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

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Optimizing electric bike battery management: Machine learning predictions of LiFePO4 temperature under varied conditions

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Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

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The future of battery data and the state of health of lithium-ion batteries in automotive applications

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Battery lifetime prediction using surface temperature features from early cycle data

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Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

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Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches

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Predicting the Cycle Life of Lithium-Ion Batteries Using Data-Driven Machine Learning Based on Discharge Voltage Curves

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An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data‐Dependent Models

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Physics-informed machine learning for high-speed on/off valve performance degradation prediction in water hydraulic manipulator

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Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

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Battery state estimation for electric vehicles: Translating AI innovations into real-world solutions

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Identification of maize seed vigor under different accelerated aging times using hyperspectral imaging and spectral deep features

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Economic Value Creation of Artificial Intelligence in Supporting Variable Renewable Energy Resource Integration to Power Systems: A Systematic Review

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Data-Driven Analytics for Personalized Medical Decision Making

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A Review of Machine Learning Applications for Li-Ion Battery State Estimation in Electric Vehicles

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

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Modeling and Mitigating the Cycle Aging Cost of Vehicle Batteries in Energy Transportation Nexus

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

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Temperature prediction of lithium-ion battery based on adaptive GRU transfer learning framework considering thermal effects decomposition characteristics

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Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

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Battery intelligent temperature warning model with physically-informed attention residual networks

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Mechanics-based design of lithium-ion batteries: a perspective

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Creating a Robust SoC Estimation Algorithm Based on LSTM Units and Trained with Synthetic Data

Azkue, M., Miguel, E., Martinez-Laserna, E., Oca, L., & Iraola, U. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14070197 · Cited by 21

Multi-model deep learning-based state of charge estimation for shipboard lithium batteries with feature extraction and Spatio-temporal dependency

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Identifiability study of lithium-ion battery capacity fade using degradation mode sensitivity for a minimally and intuitively parametrized electrode-specific cell open-circuit voltage model

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Grid Peak Shaving and Energy Efficiency Improvement: Advances in Gravity Energy Storage Technology and Research on Its Efficient Application

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Estimating the health status of lithium-ion batteries using deep learning method based on informer model

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Experimental study of maghemite nanomaterials towards sustainable energy storage device application

Priyadarshi, H., Singh, K., & Shrivastava, A. (2022) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2022.106698 · Cited by 18

State-of-Charge Estimation for Remaining Flying Time Prediction of Small UAV Using Adaptive Robust Extended Kalman Filter

Uhm, T., & Kim, S. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3449273 · Cited by 18

Perturbation-Based Battery Impedance Characterization Methods: From the Laboratory to Practical Implementation

Fan, C., Tian, X., & Gu, C. (2024) · Batteries · doi:10.3390/batteries10120414 · Cited by 19

State-of-Charge Estimation for Lead-Acid Battery Using Isolation Forest Algorithm and Long Short Term Memory Network With Attention Mechanism

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An IoT-based predictive model for improved battery management system using advanced LSTM model

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Modeling of discharge voltage for lithium-ion batteries through orthogonal experiments at subzero environment

Meng, H., Li, Y., & Zhang, C. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105058 · Cited by 17

Machine learning for a sustainable energy future

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Predicting lithium-ion battery health using attention mechanism with Kolmogorov-Arnold and physics-informed neural networks

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Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

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State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM

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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries

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Unlocking Interpretable Prediction of Battery Random Discharge Capacity With Domain Adaptative Physics Constraint

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TELL-Me: T time-series-decomposition-based ensembled lightweight learning model for diverse battery prognosis and diagnosis

Liu, K.-Y., Wang, T., Zou, B., Peng, H., & Liu, X. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.01.063 · Cited by 16

High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective

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A hybrid probabilistic battery health management approach for robust inspection drone operations

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Next-generation battery energy management systems in electric vehicles: An overview of artificial intelligence

Kumar, N., & Prabhansu (2025) · Future Batteries · doi:10.1016/j.fub.2025.100087 · Cited by 16

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

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Prediction of vanadium redox flow battery storage system power loss under different operating conditions: Machine learning based approach

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Fast and reliable calibration of thermal-physical model of lithium-ion battery: a sensitivity-based method

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State of Health Estimation and Remaining Useful Life Prediction for a Lithium-Ion Battery with a Two-Layer Stacking Regressor

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Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

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A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

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Cloud-Battery management system based health-aware battery fast charging architecture using error-correction strategy for electric vehicles

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Degradation Modeling Considering the Dependency of Rate and Volatility for Real-Time Prognostics With Error Correction

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Battery State of Health Estimation from Discharge Voltage Segments Using an Artificial Neural Network

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A modeling and state of charge estimation approach to lithium-ion batteries based on the state-dependent autoregressive model with exogenous inputs

Chen, L., Zeng, X., Xia, X., Sun, Y., & Yue, J. (2024) · Energy · doi:10.1016/j.energy.2024.131626 · Cited by 14

Research on estimating the state of heath of power batteries based on the distribution of relaxation times method

Zhang, L., Hong, X., Xu, W., & Ruan, D. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112370 · Cited by 15

A Way Towards Energy Autonomous Wireless Sensing for EV Battery Management System

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Prediction of state-of-health and remaining useful life for lithium-ion batteries using short-term relaxation voltage

