Data‐driven battery capacity estimation using support vector regression and model bagging under fast‐charging conditions

Abstract

Lithium‐ion batteries offer significant advantages in terms of their high energy and power density and efficiency, but capacity degradation remains a major issue during their usage. Accurately estimating the remaining capacity is crucial for ensuring safe operations, leading to the development of precise capacity estimation models. Data‐driven models have emerged as a promising approach for capacity estimation. However, existing models predominantly focus on constant current charging conditions, limiting their applicability in real‐world scenarios where fast‐charging conditions are commonly employed. The primary objective of this work is to develop a more versatile machine learning model (i.e., support vector regression [SVR]) capable of estimating battery capacity under fast‐charging conditions, with broader applicability across various work conditions. Genetic algorithm and cross‐validation techniques are employed to simultaneously optimize feature extraction hyperparameters and SVR hyperparameters. A model bagging method is further implemented to address prediction challenges under unknown fast‐charging conditions. The effectiveness of the developed model is validated on a cycling dataset of lithium‐ion batteries under different two‐stage fast‐charging conditions.