Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Wei Li,Yaqian Tan,Maji Luo,Xiangyu Cui

doi:10.3390/wevj12030123

Abstract

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

Highlights

Faced with the global energy shortage and climate change crisis, the market position of electric vehicles (EVs) has become obvious [1,2], leading the trend of the automotive industry with the advantages of being pollution-free, low noise, and having high energy efficiency [3,4]
In order to evaluate the performance of the present method based on vector forgetting factor recursive least squares (VRLS) online parameters identification and Adaptive Cubature Kalman filter (ACKF) in State of charge (SOC) estimation, the UDDS and Dynamic Stress Test (DST) conditions are selected
By comparing the estimated terminal voltage of the Thevenin model and dual polarization (DP) model, we can conclude that on the one hand, the Thevenin model and DP model can be adopted to the dynamic and complex condition

Summary

Introduction

Faced with the global energy shortage and climate change crisis, the market position of electric vehicles (EVs) has become obvious [1,2], leading the trend of the automotive industry with the advantages of being pollution-free, low noise, and having high energy efficiency [3,4]. Among all commercially available lithium-ion batteries, the ternary lithiumion batteries are widely utilized because of their advantages of long service life, high energy density, superior performance at high and low temperatures, and environmental protection [5]. Under certain operating conditions, the difference among all individual cells may result in battery over-charge and over-discharge, even explosions [6]. The Battery Management System (BMS) plays a crucial role in assuring safety and monitoring the operating process [7,8]. State of charge (SOC), which indicates the remaining energy in the battery, is an important part of BMS [9]. The SOC of a battery cannot be obtained directly but is often estimated based on some measurable parameters such as the terminal voltage and operation current [10].

Methods

Results

Conclusion