A Battery SOC Estimation Method Based on AFFRLS-EKF.

Area of Science:

• * Electrical Engineering
• * Automotive Engineering
• * Battery Management Systems

Background:

• * Lithium-ion batteries are essential power sources for hybrid vehicles, necessitating precise State of Charge (SOC) monitoring for safe operation.
• * Accurate modeling of complex lithium-ion battery dynamics is challenging, leading to deviations in SOC estimation.
• * Conventional Recursive Least Squares (RLS) algorithms with fixed forgetting factors struggle with accuracy and robustness during sudden changes in battery charge/discharge conditions.

Purpose of the Study:

• * To develop an improved SOC estimation strategy for lithium-ion batteries in hybrid vehicles.
• * To enhance the accuracy and robustness of SOC estimation, particularly under dynamic operating conditions.
• * To address the limitations of traditional RLS algorithms in parameter identification for complex battery systems.

Main Methods:

• * Proposed an Adaptive Forgetting Factor Regression Least-Squares-Extended Kalman Filter (AFFRLS-EKF) strategy.
• * Designed an adaptive forgetting factor for the least squares algorithm within the EKF framework.
• * Implemented and simulated the AFFRLS-EKF strategy for SOC estimation.

Main Results:

• * The AFFRLS-EKF strategy demonstrated improved SOC estimation accuracy compared to conventional methods.
• * The proposed method effectively handles changes in battery charge and discharge conditions.
• * Accurate modeling using AFFRLS-EKF leads to significant improvements in SOC estimation.

Conclusions:

• * The AFFRLS-EKF strategy provides a robust and accurate method for real-time SOC estimation in hybrid vehicle lithium-ion batteries.
• * Adapting the forgetting factor enhances the algorithm's performance under varying operational demands.
• * This approach contributes to safer and more efficient operation of hybrid vehicles through reliable battery state monitoring.