Optimal Estimation of the State-of-Energy and Surface Temperature of Li-ion Batteries using an Extended Kalman Filter with Cramer-Rao Lower Bound

Abstract

Lithium-ion batteries (LIBs) are essential and have a large variety of applications, yet their safety and performance critically depend on temperature regulations. This study presents a compact electro-thermal model that integrates an electrical equivalent circuit representation with a thermal network description of the battery. Within this framework, a joint estimation of the battery’s state-of-energy (SoE) and surface temperature is achieved using an extended Kalman filter (EKF).To rigorously assess the estimator’s optimal performance, the Cramer-Rao Lower Bound (CRLB) has been utilised. The CRLB provides the theoretical lower limit on the variance of unbiased estimators. A comparative analysis of the estimated SoE with the generalized SoE calculated from the Watt-hour (Wh) method has been carried out. Simulation and experimental results prove that the EKF improves the estimation accuracy and also follows the CRLB criteria.