A Data-Driven-Aided Thermoelectric Equivalent Circuit Model for Accurate Temperature Prediction of Lithium-Ion Batteries

Abstract

Accurate temperature estimation models for lithium (Li)-ion batteries are critical for timely identification of and response to thermal runaway effects to ensure battery safety. In this paper, a hybrid data-driven approach incorporating thermoelectric equivalent model (TEM) is proposed to predict the temperature of Li-ion batteries under different state of health (SOH) based on measured data. The proposed TEM model consists of an electrical equivalent circuit model (EECM) and a thermal equivalent circuit modeling (TECM). The electrical model is a second-order RC equivalent circuit model, and the thermal model is a first-order thermal model, which interacts with parameters such as state of charge (SOC) and internal resistance to improve the accuracy of the model. In order to solve the problem that the model part is susceptible to measurement errors, a data-driven model using Kalman filter (KF) combined bidirectional gated recursive unit (BiGRU) and Transformer is proposed to ensure high accuracy in predicting the temperature. The output of the TEM is used as the input to the data-driven part to obtain the implied relationship between the temperature and parameters. The experimental results confirm the high accuracy of the hybrid model in estimating the battery temperature. The maximum temperature prediction error of the Li-ion battery was 0.3423°C with a predicted root mean square error (RMSE) of 0.1266 under different SOH conditions.