NARX Transformer: A Dynamic Model for Leveraging Multicycle Data in Long-Term Battery State of Health Estimation

Battery state of health (SoH) is a vital indicator of its performance and longevity, making accurate SoH predictions crucial for effectively managing and maintaining battery-powered systems. Deep learning (DL) frameworks enhance SoH prediction accuracy by learning complex patterns from large datasets. Transformer architecture is particularly beneficial for processing sequential data, capturing temporal dependencies within battery data, and improving SoH estimation reliability. In this research, we introduce a novel dynamic model that integrates nonlinear auto-regressive with exogenous (NARXs) inputs into a transformer encoder-decoder architecture. This model aims to predict long-term battery SoH and end-of-life (EoL), focusing on multiple cycles and the dynamic behavior of SoH changes. We evaluated our model using a unique half-half experiment, showing that it reduces the mean absolute percentage error in long-term predictions to 1.4%. This method enables accurate battery health and EoL predictions in practical applications, showing robustness against measurement noise.