory Neural Network," Energy 282 (2023): 128956, https://doi.org/10.1016/j.energy.2023.128956.

117.Y. D. Gong, X. Y. Zhang, D. Z. Gao, et al., "State-of-Health Estimation of Lithium-Ion Batteries Based on Improved Long Short-Term Memory Algorithm," Journal of Energy Storage 53 (2022): 105046, https://doi.org/10.1016/j.est.2022.105046.

118.S. S. Zhao, L. J. Luo, S. H. Jiang, and C. L. Zhang, "Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM," Journal of Electrical and Computer Engineering (2023): 5566965, https://doi.org/10.1155/2023/5566965.

119.G. Cheng, X. Z. Wang, and Y. R. He, "Remaining Useful Life and State of Health Prediction for Lithium Batteries Based on Empirical Mode Decomposition and A Long and Short Memory Neural Network," Energy 232 (2021): 121022, https://doi.org/10.1016/j.energy.2021.121022.

120.P. P. Hu, W. F. Tang, C. H. Li, S. L. Mak, C. Y. Li, and C. C. Lee, "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies 16, no. 14 (2023): 5313, https://doi.org/10.3390/en16145313.

121.C. R. Li, X. J. Han, Q. Zhang, et al., "State-of-health and Remaining-Useful-Life Estimations of Lithium-Ion Battery Based on Temporal Convolutional Network-Long Short-Term Memory," Journal of Energy Storage 74, Part B (2023): 109498, https://doi.org/10.1016/j.est.2023.109498.

122.P. Kumari and N. Kumar, "Hybrid Optimized Deep Learning Approach for Prediction of Battery State of Charge, State of Health and State of Temperature," Electrical Engineering 106, no. 3 (2024): 1283–1290, https://doi.org/10.1007/s00202-023-02105-w.

123.R. Z. Wang, J. F. Li, X. Y. Wang, S. Y. Wang, and M. Pecht, "Deep Learning Model for State of Health Estimation of Lithium Batteries Based on Relaxation Voltage," Journal of Energy Storage 79 (2024): 110189, https://doi.org/10.1016/j.est.2023.110189.

124.K. L. Wong, R. Tse, S. K. Tang, and G. Pau, "Decentralized Deep-Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning," IEEE Transactions on Transportation Electrification 10, no. 4 (2024): 8199–8212, https://doi.org/10.1109/TTE.2024.3354551.

125.C. N. Van and D. T. Quang, "Estimation of SOH and Internal Resistances of Lithium Ion Battery Based on LSTM Network," International Journal of Electrochemical Science 18, no. 6 (2023): 100166, https://doi.org/10.1016/j.ijoes.2023.100166.

126.S. Singh and P. R. Budarapu, "Deep Machine Learning Approaches for Battery Health Monitoring," Energy 300 (2024): 131540, https://doi.org/10.1016/j.energy.2024.131540.

127.J. S. Xue, W. T. Ma, X. Y. Feng, et al., "Stacking Integrated Learning Model via ELM and GRU with Mixture Correntropy Loss for Robust State of Health Estimation of Lithium-Ion Batteries," Energy 284 (2023): 129279, https://doi.org/10.1016/j.energy.2023.129279.

128.S. M. Cui and I. Joe, "A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries," IEEE Access 9 (2021): 27374–27388, https://doi.org/10.1109/ACCESS.2021.3058018.

129.C. Y. Jia, Y. K. Tian, Y. H. Shi, J. F. Jia, J. Wen, and J. C. Zeng, "State of Health Prediction of Lithium-Ion Batteries Based on Bidirectional Gated Recurrent Unit and Transformer," Energy 285 (2023): 129401, https://doi.org/10.1016/j.energy.2023.129401.

130.M. Q. Lin, Y. Q. You, W. Wang, and J. Wu, "Battery Health Prognosis with Gated Recurrent Unit Neural Networks and Hidden Markov Model Considering Uncertainty Quantification," Reliability Engineering & System Safety 230 (2023): 108978, https://doi.org/10.1016/j.ress.2022.108978.

131.R. R. Ardeshiri and C. B. Ma, "Multivariate Gated Recurrent Unit for Battery Remaining Useful Life Prediction: A Deep Learning Approach," International Journal of Energy Research 45, no. 11 (2021): 16633–16648, https://doi.org/10.1002/er.6910.

