<ul><li>Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for timely maintenance and operational efficiency in electric applications.</li><li>This paper introduces a RUL prediction approach that utilizes recent charge-discharge cycle data to estimate the remaining usable cycles.</li><li>The approach involves a signal processing pipeline and a deep learning prediction model, incorporating capacity feature calculation, denoising, and feature enhancement techniques.</li><li>The prediction model utilizes a hybrid deep learning architecture involving 1D CNN, A-LSTM, and ODE-LSTM modules to capture local signal characteristics, long-range temporal dependencies, and continuous-time dynamics of battery degradation, demonstrating superior performance in RUL prediction applications.</li></ul>

End-to-End Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries

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