Predicting the state of charge and health of batteries using data-driven machine learning

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Data-driven prediction of battery failure for electric vehicles

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SoC estimation of lithium-ion batteries based on machine learning techniques: A filtered approach

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Ten major challenges for sustainable lithium-ion batteries

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State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

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Prognostics for lithium-ion batteries using a two-phase gamma degradation process model

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Developing extreme fast charge battery protocols – A review spanning materials to systems

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High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression

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A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

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State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network

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Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network

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Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

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Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

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Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data

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Fast-Charging Phosphorus-Based Anodes: Promises, Challenges, and Pathways for Improvement

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Sapiential battery systems: beyond traditional electrochemical energy

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Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

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State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression

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Effectiveness of PEMFC historical state and operating mode in PEMFC prognosis

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Pathways towards high energy aqueous rechargeable batteries

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Nickel-rich nickel–cobalt–manganese and nickel–cobalt–aluminum cathodes in lithium-ion batteries: Pathways for performance optimization

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Machine learning in energy storage material discovery and performance prediction

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A framework of joint SOC and SOH estimation for lithium-ion batteries: Using BiLSTM as a battery model

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Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries via deep generative transfer learning

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Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers

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Inherently Interpretable Physics-Informed Neural Network for Battery Modeling and Prognosis

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An Automatic Weak Learner Formulation for Lithium-Ion Battery State of Health Estimation

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End-of-life electric vehicle battery disassembly enabled by intelligent and human-robot collaboration technologies: A review

Li, W., Peng, Y., Zhu, Y., Pham, D. T., Nee, A. Y. C., & Ong, S. K. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102758 · Cited by 80

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

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State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

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State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM

Tian, Y., Wen, J., Yang, Y., Shi, Y., & Zeng, J. (2022) · Batteries · doi:10.3390/batteries8100155 · Cited by 76

Advances in Fine Structure Optimizations of Layered Transition‐Metal Oxide Cathodes for Potassium‐Ion Batteries

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Optimized XGBoost modeling for accurate battery capacity degradation prediction

Jafari, S., Yang, J.-H., & Byun, Y.-C. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102786 · Cited by 74

A Practical Data-Driven Battery State-of-Health Estimation for Electric Vehicles

Rahimian, S. K., & Tang, Y. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3165295 · Cited by 73

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

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Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique

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Battery pack recycling challenges for the year 2030: Recommended solutions based on intelligent robotics for safe and efficient disassembly, residual energy detection, and secondary utilization

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Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

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Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries

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Application-oriented design of machine learning paradigms for battery science

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Unraveling the Degradation Mechanisms of Lithium-Ion Batteries

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State of health estimation for lithium-ion batteries based on impedance feature selection and improved support vector regression

Xia, X., Chen, Y., Shen, J., Liu, Y., Zhang, Y., Chen, Z., & Wei, F. (2025) · Energy · doi:10.1016/j.energy.2025.136135 · Cited by 68

A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures

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A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

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Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

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Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles

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Synergizing physics and machine learning for advanced battery management

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Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

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Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

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Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction

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Accelerated Stress Factors Based Nonlinear Wiener Process Model for Lithium-Ion Battery Prognostics

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Comparative Study of Incremental Capacity Curve Determination Methods for Lithium-Ion Batteries Considering the Real-World Situation

Liu, P., Wu, Y., She, C., Wang, Z., & Zhang, Z. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3173464 · Cited by 65

Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

Dubarry, M., & Beck, D. (2021) · Energies · doi:10.3390/en14092371 · Cited by 63

On-line WSN SoC estimation using Gaussian Process Regression: An Adaptive Machine Learning Approach

Ali, O., Ishak, M. K., Ahmed, A. B., Salleh, M. F. M., Ooi, C. A., Akbar, M. F., & Khan, I. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.02.067 · Cited by 64

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Ding, S., Li, Y., Dai, H., Wang, L., & He, X. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202301452 · Cited by 67

A Combined Data-Model Method for State-of-Charge Estimation of Lithium-Ion Batteries

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Machine learning in materials design: Algorithm and application*

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Machine learning of materials design and state prediction for lithium ion batteries

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State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review

Sylvestrin, G. R., Maciel, J. N., Amorim, M. L. M., Carmo, J. P., Afonso, J. A., Lopes, S. F., & Ando, O. H. (2025) · Energies · doi:10.3390/en18030746 · Cited by 68

