Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Area of Science:

• * Electrical Engineering
• * Materials Science
• * Chemical Engineering

Background:

• * State of Charge (SOC) is critical for electric vehicle battery performance and safety.
• * Kalman Filter (KF) and Extended Kalman Filter (EKF) are common but can lack stability for SOC estimation.
• * Second-order resistor-capacitor (RC) equivalent circuit models are often used for SOC estimation.

Purpose of the Study:

• * To develop a more stable and accurate method for lithium-ion battery SOC estimation.
• * To improve upon existing SOC estimation techniques for electric vehicle power systems.
• * To combine a stable observer with the eXogenous Kalman filter (XKF) for enhanced SOC prediction.

Main Methods:

• * A two-stage approach combining a second-order RC equivalent circuit model with the eXogenous Kalman filter (XKF).
• * Stage 1: Initial SOC estimation using a stable observer without parameter uncertainty.
• * Stage 2: Refined SOC estimation by feeding initial results into the XKF for improved accuracy and stability.

Main Results:

• * The proposed two-stage XKF method demonstrated superior SOC estimation performance compared to the Extended Kalman Filter (EKF).
• * Experimental results confirmed the enhanced stability and accuracy of the developed SOC estimation technique.
• * The method effectively addresses limitations of traditional EKF approaches in SOC estimation.

Conclusions:

• * The two-stage XKF method offers a robust solution for stable and accurate lithium-ion battery SOC estimation.
• * This approach is highly applicable to the demanding power supply systems of electric vehicles.
• * Future applications may benefit from this advanced SOC estimation technique for improved battery management.