State of health estimation for lithium battery based on multi-source transfer learning

Abstract

Aiming at the problem that the state of health estimation accuracy decreases when the lithium battery in training and testing sets has different charging conditions, we proposed a solution based on the transfer learning with the multi-source method. This scheme uses the complete aging test data of 16 batteries under different charging conditions as the source domain and extracts aging characteristics respectively to train the basic model. The similarity coefficient was obtained by the similarity evaluation of the first 200 cycles of each source and target battery, and the learning weight of each source battery model was weighted by the similarity coefficient. The experimental results show that the MAE and RMSE of the transfer learning with a multi-source model decreased by 53.63% and 54.97% compared with the single-source estimation model under new charging conditions.