A Data-Driven Method for Extracting Aging Features to Accurately Predict the Battery Health

Data-driven prediction of battery cycle life before capacity degradation

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A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

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A review on the key issues of the lithium ion battery degradation among the whole life cycle

Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019) · eTransportation · doi:10.1016/j.etran.2019.100005 · Cited by 1,745 · see citing works

A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations

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Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives

Xiong, R., Pan, Y., Shen, W., Li, H., & Sun, F. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110048 · Cited by 731 · see citing works

Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine

Feng, X., Weng, C., He, X., Han, X., Lu, L., Ren, D., & Ouyang, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2927120 · Cited by 505 · see citing works

Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model

Gao, Y., Liu, K., Zhu, C., Zhang, X., & Zhang, D. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3066946 · Cited by 422 · see citing works

A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery

Sui, X., He, S., Vilsen, S. B., Meng, J., Teodorescu, R., & Stroe, D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117346 · Cited by 419 · see citing works

Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

Hu, X., Che, Y., Lin, X., & Onori, S. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3017090 · Cited by 376 · see citing works

A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction

Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351 · see citing works

State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression

Li, Q., Li, D., Zhao, K., Wang, L., & Wang, K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104215 · Cited by 335 · see citing works

Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis

Anseán, D., Garcia, V. M., Gonzalez, M., Viejo, C. B., Viera, J. C., Pulido, Y. F., & Sánchez, L. (2019) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2019.2891213 · Cited by 264 · see citing works

Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning

Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C., Tsou, N., Sun, F., & Li, J. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.05.007 · Cited by 246 · see citing works

A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries

Lin, M., Wu, D., Meng, J., Wu, J., & Wu, H. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230774 · Cited by 228 · see citing works

Automated Feature Extraction and Selection for Data-Driven Models of Rapid Battery Capacity Fade and End of Life

Greenbank, S., & Howey, D. A. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3106593 · Cited by 201 · see citing works

Intelligent state of health estimation for lithium-ion battery pack based on big data analysis

Song, L., Zhang, K., Liang, T., Han, X., & Zhang, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101836 · Cited by 197 · see citing works

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Chen, X., Liu, X., Shen, X. P., & Zhang, Q. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202107369 · Cited by 172 · see citing works

Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery

Chen, J. C., Chen, T., Liu, W., Cheng, C., & Li, M.-G. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101405 · Cited by 168 · see citing works

A review on state of health estimation for lithium ion batteries in photovoltaic systems

Tian, J., Xiong, R., & Shen, W. (2019) · eTransportation · doi:10.1016/j.etran.2019.100028 · Cited by 150 · see citing works

Application of Digital Twin in Smart Battery Management Systems

Wang, W., Wang, J., Tian, J., Lu, J.-H., & Xiong, R. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00577-0 · Cited by 149 · see citing works

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

Chen, Z., Sun, M., Shu, X., Xiao, R., & Shen, J. (2018) · Applied Sciences · doi:10.3390/app8060925 · Cited by 134 · see citing works

A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries

Bian, X., Wei, Z., He, J., Yan, F., & Liu, L. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3044779 · Cited by 133 · see citing works

Battery capacity fading estimation using a force-based incremental capacity analysis

Samad, N. A., Kim, Y., Siegel, J. B., & Stefanopoulou, A. G. (2016) · Journal of The Electrochemical Society · doi:10.1149/2.0511608jes · Cited by 130 · see citing works

Understanding Full-Cell Evolution and Non-chemical Electrode Crosstalk of Li-Ion Batteries

Knehr, K. W., Hodson, T., Bommier, C., Davies, G., Kim, A., & Steingart, D. A. (2018) · Joule · doi:10.1016/j.joule.2018.03.016 · Cited by 128 · see citing works

A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries

Liu, X., Zhang, X., Chen, X., Zhu, G., Yan, C., Huang, J., & Peng, H. (2021) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2021.12.004 · Cited by 127 · see citing works

