A novel state of health estimation method of Li-ion battery using group method of data handling

A review on the key issues for lithium-ion battery management in electric vehicles

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Power Electronics

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Prognostics methods for battery health monitoring using a bayesian framework

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A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation

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On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

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An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

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Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications

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A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries

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A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

Huang, H., Meng, J., Wang, Y., Cai, L., Peng, J., Wu, J., Xiao, Q., Liu, T., & Teodorescu, R. (2022) · Automotive Innovation · doi:10.1007/s42154-022-00175-3 · Cited by 44

Machine learning aided cyclic stability prediction for supercapacitors

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Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model

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Innovations and prognostics in battery degradation and longevity for energy storage systems

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Charge Equalization Controller Algorithm for Series-Connected Lithium-Ion Battery Storage Systems: Modeling and Applications

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Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter

Zhang, N., Xu, A., Wang, K., Han, X., Hong, W., & Hong, S. H. (2021) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.23287 · Cited by 38

Battery Health Prognosis Based on Sliding Window Sampling of Charging Curves and Independently Recurrent Neural Network

Lin, M., Wu, D., Chen, S., Meng, J., Wang, W., & Wu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3348894 · Cited by 37

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao, B., Yu, Z., Wang, H., Shuai, C., Qu, S., & Xu, M. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c08331 · Cited by 41

Capacity estimation of lithium-ion batteries with automatic feature extraction and graph-enhanced LSTM

Xu, J., Qu, J., & Xu, H. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111131 · Cited by 35

Constrained Bayesian dual-filtering for state of charge estimation of lithium-ion batteries

Dong, G., Wei, J., & Chen, Z. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.02.005 · Cited by 36

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

Ali, M. U., Kamran, M. A., Kumar, P. S., ., H., Nengroo, S. H., Khan, M. A., Hussain, A., & Kim, H. (2018) · Energies · doi:10.3390/en11112940 · Cited by 35

A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm

Rahbari, O., Omar, N., Firouz, Y., Rosen, M. A., Goutam, S., den Bossche, P. V., & Mierlo, J. V. (2018) · Energy · doi:10.1016/j.energy.2018.05.079 · Cited by 34

Joint state-of-health and remaining-useful-life prediction based on multi-level long short-term memory model prognostic framework considering cell voltage inconsistency reflected health indicators

Kwon, S., Han, D., Park, J., Lee, P., & Kim, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105731 · Cited by 32

Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

Yang, H., He, Z., Zhang, M., Tan, X., Sun, K., Liu, H., Wang, N., Guan, L., Wang, C., Wan, Y., et al. (2023) · EcoMat · doi:10.1002/eom2.12330 · Cited by 35

A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses Based on Real-World Data

Xu, N., Xie, Y., Liu, Q., Yue, F., & Zhao, D. (2022) · Sensors · doi:10.3390/s22155762 · Cited by 32

A deep learning approach to estimate the state of health of lithium-ion batteries under varied and incomplete working conditions

Zhang, Y., Wang, Y., Xia, Y. F., & Chen, W. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106323 · Cited by 33

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Rahman, M. M., Shafiullah, M., Rahman, S. M., Khondaker, A. N., Amao, A. O., & Zahir, M. H. (2020) · Sustainability · doi:10.3390/su12104045 · Cited by 31

State of Health Diagnosis and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Multi-Feature Data and Mechanism Fusion

Xu, J., Zhen, A., Cai, Z., Wang, P., Gao, K., & Jiang, D. (2021) · IEEE Access · doi:10.1109/access.2021.3083395 · Cited by 31

Safety and reliability analysis of lithium-ion batteries with real-time health monitoring

Khan, A., Naqvi, I. H., Bhargava, C., Lin, C.-P., Boles, S. T., Kong, L., & PECHT, M. G. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115408 · Cited by 31

