An Adaptive Extended Kalman Filter for Model-Based Fault Diagnosis and State of Charge Estimation of Lithium-Ion Batteries

Abstract

Precisely gauging the state of charge (SOC) of a lithium-ion battery poses a considerable obstacle due to the battery's intricate electrochemical properties, which are highly dynamic and nonlinear. The effectiveness of the control strategy employed in electric vehicles heavily hinges on the precise estimation of the battery's SOC. Among the crucial elements, the accurate assessment of SOC for power lithium-ion batteries stands out. Ensuring the durability and reliability of battery management systems in electric vehicles to predict SOC is a multifaceted endeavor. Given the inherently nonlinear degradation pattern of batteries, achieving precise forecasting of SOC while minimizing degradation proves to be a demanding task. The accurate determination of battery SOC holds significant significance for both battery electric vehicles and hybrid electric vehicles. Lithium-ion batteries have gained extensive usage in various facets of daily life due to their positive environmental and resource-related attributes. The precise determination of SOC is pivotal in ensuring proper battery operation. Nonetheless, the challenges of SOC estimation under low temperatures have received limited attention. To tackle this issue, this study introduces an adaptive extended Kalman Filter for model-based fault diagnosis and SOC estimation of lithium-ion batteries.