132.X. L. Bian, Z. B. Wei, W. H. Li, J. Pou, D. U. Sauer, and L. C. Liu, "State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis," IEEE Transactions on Power Electronics 37, no. 2 (2022): 2226–2236, https://doi.org/10.1109/TPEL.2021.3104723.

133.Y. S. Shi, Y. J. Hu, and X. R. Zhai, "Estimation of State of Health and Knee Point Identification in Lithium-Ion Batteries Using BES-GRU," paper presented at the 5th International Conference on Wireless Communications and Smart Grid, Journal of Physics: Conference Series, Xishuangbanna, China, June 30 to July 2, 2023, https://doi.org/10.1088/1742-6596/2615/1/012016.

134.Y. N. Sun, J. L. Zhang, K. F. Zhang, H. H. Qi, and C. J. Zhang, "Battery State of Health Estimation Method Based on Sparse Auto-Encoder and Backward Propagation Fading Diversity Among Battery Cells," International Journal of Energy Research 45, no. 5 (2021): 7651–1662, https://doi.org/10.1002/er.6346.

135.Z. Xu, Y. Guo, and J. H. Saleh, "A Physics-Informed Dynamic Deep Autoencoder for Accurate State-of-Health Prediction of Lithium-Ion Battery," Neural Computing and Applications 34 (2022): 15997–16017, https://doi.org/10.1007/s00521-022-07291-5.

136.A. H. Tang, Y. C. Xu, Y. Z. Hu, et al., "Battery State of Health Estimation under Dynamic Operations with Physics-Driven Deep Learning," Applied Energy 370 (2024): 123632, https://doi.org/10.1016/j.apenergy.2024.123632.

137.C. X. Zhu, M. Y. Gao, Z. W. He, et al., "State of Health Prediction for Li-Ion Batteries with End-to-End Deep Learning," Journal of Energy Storage 65 (2023): 107218, https://doi.org/10.1016/j.est.2023.107218.

138.J. Obregon, Y. R. Han, C. W. Ho, D. Mouraliraman, C. W. Lee, and J. Y. Jung, "Convolutional Autoencoder-Based SOH Estimation of Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy," Journal of Energy Storage 60 (2023): 106680, https://doi.org/10.1016/j.est.2023.106680.

139.J. Wu, J. X. Chen, X. Feng, H. T. Xiang, and Q. Zhu, "State of Health Estimation of Lithium-Ion Batteries Using Autoencoders and Ensemble Learning," Journal of Energy Storage 55, Part C (2022): 105708, https://doi.org/10.1016/j.est.2022.105708.

140.H. W. Xu, L. F. Wu, S. Z. Xiong, W. Li, A. Garg, and L. Gao, "An Improved CNN-LSTM Model-Based State-of-Health Estimation Approach for Lithium-Ion Batteries," Energy 276 (2023): 127585, https://doi.org/10.1016/j.energy.2023.127585.

141.E. Rajasekaran and S. Venkatanarayanan, "State-of-Health (SOH) Prediction for Electric Vehicle Battery Systems Using GAN-based Models with Triple Attention Mechanisms," Journal of Energy Storage 134, Part B (2025): 118143, https://doi.org/10.1016/j.est.2025.118143.

142.Z. Y. Bao, J. H. Jiang, C. X. Zhu, and M. Y. Gao, "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery," Energies 15, no. 12 (2022): 4399, https://doi.org/10.3390/en15124399.

143.X. Xiong, Y. J. Wang, C. Jiang, X. C. Zhang, H. X. Xiang, and Z. H. Chen, "End-to-End Deep Learning Powered Battery State of Health Estimation Considering Multi-Neighboring Incomplete Charging Data," Energy 292 (2024): 130495, https://doi.org/10.1016/j.energy.2024.130495.

144.Y. Zheng, J. Hu, J. Chen, H. Deng, and W. Hu, "State of health estimation for lithium battery random charging process based on CNN‑GRU method," paper presented at the 2022 3rd International Conference on Power, Energy and Electrical Engineering (PEEE 2022), Virtual, November 18–20, 2022, https://doi.org/10.1016/j.egyr.2022.12.093.