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Yu, J., Mo, B., Tang, D., Yang, J. R., Wan, J., & Liu, J. (2017) · Energies · doi:10.3390/en10122012 · Cited by 30

A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries

Qiu, X., Yan, W., Wang, S., & Chen, K. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124245 · Cited by 30

Evaluation of the state of charge of lithium-ion batteries using ultrasonic guided waves and artificial neural network

Liu, Y., Zhang, R., & Hao, W. (2022) · Ionics · doi:10.1007/s11581-022-04568-6 · Cited by 29

Study on Co-Estimation of SoC and SoH for Second-Use Lithium-Ion Power Batteries

Jiang, N., & Pang, H. (2022) · Electronics · doi:10.3390/electronics11111789 · Cited by 29

Research on online estimation of available capacity of lithium batteries based on daily charging data

Zhou, D., Zheng, W., Fu, P., & Pan, X. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.227713 · Cited by 28

Cloud-to-edge based state of health estimation method for Lithium-ion battery in distributed energy storage system

Wu, J., Liu, X., Meng, J., & Lin, M. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102974 · Cited by 29

State of Health Estimation of Lithium-Ion Batteries Based on Multihealth Features Fusion and Improved Group Method of Data Handling

Wei, Z., Sun, X., Wang, J., Liu, W., Rong, C., & Liu, C. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3417600 · Cited by 29

Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

Yu, J., Yang, J. R., Wu, Y., Tang, D., & Dai, J. (2020) · International Journal of Energy Research · doi:10.1002/er.5750 · Cited by 27

Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine

Ezemobi, E., Tonoli, A., & Silvagni, M. (2021) · Energies · doi:10.3390/en14082243 · Cited by 27

Li-ion battery state-of-health estimation based on the combination of statistical and geometric features of the constant-voltage charging stage

Chen, S., Liang, Z., Yuan, H., Yang, L., Xu, F., & Zhang, Y. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108647 · Cited by 26

A Health Monitoring Method Based on Multiple Indicators to Eliminate Influences of Estimation Dispersion for Lithium-Ion Batteries

Tang, J. W., Liu, Q., Liu, S., Xie, X., Jicheng, Z., & Li, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2936213 · Cited by 26

Machine learning‐based approach for useful capacity prediction of second‐life batteries employing appropriate input selection

Bhatt, A., Ongsakul, W., Nimal Madhu, M., & Singh, J. G. (2021) · International Journal of Energy Research · doi:10.1002/er.7160 · Cited by 28

State of health forecasting of Lithium-ion batteries applicable in real-world operational conditions

von Bülow, F., Mentz, J., & Meisen, T. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103439 · Cited by 25

Online state of health estimation for lithium-ion batteries based on a dual self-attention multivariate time series prediction network

Wang, H., Li, J., Liu, X., Rao, J., Fan, Y., & Tan, X. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.07.017 · Cited by 24

Remaining-Useful-Life Prediction for Li-Ion Batteries

Chang, Y., Hsieh, Y.-C., Chai, Y.-H., & Lin, H.-W. (2023) · Energies · doi:10.3390/en16073096 · Cited by 23

Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm

Song, X., Liu, P., Chen, K., Liu, K., Gao, G., & Wu, G. (2023) · International Journal of Green Energy · doi:10.1080/15435075.2023.2264959 · Cited by 23

Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

Jeon, S., Kim, J., Ahn, J., & Cha, H. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3012230 · Cited by 22

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

Huang, K., Guo, Y., Tseng, M., Wu, K., & Li, Z. (2018) · Applied Sciences · doi:10.3390/app8101803 · Cited by 21

State of Health Estimation for Lithium-Ion Battery Based on Long Short Term Memory Networks

Chen, Z., Song, X., Xiao, R., Shen, J., & Xia, X. (2019) · DEStech Transactions on Environment Energy and Earth Science · doi:10.12783/dteees/iceee2018/27855 · Cited by 21

Capacity Estimation of Li-Ion Batteries Using Constant Current Charging Voltage With Multilayer Perceptron

