History‐Agnostic Lithium‐Ion Batteries State of Health and Remaining Useful Life Estimation Using Fusion‐Based Bayesian Neural Network

ABSTRACT

This paper presents an innovative, history‐agnostic framework for lithium‐ion battery prognostics that addresses critical limitations of existing methods by removing the necessity for historical degradation data through the application of Bayesian neural networks (BNNs) and feature‐fusion techniques. A significant contribution is the introduction of four new health indicators (HIs) based on the voltage–time derivative (), which are fused with six other well‐established HIs derived from a single charge‐discharge cycle as inputs to the BNN. This comprehensive feature set facilitates two distinct BNNs to perform direct, non‐iterative predictions of the state of health (SoH) and remaining useful life (RUL), complete with principled uncertainty quantification. The effectiveness of the framework is validated through experiments conducted on the Oxford battery degradation dataset, employing a leave‐one‐out cross‐validation strategy. The results indicate exceptional performance, with the SoH model achieving values exceeding 0.998 and MAE below 0.002 across all test cells. The direct RUL prediction model also demonstrates high accuracy, with values consistently above 0.97 and a MAPE as low as 8.5%. By offering robust, uncertainty‐aware predictions without reliance on historical data, this approach presents a practical and scalable solution for real‐world battery management systems, particularly in applications involving second‐life batteries.