145.S. Jafari and Y. C. Byun, "A CNN-GRU Approach to the Accurate Prediction of Batteries' Remaining Useful Life from Charging Profiles," Computers 12, no. 11 (2023): 219, https://doi.org/10.3390/computers12110219.

146.H. Q. Zhao, Z. Chen, X. Shu, J. W. Shen, Z. Z. Lei, and Y. J. Zhang, "State of Health Estimation for Lithium-Ion Batteries Based on Hybrid Attention and Deep Learning," Reliability Engineering & System Safety 230 (2023): 109066, https://doi.org/10.1016/j.ress.2022.109066.

147.Y. K. Tian, J. Wen, Y. R. Yang, Y. H. Shi, and J. C. Zeng, "State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM," Batteries 8, no. 10 (2022): 155, https://doi.org/10.3390/batteries8100155.

148.B. L. Liu, J. L. Xu, and W. Xia, "State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series," International Journal of Energy Research (2023): 8569161, https://doi.org/10.1155/2023/8569161.

149.S. Q. Wang, K. Ou, and Y. X. Wang, "A State-of-Charge and State-of-Health Joint Estimation Method of Lithium-Ion Battery Based on Temperature-Dependent Extended Kalman Filter and Deep Learning," IEEE Transactions on Industrial Electronics 72, no. 1 (2025): 570–580, https://doi.org/10.1109/TIE.2024.3409912.

150.Y. S. Yang, Y. C. Xu, Y. W. Nie, et al., "Deep Transfer Learning Enables Battery State of Charge and State of Health Estimation," Energy 294 (2024): 130779, https://doi.org/10.1016/j.energy.2024.130779.

151.K. Liu, L. Y. Kang, and D. Xie, "Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network," Batteries 9, no. 2 (2023): 94, https://doi.org/10.3390/batteries9020094.

152.J. X. Li, B. Jin, J. D. Chang, T. Zhang, and Z. Y. Liu, "Predicting the State of Health of Lithium Battery Based on Improved LSTM-RNN," paper presented at the 2023 International Symposium on Intelligent Robotics and Systems (ISOIRS), Changsha, China, May 26–28, 2023, https://doi.org/10.1109/ISOIRS59890.2023.00020.

153.M. Y. Gao, Z. Y. Bao, C. X. Zhu, et al., "HFCM-LSTM: A Novel Hybrid Framework for State-of-Health Estimation of Lithium-Ion Battery," Energy Reports 9 (2023): 2577–2590, https://doi.org/10.1016/j.egyr.2023.01.109.

154.S. Yalçın and M. S. Herdem, "Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging," Energies 17, no. 12 (2024): 2883, https://doi.org/10.3390/en17122883.

155.Y. M. Ye, "Reinforcement Learning-Based Energy Management for Electric Vehicle Application" (PhD diss., Clemson University, 2024), https://tigerprints.clemson.edu/all_dissertations/3567.

156.S. Baccari, M. Tipaldi, and V. Mariani, "Deep Reinforcement Learning for Cell Balancing in Electric Vehicles with Dynamic Reconfigurable Batteries," IEEE Transactions on Intelligent Vehicles 9, no. 10 (2024): 6450–6461, https://doi.org/10.1109/TIV.2024.3369107.

157.N. Q. Doan, S. M. Shahid, S. J. Choi, and S. Kwon, "Deep Reinforcement Learning-Based Battery Management Algorithm for Retired Electric Vehicle Batteries with a Heterogeneous State of Health in BESSs," Energies 17, no. 1 (2024): 79, https://doi.org/10.3390/en17010079.

158.H. Y. Chun and S. H. Han, "Maximizing the Performance of a Lithium-Ion Battery Aging Estimator Using Reinforcement Learning," IEEE Transactions on Industrial Informatics 20, no. 4 (2024): 7019–7029, https://doi.org/10.1109/TII.2024.3353861.

159.S. Chen, J. L. Li, C. P. Jiang, and W. D. Xiao, "Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network," Entropy 24, no. 5 (2022): 630, https://doi.org/10.3390/e24050630.