Park, M., Seo, M., Song, Y., & Kim, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3028095 · Cited by 22

Battery Capacity Estimation Based on Incremental Capacity Analysis Considering Charging Current Rate

Lin, Y., Jiang, B., & Dai, H. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12040224 · Cited by 20

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang, Z., Sugiarto, L., & Lu, Y.-C. (2023) · EcoMat · doi:10.1002/eom2.12345 · Cited by 20

Neural network for the estimation of LFP battery SOH cycled at different power levels

Scarpelli, C., Gazzarri, J., Huria, T., Lutzemberger, G., & Ceraolo, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107027 · Cited by 20

Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

Rahbari, O., Mayet, C., Omar, N., & Mierlo, J. V. (2018) · Applied Sciences · doi:10.3390/app8081301 · Cited by 20

Online state of health monitoring of lithium-ion battery based on model error spectrum for electric vehicle applications

Chen, Z., Li, S., Cai, X., Zhou, N., & Cui, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103507 · Cited by 19

Online Estimation of the State of Health for the Lithium-Ion Battery Based on Open Circuit Model

Pan, Y., Li, J., Tian’an, W., Shengxun, Y., Yunxuan, Z., Zigang, L., & Yanwen, H. (2025) · IEEE Access · doi:10.1109/access.2025.3536166 · Cited by 18

Lithium Ion Batteries: Characteristics, Recycling and Deep‐Sea Mining

Bokkassam, S. D., & Krishnan, J. N. (2024) · Battery energy · doi:10.1002/bte2.20240022 · Cited by 18

A novel unsupervised domain adaptation-based method for lithium-ion batteries state of health prognostic

Qiu, X., Bai, Y., & Wang, S. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109358 · Cited by 17

A review of sliding mode observers based on equivalent circuit model for battery SoC estimation

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Log-Linear Model for Predicting the Lithium-ion Battery Age Based on Resistance Extraction from Dynamic Aging Profiles

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Extremely randomized trees versus random forest, group method of data handling, and artificial neural network

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Technical Assessment of Reusing Retired Electric Vehicle Lithium-Ion Batteries in Thailand

Phophongviwat, T., Polmai, S., Manee-Inn, C., Hongesombut, K., & Sivalertporn, K. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14060161 · Cited by 14

Bayesian optimization algorithm‐based Gaussian process regression for in situ state of health prediction of minorly deformed lithium‐ion battery

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Improving electric vehicles sustainability: Accurate forecasting of lithium-ion battery health using machine learning models

Yang, C. J., Ye, Z., Xiong, X., Su, C., & Xie, M. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114280 · Cited by 18

State of Health Estimation of Lithium-Ion Battery Using Time Convolution Memory Neural Network

Zhu, C., Zheng, B., He, Z., Gao, M., Sun, C., & Bao, Z. (2021) · Mobile Information Systems · doi:10.1155/2021/4826409 · Cited by 14

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

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Charging strategy design of lithium-ion batteries for energy loss minimization based on minimum principle

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LiFePO4 battery charging strategy design considering temperature rise minimization

Chen, Z., Shu, X., Li, X., Xiao, R., & Shen, J. (2017) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.4996713 · Cited by 13

Data-Driven Semi-Empirical Model Approximation Method for Capacity Degradation of Retired Lithium-Ion Battery Considering SOC Range

Xu, W., Cao, H., Lin, X., Shu, F., Du, J., Wang, J., & Tang, J. (2023) · Applied Sciences · doi:10.3390/app132111943 · Cited by 13

State of Health Estimation for Power Battery Based on Support Vector Regression and Particle Swarm Optimization Method

Yang, R., Zhang, X., Liu, G., & Hou, S. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550526 · Cited by 12

New ANN results on a major benchmark for the prediction of RUL of Lithium Ion batteries in electric vehicles

