An adaptive deep neural network with transfer learning for state-of-charge estimations of battery cells

Abstract

This paper proposes a new adaptive learning model for capacity estimation of lithium-ion battery cells. The proposed deep neural network transfers knowledge from other cells and adapts its behavior by exponentially weighting the data from the historical cells using a custom weighting function. The proposed model is shown to achieve state-of-art with an MAE of 0.56% when compared with three other traditional transfer learning and adaptive learning models for Li-ion battery cells. Details of the model followed by derivations and experimental results are provided.