Quadrature Kalman filter–based state of charge estimation for lithium-ion battery

Jingting Wang,Bao Liu,Mengtao Huang,Chao Wang,Fan Wang

doi:10.1177/1687814020942696

Abstract

This article presents an approach to lithium-ion battery state of charge estimation based on the quadrature Kalman filter. Among the existing state of charge estimation approaches, the extended Kalman filter–based state of charge and unscented filter–based state of charge algorithms are influenced by the linearization or the solution of sigma points. The proposed quadrature Kalman filter–based state of charge algorithm avoids these problems. Specifically, the battery system equations are built based on the second-order resistance–capacitance equivalent circuit model, and the parameters are identified according to the hybrid pulse power characterization discharging test. Then, the quadrature points and corresponding weights are defined by the Gauss–Hermite quadrature rule, and the Kronecker tensor product is adopted to solve the points of multivariate. In addition, the stability of quadrature Kalman filter–based state of charge is verified. Finally, the simulation is carried out under the discharging and urban dynamometer driving schedule condition, which demonstrates that the quadrature Kalman filter–based state of charge algorithm has a better performance compared with extended Kalman filter–based state of charge and unscented filter–based state of charge.

Highlights

Lithium-ion battery has been widely used in our daily life, such as electric vehicle and power storage system
The lithium-ion battery has higher energy density, low self-discharging rate, and long cycle life compared with other batteries such as lead–acid battery, nickel– chromium battery, and nickel–metal hybrid battery.[1]
We prove the stability of quadrature Kalman filter–based state of charge (QSOC) algorithm according to the error convergence

Summary

Introduction

Lithium-ion battery has been widely used in our daily life, such as electric vehicle and power storage system. The unscented filter (UF)[8,9] algorithm is applied in the battery SOC estimation. The quadrature Kalman filter–based state of charge (QSOC) algorithm is proposed to handle the battery SOC estimation problem. This approach adopts a series of quadrature points and weights to estimate the mean and covariance. R i1⁄41 where ji and vi represent the quadrature points and weights, respectively They can be obtained by rootfinding methods. Before the SOC estimation, the quadrature points and weights should be determined. Combining with the SOC estimation problem, the quadrature points and weights of single variable cannot satisfy the process of the evaluating quadrature points. L 1⁄4 1; 2; . . . ; mn where jl is the quadrature point vector. mn is the quantity of Gauss–Hermite quadrature point vectors

Estimated prediction error covariance

Estimate the corresponding error covariance

Conclusion