160.X. T. Xu, L. J. Zhang, J. G. Lai, L. Z. Yang, and J. W. Xiao, "Safe Reinforcement Learning for Power Allocation of Hybrid Energy Storage Systems in Electric Vehicles Using PPO and Predictive Safety Filter," IEEE Transactions on Transportation Electrification 11, no. 1 (2025): 4024–4034, https://doi.org/10.1109/TTE.2024.3450731.

161.Y. Tavakol-Moghaddam, M. Boroushaki, and M. Astaneh, "Reinforcement Learning for Battery Energy Management: A New Balancing Approach for Li-Ion Battery Packs," Results in Engineering 23 (2024): 102532, https://doi.org/10.1016/j.rineng.2024.102532.

162.C. C. Wu, J. K. Peng, H. W. He, et al., "Health-Awareness Energy Management Strategy for Battery Electric Vehicles Based on Self-Attention Deep Reinforcement Learning," Journal of Power Sources 623 (2024): 235463, https://doi.org/10.1016/j.jpowsour.2024.235463.

163.Y. X. Yin, X. M. Zhu, and X. Zhao, "A Deep Reinforcement Learning Approach for State of Charge and State of Health Estimation in Lithium-Ion Batteries," AIP Advances 13, no. 10 (2023): 105207, https://doi.org/10.1063/5.0172683.

164.M. Q. Lin, Y. Q. You, J. H. Meng, W. Wang, J. Wu, and D. I. Stroe, "Lithium-Ion Batteries SOH Estimation with Multimodal Multilinear Feature Fusion," IEEE Transactions on Energy Conversion 38, no. 4 (2023): 2959–2968, https://doi.org/10.1109/TEC.2023.3282017.

165.J. P. Wen, X. Chen, X. H. Li, and Y. K. Li, "SOH Prediction of Lithium Battery Based on IC Curve Feature and BP Neural Network," Energy 261, Part A (2022): 125234, https://doi.org/10.1016/j.energy.2022.125234.

166.M. Zhang, D. F. Yang, J. X. Du, et al., "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies 16, no. 7 (2023): 3167, https://doi.org/10.3390/en16073167.

167.S. Q. Shen, B. C. Liu, K. Zhang, and S. Ci, "Toward Fast and Accurate SOH Prediction for Lithium-Ion Batteries," IEEE Transactions on Energy Conversion 36, no. 3 (2021): 2036–2046, https://doi.org/10.1109/TEC.2021.3052504.

168.F. J. Wang, Z. B. Zhao, Z. Zhai, et al., "Explainability-Driven Model Improvement for SOH Estimation of Lithium-Ion Battery," Reliability Engineering & System Safety 232 (2023): 109046, https://doi.org/10.1016/j.ress.2022.109046.

169.D. Kumar, R. Nasimov, A. Singh, et al., "Artificial Intelligence Approaches for SOH Estimation in Lithium-Ion Batteries: A Comprehensive Review of Progress, Critical Evaluations, Challenges, and Toward Future Outlooks," Applied Computational Intelligence and Soft Computing (2025): 5516151, https://doi.org/10.1155/acis/5516151.

170.R. Subramanya, S. A. Sierla, and V. Vyatkin, "Exploiting Battery Storages with Reinforcement Learning: A Review for Energy Professionals," IEEE Access 10 (2022): 54484–54506, https://doi.org/10.1109/ACCESS.2022.3176446.

171.W. H. Li, H. Cui, T. Nemeth, et al., "Cloud-Based Health-Conscious Energy Management of Hybrid Battery Systems in Electric Vehicles with Deep Reinforcement Learning," Applied Energy 293 (2021): 116977, https://doi.org/10.1016/j.apenergy.2021.116977.

172.H. Kang, S. Jung, H. Kim, et al., "Multi-Objective Sizing and Real-Time Scheduling of Battery Energy Storage in Energy-Sharing Community Based on Reinforcement Learning," Renewable and Sustainable Energy Reviews 185 (2023): 113655, https://doi.org/10.1016/j.rser.2023.113655.

173.L. Vichard, A. Ravey, P. Venet, F. Harel, S. Pelissier, and D. Hissel, "A Method to Estimate Battery SOH Indicators Based on Vehicle Operating Data Only," Energy 225 (2021): 120235, https://doi.org/10.1016/j.energy.2021.120235.