Jorge, I., Samet, A., Mesbahi, T., & Boné, R. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00196 · Cited by 12

Streamflow Estimation at Ungauged Basin using Modified Group Method of Data Handling

Badyalina, B., Shabri, A., & Marsani, M. F. (2021) · Sains Malaysiana · doi:10.17576/jsm-2021-5009-22 · Cited by 12

Boosting battery health prediction in electric vehicles via extended quantum optimized model with gaussian process regression

Soon, K. L., Soon, L. T., Lai, N. S., Paramesran, R., Lian, W. X., & Lau, C. Y. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.101086 · Cited by 12

Recent progresses in state estimation of lithium-ion battery energy storage systems: A review

Yang, Y., Zhou, Q., Zhang, L., Du, D., Zheng, M. L., Niu, Q., Gao, L., & Yuan, X. (2022) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312221124354 · Cited by 13

Lithium-ion battery state of health estimation using intelligent methods

Hemavathi, S. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100237 · Cited by 9

Sustainable graphene-based energy storage device technology: Materials, methods, monitoring and digital twin

Priyadarshi, H., Ahmed, G., Mishra, D., Srivastava, A. K., McGee, R. C. V., Shrivastava, A., & Singh, K. (2024) · Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences · doi:10.1080/10408436.2024.2311413 · Cited by 9

Joint Estimation of SOC and SOH for Single-Flow Zinc–Nickel Batteries

Song, C., Zhang, Y., Ling, Q., & Zheng, S. (2022) · Energies · doi:10.3390/en15134781 · Cited by 9

Time-Series Prediction of Iron and Silicon Content in Aluminium Electrolysis Based on Machine Learning

Chen, L., Wu, Y., Liu, Y., Liu, T., & Sheng, X. (2021) · IEEE Access · doi:10.1109/access.2021.3050548 · Cited by 7

ANN based Battery Health Monitoring - A Comprehensive Review

Bhagavatula, S. V., Yellamraju, V. R. B., Eltem, K. C., Bobba, P. B., & Marati, N. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202018401068 · Cited by 8

A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve

Lin, M., Wu, D., Zheng, G., & Wu, J. (2021) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312211040901 · Cited by 7

Cycling Lifetime Prediction Model for Lithium-ion Batteries Based on Artificial Neural Networks

Vatani, M., Vie, P. J. S., & Ulleberg, Ø. (2018) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2018.8571814 · Cited by 8

Enhanced Lithium-Ion Battery SOH Estimation Using Bayesian-Optimized CNN Deep Learning Approach

Huang, X., Wei, J., Huang, J., Zhang, Q., Zhong, R., & Lai, R. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424520207 · Cited by 7

An open access tool for exploring machine learning model choice for battery life cycle prediction

Schauser, N. S., Lininger, C. N., Leland, E. S., & Sholklapper, T. Z. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.1058999 · Cited by 7

Estimation of direct current resistance online for new energy vehicles

Ren, Y., Su, L., Sun, W., Lin, B., & Wang, Z. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.232388 · Cited by 7

Hydroclimatic Data Prediction using a New Ensemble Group Method of Data Handling Coupled with Artificial Bee Colony Algorithm

Basri, B., Mokhtar, N. A., Jan, N. A. M., Marsani, M. F., Ramli, M. F., Majid, M., & Yaacob, F. F. (2021) · Sains Malaysiana · doi:10.17576/jsm-2022-5108-24 · Cited by 6

State of Health Estimation Method for Lithium-Ion Batteries via Generalized Additivity Model and Transfer Component Analysis

Lin, M., Yan, C., & Zeng, X. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14010014 · Cited by 6

A Review of State-of-health Estimation of Lithiumion Batteries: Experiments and Data

Zhou, R., Fu, S., & Peng, W. (2020) · Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) · doi:10.1109/aparm49247.2020.9209548 · Cited by 6

Design, Analysis, and Operation of Photovoltaic Power in a Microgrid with an EESS