Wan, F., Lin, Y., Yang, D., Li, S., Liu, R., Zhu, L., Yin, W., & Chen, W. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117397 · Cited by 15

Battery state of health estimation using deep transfer learning on short-term charging data

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Revolutionizing batteries based on digital twin through AI-simulation synergy for design, manufacturing, operation, and recycling

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Molecular Design of Ether-Based Electrolyte Solvents for High-Performance Lithium Batteries

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Visualization-based prediction of dendritic copper growth in electrochemical cells using convolutional long short-term memory

Kumar, R., Dhara, T., Hu, H., & Chakraborty, M. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100203 · Cited by 13

Artificial intelligence and machine learning in energy storage and conversion

Seh, Z. W., Jiao, K., & Castelli, I. E. (2023) · Energy Advances · doi:10.1039/d3ya90022c · Cited by 14

Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process

Cui, X., Lu, J., & Han, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3374776 · Cited by 14

Data-driven insights into the reaction mechanism of Li-rich cathodes

Kim, J., Choi, I., Kim, J. S., Hwang, H., Yu, B., Nam, S. C., & Park, I. (2025) · Energy & Environmental Science · doi:10.1039/d4ee05222f · Cited by 14

Recollection center location for end-of-life electric vehicle batteries using fleet size forecast: Scenario analysis for Germany

Yükseltürk, A., Wewer, A., Bilge, P., & Dietrich, F. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.01.084 · Cited by 14

The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World Data

Akil, M., Dokur, E., & Bayındır, R. (2021) · Energies · doi:10.3390/en14248508 · Cited by 14

Battery Degradation Modelling and Prediction with Combination of Machine Learning and Semi-empirical Methods

Xue, Y., Zhou, H., Luo, Y., & Lam, J. (2022) · doi:10.1109/cpeee54404.2022.9738723 · Cited by 13

Efficient estimating and clustering lithium-ion batteries with a deep-learning approach

Wu, J., Sun, Z., Li, D., He, W., Yang, D., Wu, Z., Geng, X., Yang, H. M., Wang, H., Hu, L., et al. (2025) · Communications Engineering · doi:10.1038/s44172-025-00488-1 · Cited by 13

Recent advancements in Artificial Neural Network-based temperature prediction and management of lithium-ion batteries: A comprehensive review

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Advancements in production planning and scheduling within steel manufacturing: A review and its intelligent development

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Towards artificial intelligence-enabled autonomous battery prognostics and management

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Mechanistically guided residual learning for battery state monitoring throughout life

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Remaining Useful Life Prediction of Lithium-Ion Battery Based on LSTM and GRU

Yuliani, A. R., Ramdan, A., Zilvan, V., Supianto, A. A., Krisnandi, D., Yuwana, R. S., Prajitno, D. R., & Pardede, H. F. (2021) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1145/3489088.3489092 · Cited by 13

Toward Automated Computational Discovery of Battery Materials

Feng, X., Zhang, Q., & Seh, Z. W. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200616 · Cited by 13

Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost

Lee, J.-H., & Lee, I.-S. (2023) · Batteries · doi:10.3390/batteries9110544 · Cited by 14

Machine-Learning-Based Smart Energy Management Systems: A Review

Husseini, F. E., Noura, H., & Vernier, F. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592420 · Cited by 13

Integrating machine learning for health prediction and control in over-discharged Li-NMC battery systems

Naresh, G., & Thangavelu, P. (2024) · Ionics · doi:10.1007/s11581-024-05834-5 · Cited by 13

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

Vakhrusheva, D. M., & Xu, J. (2024) · Batteries & Supercaps · doi:10.1002/batt.202400539 · Cited by 14

Navigating the intricacies: A critical review of numerical modeling in battery research and design

Wen, J. G., Wang, L., & He, X. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235902 · Cited by 13

Leveraging Neural Networks and Genetic Algorithms to Refine Electrode Properties in Redox Flow Batteries

Tenny, K. M., Braatz, R. D., Chiang, Y., & Brushett, F. R. (2021) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/abf77c · Cited by 12

Deep transfer learning-enabled battery health prognosis using impedance spectrum data

Hua, N., Chen, H., & Dong, G. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117855 · Cited by 12

Deep imputation on large‐scale drug discovery data

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A Novel Method for State of Health Estimation of Lithium-Ion Batteries Based on Deep Learning Neural Network and Transfer Learning

Ren, Z. G., Du, C., & Zhao, Y. (2023) · Batteries · doi:10.3390/batteries9120585 · Cited by 15

Unravelling degradation mechanisms and overpotential sources in aged and non-aged batteries: A non-invasive diagnosis

Appiah, W. A., Rieger, L., Flores, E., Vegge, T., & Bhowmik, A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111000 · Cited by 12

A Data-Driven-Based SOC Estimation Method Using Newly Constructed Features for Different Application Environments

Qin, P., & Zhao, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3386819 · Cited by 12

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

Li, Z., Zhou, Y., Guo, C., Dang, Y., Ji, X., & He, G. (2024) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ad3eb9 · Cited by 12

Enhancing EV lithium-ion battery management: automated machine learning for early remaining useful life prediction with innovative multi-health indicators

Mishra, S., Choubey, A., Reddy, B. A., & Misra, R. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06264-w · Cited by 13

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals

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Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration

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