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

Shi, D., Zhao, J., Wang, Z., Zhao, H., Eze, C., Wang, J., Lian, Y., & Burke, A. F. (2023) · Energies · doi:10.3390/en16093855 · Cited by 61

A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions

Ouyang, N., Zhang, W., Yin, X., Li, X., Xie, Y., He, H., & Long, Z. (2023) · Energy · doi:10.1016/j.energy.2023.127168 · Cited by 61

Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries

Matsuda, S., Lambard, G., & Sodeyama, K. (2022) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2022.100832 · Cited by 60

Opportunities and challenges in transformer neural networks for battery state estimation: Charge, health, lifetime, and safety

Zhao, J., Han, X., Wu, Y., Wang, Z., & Burke, A. F. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.11.011 · Cited by 66

Physics-informed machine learning estimation of the temperature of large-format lithium-ion batteries under various operating conditions

Shen, K., Xu, W., Lai, X., Li, D., Meng, X., Zheng, Y., & Feng, X. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.126200 · Cited by 62

Neural ordinary differential equations and recurrent neural networks for predicting the state of health of batteries

Pepe, S., Liu, J., Quattrocchi, E., & Francesco Ciucci, A. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104209 · Cited by 60

State of Charge Estimation for Lithium-Ion Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter

Shu, X., Chen, Z., Shen, J., Guo, F., Zhang, Y., & Liu, Y. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3180077 · Cited by 58

Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning

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Smart Battery Technology for Lifetime Improvement

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

Chen, X., Liu, X., Shen, X. P., & Zhang, Q. (2021) · Angewandte Chemie · doi:10.1002/ange.202107369 · Cited by 57

Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Ji, S., Zhu, J., Yang, Y., dos Reis, G., & Zhang, Z. (2024) · Small Methods · doi:10.1002/smtd.202301021 · Cited by 59

Advancement in battery health monitoring methods for electric vehicles: Battery modelling, state estimation, and internet-of-things based methods

Waseem, M., Lakshmi, G. S., Amir, M., Ahmad, M., & Suhaib, M. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236414 · Cited by 62

The state-of-charge predication of lithium-ion battery energy storage system using data-driven machine learning

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Battery monitoring system using machine learning

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LightGBM-Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions

Jiao, Z., Wang, H., Xing, J., Yang, Q., Yang, M., Zhou, Y., & Zhao, J. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3246124 · Cited by 54

Machine learning-inspired battery material innovation

Ng, M., Sun, Y., & Seh, Z. W. (2023) · Energy Advances · doi:10.1039/d3ya00040k · Cited by 59

Early prediction of battery degradation in grid-scale battery energy storage system using extreme gradient boosting algorithm

Apribowo, C. H. B., Hadi, S. P., Wijaya, F. D., Setyonegoro, M. I. B., & Sarjiya, S. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101709 · Cited by 55

Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result

Lee, J.-H., & Lee, I.-S. (2021) · Energies · doi:10.3390/en14154506 · Cited by 54

Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features

Bao, M., Liu, D., Wu, Y., Wang, Z., Yang, J., Lin, L., & Ru, Q. (2024) · Electrochimica Acta · doi:10.1016/j.electacta.2024.144449 · Cited by 57

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

Hu, Q., Lu, J., Hui, J., Rao, Z., Ren, Y., & Wang, H. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202508438 · Cited by 60

Precise and fast safety risk classification of lithium-ion batteries based on machine learning methodology

Jia, Y., Li, J., Yao, W., Li, Y., & Xu, J. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.232064 · Cited by 52

A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms

Rauf, H., Khalid, M., & Arshad, N. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107577 · Cited by 52

Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning

Wang, Z., Zhang, H., Ren, J., Lin, X., Han, T., Liu, J., & Li, J. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00494-9 · Cited by 52

Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope

Shen, J., Ma, W., Xiong, J., Shu, X., Zhang, Y., Chen, Z., & Liu, Y. (2022) · Energy · doi:10.1016/j.energy.2022.123236 · Cited by 51

Battery health evaluation using a short random segment of constant current charging

Deng, Z., Hu, X.-S., Xie, Y., Xu, L., Li, P.-H., Lin, X.-K., & Bian, X. (2022) · iScience · doi:10.1016/j.isci.2022.104260 · Cited by 49

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

Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

Yu, T., Wang, C., Yang, H., & Li, F. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.10.049 · Cited by 51

Degradation Pattern Recognition and Features Extrapolation for Battery Capacity Trajectory Prediction

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