Abstract

An AI-based methodology was developed for estimating the state-of-health (SOH) of lithium-ion batteries based on secondary operational data and benchmarked with ANN, SVM, RF, and BiLSTM models. The proposed framework was evaluated by using tolerance-based accuracy, Bland–Altman agreement analysis, residual autocorrelation diagnostics, and Cartesian Taylor diagram comparison. The BiLSTM model was the best among the tested models for SOH prediction, with the least prediction error, best agreement with the reference SOH values, and near-white-noise residual behavior. The framework was further extended to Remaining Useful Life (RUL) prediction, where the BiLSTM model showed the most consistent overall performance. We also propose a residual-based anomaly detection as a potential extension of the battery monitoring framework. However, a quantitative evaluation of anomaly detection is out of scope in this study due to the lack of labeled anomaly data in the CALCE dataset. The proposed framework is validated by complementary statistical diagnostics, providing a robust and practical framework for non-intrusive battery health monitoring.

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