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Meng, X., Jiang, R., Lin, D., Bustillo, J., Jones, T., Chen, J., Yu, Q., Du, Y., Zhang, Y., Jiang, T., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.026 · Cited by 106 · see citing works

Battery state-of-health modelling by multiple linear regression

Vilsen, S. B., & Stroe, D. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125700 · Cited by 106 · see citing works

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Wei, X., Yi-min, M., & Yan, C. (2020) · IEEE Access · doi:10.1109/access.2020.3026552 · Cited by 100 · see citing works

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Xiong, R., Wang, J., Shen, W., Tian, J., & Mu, H. (2021) · Engineering · doi:10.1016/j.eng.2020.10.022 · Cited by 98 · see citing works

State-of-health estimation based on real data of electric vehicles concerning user behavior

He, Z., Shen, X., Sun, Y., Zhao, S., Fan, B., & Pan, C. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102867 · Cited by 82 · see citing works

The state of health estimation of lithium-ion batteries based on data-driven and model fusion method

Huang, P., Gu, P., Kang, Y., Zhang, Y., Duan, B., & Zhang, C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.132742 · Cited by 82 · see citing works

Research on state of health prediction model for lithium batteries based on actual diverse data

Zhou, D., Zheng, W., Chen, S., Fu, P., Zhu, H., Song, B., Qu, X., & Wang, T. (2021) · Energy · doi:10.1016/j.energy.2021.120851 · Cited by 42 · see citing works

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Tan, X., Liu, X., Wang, H., Fan, Y., & Feng, G. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.844985 · Cited by 18 · see citing works

Feature Selection

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Online estimation of lithium‐ion batteries state of health during discharge

Liu, F., Liu, Y., Su, W., Jiao, C., & Liu, Y. (2021) · International Journal of Energy Research · doi:10.1002/er.6502 · Cited by 15 · see citing works

Review of battery state estimation methods for electric vehicles-Part II: SOH estimation

Demirci, O., Taşkın, S., Schaltz, E., & Demirci, B. A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112703 · Cited by 241 · see citing works

Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU

Zhang, C., Luo, L., Yang, Z., Zhao, S., He, Y., Wang, X., & Wang, H. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100108 · Cited by 207 · see citing works

Deep Learning Framework for Lithium-ion Battery State of Charge Estimation: Recent Advances and Future Perspectives

Tian, J., Chen, C., Shen, W., Sun, F., & Xiong, R. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102883 · Cited by 196 · see citing works

Battery state of health estimation under dynamic operations with physics-driven deep learning

Tang, A., Xu, Y., Hu, Y., Tian, J., Nie, Y., Yan, F., Tan, Y., & Yu, Q. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123632 · Cited by 118 · see citing works

A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges

Wang, Y., Guo, S., Cui, Y., Deng, L., Zhao, L., Li, J., & Wang, Z. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116125 · Cited by 107 · see citing works

IoB: Internet-of-batteries for electric Vehicles–Architectures, opportunities, and challenges

Li, H., Bin Kaleem, M., Liu, Z., Wu, Y., Liu, W., & Huang, Z. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100128 · Cited by 95 · see citing works

A novel pseudo-open-circuit voltage modeling method for accurate state-of-charge estimation of LiFePO4 batteries

Zhang, K., Xiong, R., Li, Q., Chen, C., Tian, J., & Shen, W. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121406 · Cited by 88 · see citing works

Machine learning in energy storage material discovery and performance prediction

Huang, G., Huang, F., & Dong, W. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.152294 · Cited by 81 · see citing works

Boosting battery state of health estimation based on self-supervised learning

Che, Y., Zheng, Y., Sui, X., & Teodorescu, R. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.05.034 · Cited by 78 · see citing works

State of health estimation for Lithium-ion batteries based on novel feature extraction and BiGRU-Attention model