Makola, C. S., Roux, P. F. L., & Jordaan, J. A. (2021) · IEEE PES/IAS PowerAfrica Conference (PowerAfrica) · doi:10.1109/powerafrica52236.2021.9543402 · Cited by 6

State of Health Estimation for Lithium-ion Battery Using Fuzzy Entropy and Support Vector Machine

Sui, X., He, S., Stroe, D., & Teodorescu, R. (2020) · International Power Electronics and Motion Control Conference (EPE-PEMC) · doi:10.1109/ipemc-ecceasia48364.2020.9368182 · Cited by 5

Battery status estimation using extended belief rule base with novel rule reduction method

Zhu, H., Xiao, M., Li, J., & Liu, Z. Z. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1507/8/082019 · Cited by 5

Syllogism of Li-FePO4 Battery Cell Voltage Parameter Guess Under Aperiodic Dynamic Current Profile by Some Data-Driven Techniques: A Error-Based Statistical Comparison Between Decision Tree, Support Vector, Bee Colony, and Neural Network

Çarkıt, T., & ÇARKIT, S. (2023) · Black Sea Journal of Engineering and Science · doi:10.34248/bsengineering.1240513 · Cited by 4

A Micromesh Multi-Scaled Features Extraction Network for Li-Ion Batteries SOH Estimation

Wang, M., Chen, Y., Guo, D., & Xu, Z. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3544483 · Cited by 4

A comparative study of the electro-thermal behaviour of the high energy density lithium-ion pouch battery cell and mini module

Tahirağaoğlu, B., Sevi̇lgen, G., & Bürümlü, M. (2025) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2025.107492 · Cited by 4

State of Charge Estimation for Lithium-ion Battery Using Long Short-Term Memory Networks

Chen, H., Wang, L., Xu, Y., Jin, Y., Chen, X., Zhang, Q., Li, S., Liao, C., Wang, L., & Wang, L. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2890/1/012024 · Cited by 4

Battery Parameter Evaluation Using Unscented Kalman Filter for Electric Vehicle Drive Cycles

Bhat, C., Herle, A., Channegowda, J., & Naraharisetti, K. (2022) · IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE) · doi:10.1109/pesgre52268.2022.9715919 · Cited by 4

Batarya sağlık durumunun makine öğrenmesi ile kestirimi

ÇAVUŞ, E., & SANCAKTAR, İ. (2022) · Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.28948/ngumuh.1112985 · Cited by 4

A Comparative Review of Capacity Measurement in Energy Storage Devices

Townsend, A. K., & Gouws, R. (2023) · Energies · doi:10.3390/en16104253 · Cited by 4

Li-ion Battery Life Estimation using K-Nearest Neighbor Algorithm

Allirani, S., Pooja, K., Soundarya, E., & Nair, S. S. (2023) · International Conference on Edge Computing and Applications (ICECAA) · doi:10.1109/icecaa58104.2023.10212304 · Cited by 4

A comparison of the thermal and electrical properties of two different battery cells

Demiröz, G., Tahirağaoğlu, B., & Sevi̇lgen, G. (2025) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2025.107583 · Cited by 3

Battery management in electric vehicles: Advancements, charging standards, and associated challenges

Hasan, S., Upam, A. A.-M., Haque, M. M., Chowdhury, P., Rahman, A., & Farrok, O. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121309 · Cited by 3

Two-dimensional Modeling and Analysis of Lithium-ion Cell for Electric Vehicle Application

Priyadarshi, H., Ahmed, G., Singh, K., & Shrivastava, A. (2021) · International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) · doi:10.1109/rdcape52977.2021.9633549 · Cited by 3

A Data-Driven DAE-CNN-BiLSTM-Attention Prediction Model for the State of Health of Lithium-Ion Batteries

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SOH Estimation for Li-ion Batteries Based on Features of IC Curves and Multi-output Gaussian Process Regression Method

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