Abstract: AI-based predictive battery health monitoring system to address challenges associated with lithium-ion battery failures and degradation in electric vehicles and renewable energy systems. By employing machine learning and deep learning algorithms, including CNNs, LSTMs, Logistic Regression, KNN, and SVM, the system accurately predicts key parameters such as State of Health, State of Charge, and Remaining Useful Life. Comparative analysis using datasets like NASA’s highlights the superior performance of CNN and LSTM models over traditional rule-based methods. MATLAB Simulink simulations enhance data quality for training and testing, while novel feature extraction techniques ensure robust model performance across diverse conditions. The system achieves a high accuracy of 0.986 in predicting battery metrics, demonstrating strong noise resilience and dynamic adaptability. These results emphasize the potential of AI-driven battery management systems to improve maintenance strategies, reduce operational costs, and promote the sustainable use of lithium-ion batteries.
Keywords: State of Health, State of Charge, Remaining Useful Life, CNN, LSTM, MATLAB, Logistic Regression, KNN, SVM.
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DOI:
10.17148/IJARCCE.2025.14503