Rongli, S., Chen, J., Li, B., & Piao, C. (2025) · Energy · doi:10.1016/j.energy.2025.134756 · Cited by 76 · see citing works

State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives

Yu, H., Zhang, H., Zhang, Z., & Yang, S. (2025) · eTransportation · doi:10.1016/j.etran.2025.100420 · Cited by 73 · see citing works

Comprehensive performance comparison among different types of features in data-driven battery state of health estimation

Feng, X., Zhang, Y., Xiong, R., & Wang, C. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123555 · Cited by 68 · see citing works

Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels

Wang, T., Ma, Z., Zou, S., Chen, Z., & Wang, P. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122332 · Cited by 64 · see citing works

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

Li, X., Yu, D., Vilsen, S. B., & Stroe, D. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.01.037 · Cited by 59 · see citing works

Battery state of health estimation under fast charging via deep transfer learning

Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works

Towards real-world battery health intelligence: A review of machine learning advances and challenges in SOH estimation

Shu, X., Shen, J., Guo, F., Liu, Y., Zhang, Y., Chen, Z., & Zhao, H. (2025) · eTransportation · doi:10.1016/j.etran.2025.100509 · Cited by 50 · see citing works

Fault evolution mechanism for lithium-ion battery energy storage system under multi-levels and multi-factors

Song, S., Tang, X., sun, Y., Sun, J., Li, F., Chen, M., Lei, Q., Sun, W., He, Z., & Zhang, L. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2023.110226 · Cited by 47 · see citing works

A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

Qiang, X., Liu, W., Lyu, Z., Ruan, H., & Li, X. (2024) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2024.100169 · Cited by 48 · see citing works

A comparative study of data-driven battery capacity estimation based on partial charging curves

Lin, C., Xu, J., Jiang, D., Hou, J., Liang, Y., Zhang, X., Li, E., & Mei, X. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.09.025 · Cited by 44 · see citing works

Online data-driven battery life prediction and quick classification based on partial charging data within 10 min

Zhang, Y., Zhao, M., & Xiong, R. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.234007 · Cited by 45 · see citing works

Early prediction of lithium-ion battery degradation with a generative pre-trained transformer

Hu, J., Fu, P., Wei, Z., Huang, Y., Early, J. M., Fly, A., & Zhang, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-66819-0 · Cited by 45 · see citing works

A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network

Zhang, T., Hou, Z., Xiao-qin, L., Chen, Q., Wang, Q., Lüddeke, C. T., Wu, L., Wu, X., & We, S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125569 · Cited by 44 · see citing works

Recent advancements and perspectives in lithium-ion battery aging: Mechanism, characterization, and prediction

Liu, Y., Lai, X., Zheng, Y., Cheng, E., Zhu, J., & Qian, L. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116670 · Cited by 47 · see citing works

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Han, C., Gao, Y., Chen, X., Liu, X., Yao, N., Yu, L., Kong, L., & Zhang, Q. (2024) · InfoMat · doi:10.1002/inf2.12521 · Cited by 44 · see citing works

A feature extraction approach for state-of-health estimation of lithium-ion battery

Piao, C., Rongli, S., Chen, J., Liu, M., & Wang, Z. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108871 · Cited by 38 · see citing works

Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves

Sun, Y., Xiong, R., Meng, X., Deng, X., Li, H., & Sun, F. (2024) · eTransportation · doi:10.1016/j.etran.2024.100347 · Cited by 38 · see citing works

Deep learning driven battery voltage-capacity curve prediction utilizing short-term relaxation voltage

Tang, A., Xu, Y., Liu, P., Tian, J., Wu, Z., Hu, Y., & Yu, Q. (2024) · eTransportation · doi:10.1016/j.etran.2024.100378 · Cited by 37 · see citing works

Towards practical data-driven battery state of health estimation: Advancements and insights targeting real-world data

