Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

Fan Zhang,Jianqiang Kang,Lele Yin

doi:10.3390/en14196284

Fan Zhang, Jianqiang Kang + Show 1 more

Open Access

https://doi.org/10.3390/en14196284

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Journal: Energies	Publication Date: Oct 2, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Wuhan University of Technology, InferVision (China)

Abstract

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

Highlights

Considering the current severe environmental challenges and gradual exhaustion of non-renewable fossil fuels, electric vehicles (EVs) have been recognized by the global automotive industry as a potential alternative to the widely used internal combustion engine vehicles [1]
Under the FUDS test, the result achieved by the SH algorithm still has local irregularities and deviates from the real
We proposed two improved algorithms, named ISH and IIAE, for adaptive noise and simultaneously analyzed the stability and robustness on the estimation

Summary

Introduction

Considering the current severe environmental challenges and gradual exhaustion of non-renewable fossil fuels, electric vehicles (EVs) have been recognized by the global automotive industry as a potential alternative to the widely used internal combustion engine vehicles [1]. To improve the economic cost and cruising range of EVs, many studies focus on advancing the battery technology suitable for EVs [2]. To guarantee the safety and reliability of EVs, battery management system (BMS) is equipped in the EVs. One of the key functions of the BMS is to estimate the state-of-charge (SOC) of batteries, which is defined as the ratio of the battery’s current capacity to the nominal capacity [5]. Based on the SOC estimation, the BMS adopts an appropriate control strategy to make the EVs work safely and efficiently [6]

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Conclusion