Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning

Abstract

Accurate estimation of the state-of-charge (SOC) of lithium-ion batteries is a key technique for automotive battery management systems to overcome the non-linearity and complications of practical applications. The data-driven approach for estimating SOC requires a large number of training samples and costly input. To this end, an improved gated recurrent unit (GRU)-based transfer learning SOC estimation is proposed for small target sample sets. To ensure the completeness and consistency of data features, Lagrangian interpolations and standard normalization are used for analyzing the open-source battery datasets. The source domain GRU model is pre-trained to obtain rich battery characteristics with the preprocessed datasets; the GRU hidden unit structure can be enhanced, and it is advantageously used in conjunction with transfer learning. Moreover, weight parameters of the source domain are transferred to the GRU model of target batteries. The experimental results show that the proposed improved GRU-based transfer learning can use small target samples to achieve fast and accurate SOC estimations by ordinary computing hardware. In particular, the RMSEs are 1.115%, 1.867%, and 1.141% under dynamic conditions, 32 °C-FUDS, 36 °C-US06, and 50 °C-UDDS, respectively. The proposed method demonstrates the potential of SOC estimation using small target samples-based big data techniques in practice.