Chen, H., Chen, Y., Sun, C., Huo, L., Zhang, W., Shen, P., Huang, L., Luan, W., & Chen, H. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.07.022 · Cited by 40 · see citing works

Resource-efficient artificial intelligence for battery capacity estimation using convolutional FlashAttention fusion networks

Lv, Z., & Zhao, J. (2024) · eTransportation · doi:10.1016/j.etran.2024.100383 · Cited by 37 · see citing works

AM-MFF: A multi-feature fusion framework based on attention mechanism for robust and interpretable lithium-ion battery state of health estimation

Chen, S., Liu, J., Yuan, H., Tao, Y., Xu, F., & Yang, L. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125116 · Cited by 35 · see citing works

Artificial intelligence for estimating State of Health and Remaining Useful Life of EV batteries: A systematic review

Nazim, M. S., Chakma, A., Joha, M. I., Alam, S. S., Rahman, M. M., Umam, M. K. S., & Jang, Y. M. (2025) · ICT Express · doi:10.1016/j.icte.2025.05.013 · Cited by 36 · see citing works

An innovative multitask learning - Long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions

Tao, J., Wang, S., Cao, W., Fernández, C., Blaabjerg, F., & Cheng, L. (2024) · Energy · doi:10.1016/j.energy.2024.134272 · Cited by 31 · see citing works

A fast data-driven battery capacity estimation method under non-constant current charging and variable temperature

Lin, C.-P., Xu, J., Hou, J.-Y., Jiang, D., Liang, Y., Zhang, X.-G., Li, E., & Mei, X.-S. (2023) · Energy Storage Materials · doi:10.1016/j.ensm.2023.102967 · Cited by 35 · see citing works

Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Zhou, Z., Liu, Y., Zhang, C., Shen, W., & Xiong, R. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.11.009 · Cited by 29 · see citing works

Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation

Gao, Z., Jin, Y., Zhang, Y., Zhang, Z., Li, S., Liu, J., & Wang, H. (2025) · Measurement · doi:10.1016/j.measurement.2025.117780 · Cited by 28 · see citing works

Cured Memory RUL Prediction of Solid-State Batteries Combined Progressive-Topologia Fusion Health Indicators

Wang, Z., Ma, Y., Gao, J., & Chen, H. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3528576 · Cited by 26 · see citing works

Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Wang, Y., Zhao, S., Wang, S., Ou, K., & Zhang, J. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100405 · Cited by 25 · see citing works

A deep learning approach for enhanced degradation diagnostics of NMC lithium-ion batteries via impedance spectra

Sun, Y., Xiong, R., Wang, P., Li, H., & Sun, F. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.05.014 · Cited by 25 · see citing works

Forecasting battery degradation trajectory under domain shift with domain generalization

Tan, R., Lu, X., Cheng, M., Li, J., Huang, J., & Zhang, T. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103725 · Cited by 24 · see citing works

Recent progress in state of health estimation for lithium-ion batteries: From laboratory to practical application

Fu, S., Fan, H., Jin, Z., Ji, F., Tao, Y., Dong, Y., Chen, X. T., Shao, M., Yuan, S., Wang, Y., et al. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116323 · Cited by 26 · see citing works

Deep learning to predict battery voltage behavior after uncertain cycling-induced degradation

Lu, J., Xiong, R., Tian, J., Wang, C., & Sun, F. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233473 · Cited by 23 · see citing works

Physical analysis of self-discharge mechanism for supercapacitor electrode for hybrid electric energy storage system

Zhao, W., Zuo, Z., Hamza, M., Jia, B., Feng, H., & Mei, B.-A. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100123 · Cited by 24 · see citing works

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Xiong, R., Zhao, Z., Chen, C., Li, X., & Shen, W. (2024) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-024-01024-6 · Cited by 23 · see citing works

State-of-health estimation for lithium-ion batteries using relaxation voltage under dynamic conditions

