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Advanced Techniques for Estimating State of Charge (SoC) in Lithium-Ion Batteries

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Machine learning methods improve state of charge estimation in lithium-ion batteries, suggesting enhanced battery management systems.

Neural networks significantly improved state of charge estimation accuracy, outperforming traditional models.
The best-performing neural network achieved an RMSE of 0.0211 and an R² of 0.9959 in evaluation metrics.
Machine learning regression models including support vector machines and regression trees were analyzed for performance.
The publicly available LG 18650HG2 dataset provided over 670,000 data points for training and testing the models.

Nossier et al. (2025) studied this question.

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