Parameter Estimation of Lithium Battery Thermal Model Based on Two-Stage Forgetting Factor Least Square Method

With the increasing popularity of lithium-ion batteries, managing the dynamic thermal behavior of lithium-ion batteries has become a profound yet challenging topic. To date, various battery thermal models have been proposed. Among these, the equivalent circuit model and the two-node thermal model are widely used because of their reduced computational complexity and high accuracy. The performance of the model, however, depends on an accurate estimation of model parameters, which are time-dependent and may vary with other factors such as temperature, state of charge, and aging of the battery. In this paper, a two-stage forgetting factor recursive least square method (TSFFRES) is proposed to address efficiently the challenge of estimating variable parameters. The proposed method is compared with canonical approaches and features better performance in the sense of computational time and accuracy.