Ke, X., Hong, H., Zheng, P., Zhang, S., Zhu, L., Li, Z., Cai, J., Fan, P., Yang, J. J., Wang, J., et al. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113506 · Cited by 24 · see citing works

An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries

Jiang, F., Ren, Y., Tang, T. H., Wu, Z., Xia, Q., Sun, B., & Yang, D. (2024) · Energy · doi:10.1016/j.energy.2024.132213 · Cited by 25 · see citing works

A battery SOH estimation method based on entropy domain features and semi-supervised learning under limited sample conditions

Liu, Y., Ding, J., Cai, Y., Luo, B., Yao, L., & Wang, Z. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114822 · Cited by 22 · see citing works

State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model

Zhang, Y., Wang, A., Zhang, C., He, P., Shao, K., Cheng, K., & Zhou, Y. (2025) · Batteries · doi:10.3390/batteries11060217 · Cited by 22 · see citing works

Estimation of Parameters and State of Charge for Solid-State Batteries Based on Posterior Cramer-Rao Lower Bound Analysis

Jiang, S., Xiong, R., Chen, C., & Shen, W. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3446634 · Cited by 21 · see citing works

Robust and generalizable lithium-ion battery health estimation using multi-scale field data decomposition and fusion

Huang, X., Tao, S., Liang, C., Ma, R., Wang, X., Xia, B., & Zhang, X. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236939 · Cited by 19 · see citing works

Spatially heterogeneous degradation in LiFePO 4 //graphite pouch batteries under temperature accelerated aging process

Zhou, J., Dong, J., Lu, Y., Kang, Y., Guan, Y., Zhao, F., Tang, R., Shen, W., Li, N., Su, Y., et al. (2025) · EES batteries. · doi:10.1039/d5eb00131e · Cited by 21 · see citing works

Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery

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Status, challenges, and promises of data‐driven battery lifetime prediction under cyber‐physical system context

Liu, Y., Chen, S., Li, P., Wan, J., & Li, X. (2024) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.12086 · Cited by 21 · see citing works

A novel lithium-ion battery state-of-health estimation method for fast-charging scenarios based on an improved multi-feature extraction and bagging temporal attention network

Fan, Y., Li, Y., Zhao, J., Wang, L., Yan, C., Wu, X., Wang, J., Gao, G., Ren, Z., Li, S., et al. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113396 · Cited by 20 · see citing works

Cross-domain state of health estimation for lithium-ion battery based on latent space consistency using few-unlabeled data

Dou, B., Hou, S., Hai, L., Zhao, Y., Fan, Y., Сун, Л., & Chen, H. (2025) · Energy · doi:10.1016/j.energy.2025.135257 · Cited by 19 · see citing works

Artificial intelligence for battery reuse, recycling and remanufacturing

Tao, S., Moura, S. J., Brandell, D., Han, Z., Urréhman, S., Zou, C., Zhang, X. B., & Zhou, G. (2026) · Nature Reviews Clean Technology · doi:10.1038/s44359-026-00167-0 · Cited by 22 · see citing works

Accurate State of Health Estimation of Battery System Based on Multistage Constant Current Charging and Behavior Analysis in Real-World Electric Vehicles

She, C., Shen, Y., Bin, G., Hong, J., & Peng, Y. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3386831 · Cited by 19 · see citing works

Capacity estimation for lithium-ion batteries based on heterogeneous stacking model with feature fusion

Mu, G., Wei, Q., Xu, Y., Zhang, H., Zhang, J., & Li, Q. (2024) · Energy · doi:10.1016/j.energy.2024.133881 · Cited by 19 · see citing works

A state of health estimation method for lithium-ion batteries based on initial charging segment and Gated Recurrent Unit neural network

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Data-driven state of health and state of safety estimation for alternative battery chemistries — A comparative review focusing on sodium-ion and LFP lithium-ion batteries

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Research on State of Health Estimation of Lithium-Ion Battery Based on Feature Extraction and EnhancementPreprint

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