Robust State-of-Charge Estimation for Lithium-Ion Batteries Using an Adaptive Invariant Extended Kalman Filter With an Enhanced Self-Correcting Model

Abstract

State-of-Charge (SoC) estimation plays a vital role in the safe operation of lithium-ion batteries, which can be used in different applications such as Electric Vehicles (EVs). However, traditional SoC estimation methods, such as Coulomb counting and Extended Kalman Filter (EKF), are often prone to accumulating errors, parameter uncertainty, and measurement noise under dynamic operating conditions. Therefore, in this work, an Adaptive Invariant Extended Kalman Filter (Adaptive IEKF) together with an Enhanced Self-Correcting (ESC) battery model will be proposed to estimate SoC in real time. Parameters of the ESC model were found by means of OCV-SoC characterization, Static Capacity Test (SCT), and Hybrid Pulse Power Characterization (HPPC). Measurement noise covariance adaptation on the basis of the innovation approach was used in order to increase robustness to uncertainty and variations in operating conditions. The developed algorithm was tested with the help of MATLAB/Simulink in the case of SCT and HPPC operating profiles and implemented on an STM32F446RE microcontroller in order to verify its performance under real-time embedded conditions. The obtained experimental results confirm that the proposed technique keeps SoC estimation error under 2% under both dynamic and static operating conditions. During the HPPC, the Adaptive IEKF showed RMSE and MAE equal to 0.01487 and 0.0156, respectively, while for the SCT conditions, RMSE and MAE are 0.01112 and 0.01271, respectively.