A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

Abstract

State of Charge (SOC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of lithium-ion batteries. SOC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and distinct nonlinear behavior. This paper compares two of the basic methods and algorithms for SOC estimation of lithium-ion batteries (LIBs) focusing on the description of the two techniques in a test experiment and the elaboration of their differences for use in battery management systems (BMS) applications. A Reduced-order unscented Kalman filter method is used for estimation and tracking to realizer real-time high-precision estimation of lithium-ion battery state of charge. Experimental tests are carried out with a lithium-ion battery cell for model and state estimation validations. Many researchers have proposed different methods of estimating SOC that raised the challenge of establishing a relationship between the accuracy and robustness of the method. The experimental results of the OCV-SOC estimation method, Hybrid Pulse Power Characterization (HPPC) test, and Beijing Bus Dynamic Stress Test (BBDST) working condition method are analyzed. The error of SOC estimation based on the established Thevenin RC modeling using the Reduced-order-unscented Kalman filter is less than 0.3%. The result from the use of the reduced-order unscented Kalman filtering algorithm proves that it has high accuracy in the state of charge estimation of the lithium-ion batteries.

Keywords: lithium-ion battery, open-circuit voltage, state of charge estimation, battery management system, HPPC test, reduced-order unscented Kalman filter

DOI: 10.7176/JETP/11-6-05

Publication date: December  30^th 2021