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

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Electrochemical Impedance Spectroscopy Based State-of-Health Estimation for Lithium-Ion Battery Considering Temperature and State-of-Charge Effect

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Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime

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A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

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Battery Safety: Machine Learning-Based Prognostics

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A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

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Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

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Recent advances in artificial intelligence boosting materials design for electrochemical energy storage

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A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries

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Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

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Machine Learning Screening of Metal-Ion Battery Electrode Materials

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Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

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Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

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An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

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Artificial intelligence-driven real-world battery diagnostics

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