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

Zhuo Wang (), Jinrong Shen and Yang Xu
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Zhuo Wang: College of Information Science and Enginnering, Hohai University, Changzhou 213200, China
Jinrong Shen: College of Information Science and Enginnering, Hohai University, Changzhou 213200, China
Yang Xu: College of Information Science and Enginnering, Hohai University, Changzhou 213200, China

Energies, 2024, vol. 17, issue 14, 1-13

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.

Keywords: state-of-charge; sliding innovation filter; lithium-ion battery; extended Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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