State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification

Juqiang Feng,Xing Zhang,Kaifeng Huang,Long Wu,Jun Lu,R Weerasinghe,J Wu,C.-H Weng

doi:10.1051/e3sconf/202019402023

Juqiang Feng, Xing Zhang + Show 6 more

Open Access

https://doi.org/10.1051/e3sconf/202019402023

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Abstract

Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy and feasibility of the method are verified by Federal Urban Driving Schedule (FUDS), the online identification and SOC estimation are carried out. The experimental results show that the SOC estimation of online parameter identification is more accurate, the system stability is faster and the error is smaller.

Highlights

Lithium-ion batteries are widely used in many applications, such as power grids, new energy vehicles, energy storage systems and various electronic devices, and play a vital role [1,2]
The value of forgetting factor was set as 0.95, the gain matrix and covariance matrix were calculated, and the parameters were identified and updated online, so as to realize state of charge (SOC) estimation and terminal voltage value prediction. 2.3 Validation of the model In order to verify the validity of the online identification algorithm in this paper, Federal Urban Driving Schedule (FUDS) operating condition experiment is designed
It can be seen that the online identification method proposed in this paper can converge rapidly and effectively, and approach the theoretical value steadily all the time

Summary

Introduction

Lithium-ion batteries are widely used in many applications, such as power grids, new energy vehicles, energy storage systems and various electronic devices, and play a vital role [1,2]. The usage of lithium-ion cells has fuelled growth in many applications because of high energy density, high power ratio, long cycling life (more than 500 times) and low self-discharge (less than 5% per month) [3]. The identification method of battery model parameters directly affects the identification accuracy and reliability, affecting the estimation accuracy of SOC. Recursive least squares (RLS) method is a common identification algorithm based on online estimation to achieve high accuracy. For online identification of lithium-ion, data updating will lead to problems such as data saturation, and it is difficult to track the parameters of the time-varying system. Based on the mixed simplified electrochemical model, the model parameter variables were identified online by forgetting factor recursive least squares (FFRLS) method. The accuracy and feasibility of the method are verified by Federal Urban Driving Schedule (FUDS) experiment

Model establishment and parameter identification

Model parameter identification

Estimation of SOC

Conclusion