A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

Zizhou Lao,Yongzhi Lai,Wei Sun,Mingwang Wang,Wei Wang,Bizhong Xia

doi:10.3390/en11061358

Abstract

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

Highlights

Compared with other batteries, the performance of lithium-ion batteries is better in terms of power capability, cycle life, thermal stability, and so forth [1]
This paper proposes an online parameter identification algorithm and applies it to state of charge (SOC) estimation
Measured points fitted open-circuit voltage–state of charge (OCV–SOC)

Summary

Introduction

The performance of lithium-ion batteries is better in terms of power capability, cycle life, thermal stability, and so forth [1]. The lithium-ion battery industry has developed rapidly, and the batteries have a wide range of commercial applications, such as in electric vehicles, cell phones, laptop aviation products, and grid energy storage. The battery management system (BMS) is one of the most important parts of an electric vehicle [2]. State of charge (SOC) represents the remaining charge of the battery and is an important assessment of the battery state. The estimation of the SOC is an important function of the BMS, but is a fundamental research topic in terms of BMSs. J.

Results

Discussion

Conclusion