Battery aging assessment: from critical insights to enhanced diagnosis

Abstract

Reliable battery health diagnosis and cycle life prediction remain a central challenge for energy storage systems. This work first provides a systematic analysis of key factors for battery health diagnosis, highlighting previously overlooked yet critical elements that affect health assessments. Building on these insights, a rate-adaptive transformation model converts high C-rate features into low C-rate equivalents, enabling rapid diagnostics of battery aging modes without time-consuming testing using a low C-rate. To address fitting inaccuracies caused by aging, blended materials, and kinetic effects, an interpretable residual learning model corrects voltage mismatches, which also enables low C-rate fitting by using high C-rate data. Leveraging mechanistic-informed features, early cycle life prediction achieves mean errors of less than 70 cycles using data from fewer than 30 equivalent full cycles across complex and unseen aging conditions. This interpretable and generalizable framework bridges electrochemical understanding with practical diagnosis and offers a fast and reliable path toward mechanism-informed battery prognostics.