On-line Capacity Estimation of Li-ion battery Using Semi-parametric Transfer Learning

Abstract

Capacity estimation of lithium-ion (Li-ion) rechargeable batteries with adequate accuracy based on a small amount of charge-discharge cycling data are challenging. This is because the cycle data may not account for the considerable cell-to-cell variability that occurs during the aging process. Collecting long-term cycle data from numerous cells, on the other hand, is a costly and time-consuming operation in real-world applications. This article presents a semi-parametric Adaptive transfer learning method based on Gaussian process regression (AT-GPR) for assessing cell-level capacity despite only having access to a small dataset. It could be used to adapt transfer learning by automatically evaluating the similarity between the source and target tasks. Experimental results indicate that the proposed AT-GPR capacity estimation model may produce reliable prediction results, although the training data only accounts for 20% of the total dataset.