A Novel Redacted Extended Kalman Filter and Fuzzy Logic-Based Technique for Measurement of State-of-Charge of Lithium-Ion Battery

Abstract

Abstract This paper presents a novel technique based on an adaptive approach of redacted extended Kalman filter (REKF) assimilating fuzzy logic features for measuring the state-of-charge (SoC) of lithium-ion batteries. Accurately determining SoC is crucial for maximizing battery capacity and performance. However, existing extended Kalman filtering algorithms suffer from issues such as inadequate noise resistance and noise sensitivity, as well as difficulties in selecting the forgetting factor. The aforementioned REKF technique addresses these challenges adequately for accurate measurement of SoC. The proposed method involves establishing a Thevenin equivalent circuit model and using the recursive least squares with forgetting factor (RLSFF) to identify model parameters. Furthermore, an evaluation factor is established, and to adaptively adjust the value of the forgetting factor, fuzzy control is utilized, which enhances the extended Kalman filtering algorithm with noise adaptive algorithm features to estimate the SoC accurately. This modified algorithm considers the identification results from the parameter estimation step and executes them circularly to achieve precise SoC estimation. Results demonstrate that the proposed method has excellent robustness and estimation accuracy compared to other filtering algorithms, even under variable working conditions, including a wide range of state-of-health (SOH) and temperature. The proposed method is expected to enhance the performance of battery management systems for various applications.