State-of-Charge Estimation for Lithium-Ion Battery Base on Adaptive Extended Sliding Innovation Filter

Abstract

Accurate State of Charge (SoC) estimation is pivotal in advancing battery technology. In order to enhance the precision of SoC estimation, this study introduces the 2RC equivalent circuit model for lithium batteries. The Adaptive Extended Sliding Innovation Filter (AESIF) algorithm merges the model’s predictive outcomes with observation results. However, further improvements are required for this algorithm to perform optimally in strong noise environments. By adapting to observation noise and utilizing PID control to adjust the sliding boundary layer, the algorithm can accommodate varying noise levels and control interference fluctuations within specific limits. This study enhances the AESIF algorithm in these areas, proposing an improved version (IAESIF) to elevate performance in strong noise environments and improve overall estimation accuracy. Comprehensive tests were conducted under diverse operational conditions and temperatures, with results indicating that, compared to the EKF and the AESIF algorithm in strong noise environments, the IAESIF algorithm demonstrates improved noise adaptation and overall estimation accuracy.