174.S. Li, X. Du, Y. Gao, A. Fan, Y. Zhao, and H. Du, "Enhanced Energy Storage Performance by Stabilizing Antiferroelectric Phase through High-Entropy Engineering of NaNbO3," Journal of Materials Science: Materials in Electronics 37, no. 1 (2026): 888, https://doi.org/10.1007/s10854-026-17285-x.

175.L. H. Wang, J. Lu, Z. Li, et al., "Lattice Matching of Zn2V2O7/NaV8O20 Cathode Enhances Built-in Electric Field for Durable Zinc-Ion Batteries," Journal of Electroanalytical Chemistry 1015 (2026): 120228, https://doi.org/10.1016/j.jelechem.2026.120228.

176.Y. Gao, X. Du, Z. Li, et al., "Enhanced VOCs Degradation Performance of TiO2-OV/NH2-MIL-125 by Synergistic Modulation of Oxygen Vacancy and Heterojunction," Colloids and Surfaces A: Physicochemical and Engineering Aspects 741 (2026): 140318, https://doi.org/10.1016/j.colsurfa.2026.140318.

177.M. F. Lei, M. Zhang, and K. Wang, "Research on Bidding Optimization Strategy for Virtual Power Plants with Wind–Solar–Storage Systems Based on IGDT–DRO," Electrical Engineering 108 (2026): 208, https://doi.org/10.1007/s00202-026-03544-x.

Basic Information:

DOI：10.23919/CHAIN.2026.000018

Chinese Library Classification Number:

Citation Information:

Amid the global energy transition, lithium-ion batteries (LIBs) serve as a cornerstone of clean energy storage systems. Robust state of health (SOH) monitoring is critical for cost reduction, safety hazard mitigation, and predictive maintenance. A systematic review of artificial intelligence (AI)-driven SOH assessment technologies is presented, with a comprehensive framework established covering feature mining, state estimation, and health management. Multi-physics health features across electrical, thermal, and mechanical domains are categorized, and corresponding feature extraction methods and dimensionality reduction techniques are classified. The modeling strengths of conventional machine learning for small-to-medium datasets are elaborated, and deep learning's automatic feature extraction capability under complex operating conditions is highlighted. Applications of reinforcement learning in charge-discharge optimization, energy scheduling, and battery health management are reviewed. Key challenges are identified, including multi-physics feature decoupling difficulties, limited model generalization and interpretability, inconsistent evaluation benchmarks, and industrial deployment barriers. Future research directions include constructing multi-physics coupled feature systems, fusing electrochemical mechanisms with data-driven models, developing lightweight reinforcement learning algorithms, and advancing engineering applications through cloud-edge collaboration and digital twin technologies.

quote

GB/T 7714-2015 [1] Yao Cheng, Jiaqiang Tian, Mince Li, et al. Decoding lithium-ion battery aging: state of health assessment and lifecycle regulation via artificial intelligence[J]. Chain, 2026, 3(3): 352-397. DOI:10.23919/CHAIN.2026.000018.
MLA [1] Yao Cheng, et al., "Decoding lithium-ion battery aging: state of health assessment and lifecycle regulation via artificial intelligence." Chain, vol. 3, no. 3, 2026, pp. 352-397, https://doi.org/10.23919/CHAIN.2026.000018.
APA [1] Yao Cheng, Jiaqiang Tian, Mince Li, Xiang Dong, Tianhong Pan, Duo Yang, Kuijie Li, & Jilei Liu. (2026). Decoding lithium-ion battery aging: state of health assessment and lifecycle regulation via artificial intelligence. Chain, 3(3), 352-397. https://doi.org/10.23919/CHAIN.2026.000018
IEEE [1] Yao Cheng, Jiaqiang Tian, Mince Li, Xiang Dong, Tianhong Pan, Duo Yang, Kuijie Li, and Jilei Liu, "Decoding lithium-ion battery aging: state of health assessment and lifecycle regulation via artificial intelligence," Chain, vol. 3, no. 3, pp. 352-397, 2026, doi: 10.23919/CHAIN.2026.000018. keywords: {deep learning;feature engineering;health optimization management;lithium-ion batteries;machine learning;multi-physics features;reinforcement learning;state of health estimation}