An Enhanced Fusion Algorithm with Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

Abstract

An enhanced dual extended Kalman filter method is presented in this article for estimating and tracking the state-of-temperature of lithium-ion battery cells. A simple but effective dynamic and measurement empirical fit models are proposed and utilized to estimate the state-of-temperature concurrently with the state-of-charge. The proposed dual estimator improves the estimation accuracy of the temperature state by accounting for the variations in the state-of-charge. To test the performance of the proposed estimation method, two independent lithium-ion battery cell datasets were used to derive the empirical models and run the estimation algorithm. The obtained results show a promising performance of the estimation method in terms of the high estimation accuracy even in the case when the measurement contains high-magnitude noise or when the estimation algorithm is inaccurately initialized. The proposed models and the estimation algorithm are derived and experimentally tested in this article.