Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Abstract

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.