Physics-informed signal decomposition and data-driven RUL estimation for lithium-ion batteries

Abstract Accurate Remaining Useful Life (RUL) prediction is pivotal for the reliability of lithium-ion battery management systems but is frequently challenged by the non-linear coupling of irreversible degradation trends and transient capacity regeneration phenomena. To address this, this paper proposes a novel hybrid framework specifically designed to integrate physics-informed signal decomposition with data-driven deep learning. First, we introduce an adaptive decomposition scheme-Variational Mode Decomposition (VMD) optimized by an Improved Triangulation Topology Aggregation Optimizer (ITTAO). This physics-informed stage effectively decouples the raw capacity trajectory into two physically distinct components: a global degradation trend and local regeneration fluctuations. Subsequently, a dual-stream data-driven strategy is developed to characterize these components: a Statistical-Guided Spectral Attention LSTM (SGA-LSTM) is proposed to capture the longterm dependencies of the trend component, while a Temporal Convolutional Network with Ordered Neurons LSTM (TCN-ONLSTM) models the high-frequency dynamics of the regeneration component. By reconstructing the predictions from these tailored sub-models, the proposed method achieves a holistic assessment of battery health. Comprehensive validations on NASA and CALCE benchmark datasets demonstrate that the framework controls relative errors within 5%, exhibiting superior accuracy and robustness compared to state-of-the-art approaches.