Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Peipei Xu,Fengchun Sun,Junqiu Li,Guodong Yang,Chao Sun

doi:10.3390/electronics10020122

Peipei Xu, Fengchun Sun + Show 3 more

Open Access

https://doi.org/10.3390/electronics10020122

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Journal: Electronics	Publication Date: Jan 8, 2021
Citations: 25	License type: CC BY 4.0

Affiliation: Beijing Institute of Technology

Abstract

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Highlights

Lithium-ion batteries (LIBs), with their high energy density, low pollution and low selfdischarge rate, have become one of the main energy sources of electric vehicles (EVs) [1,2]
The ampere–hour integration method and the open circuit voltage (OCV) method are widely used in state of charge (SOC) estimation
The ampere–hour integral method is applicable to online SOC estimation; there are some errors in the current value due to the measurement errors during battery charging and discharging

Summary

Introduction

Lithium-ion batteries (LIBs), with their high energy density, low pollution and low selfdischarge rate, have become one of the main energy sources of electric vehicles (EVs) [1,2]. A variety of SOC estimation methods have been applied, including the ampere–time integral method [5], open-circuit voltage method [6], data-driven methods [7] and modelbased methods [8] These algorithms have greatly improved the estimation of SOC. The ECM has been extensively used for the BMS of EVs. Ye et al [13] proposed particle swarm optimization to optimize the Extended Kalman filter (EKF) to estimate SOC; Xiong et al [14] used the multi-scale EKF to realize the joint estimation of the parameters and states of LIBs. EKF can effectively obtain good estimation results, it ignores the higher-order term of the Taylor series expansion of the nonlinear function, which significantly reduces the estimation accuracy of SOC.

Battery Model and Parameter Identification

AEKF-Based SOC Estimation

Lithium-Ion Battery Degradation Model under Static Conditions

SOC Estimation Results

Lithium-Ion Battery Degradation Model under Dynamic Conditions

Hardware-in-the-Loop Validation