ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Abstract

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment.

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

“…124 However, the equivalent circuit model parameters of a power LIB differ under different usage conditions and environmental temperatures. 125 Therefore, the generalization ability of the model parameters obtained by offline parameter identification is poor and not universal. Usually, the offline parameter identification method needs to identify parameters under different usage conditions and temperatures, construct an interpolation table of the obtained model parameters, and finally obtain higher universality by looking up the table during use.…”

A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle

“…Battery types have also started to be produced in different types to meet the needs of these sectors. However, lithium ion batteries have a wider range of applications due to their advantages such as high energy density, low variation in operating voltage levels, long cycle life and light weight [9]. In recent years, lithium ion batteries have gained a significant place as an energy storage technology for electric vehicles, mobile electronic devices and grid stability [10].…”