Data-driven estimation of battery state-of-health with formation features

Abstract

Accurately estimating the state-of-health (SOH) of a battery is crucial for ensuring battery safe and efficient operation. The lifetime of lithium-ion batteries (LIBs) starts from their manufacture, and the performance of LIBs in the service period is highly related to the formation conditions in the factory. Here, we develop a deep transfer ensemble learning framework with two constructive layers to estimate battery SOH. The primary approach involves a combination of base models, a convolutional neural network to combine electrical features with spatial relationships of thermal and mechanical features from formation to subsequent cycles, and long short-term memory to extract temporal dependencies during cycling. Gaussian process regression (GPR) then handles SOH prediction based on this integrated model. The validation results demonstrate highly accurate capacity estimation, with a lowest root-mean-square error (RMSE) of 1.662% and a mean RMSE of 2.512%. Characterization on retired cells reveals the correlation between embedded formation features and their impact on the structural, morphological, and valence states evolution of electrode material, enabling reliable prediction with the corresponding interplay mechanism. Our work highlights the value of deep learning with comprehensive analysis through the relevant features, and provides guidance for optimizing battery management.