Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery

Quan Sun,Wentao Ma,Jianrong Zhang,Hong Zhang

doi:10.3390/en11113123

Abstract

As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises.

Highlights

Reliable and accurate state of charge (SOC) estimate of lithium-ion battery plays a crucial role in battery management systems (BMS) [1,2,3,4,5,6]
We focused on the development of a novel SOC estimate method based on the proposed adaptive correntropy unscented KF (CUKF)
We performed experiments to verify the effectiveness and feasibility of the proposed adaptive CUKF (ACUKF) for SOC estimation of lithium ion with respect to the database available in the research of the equivalent circuit mode (ECM) data repository [36], and the true SOC was obtained by subtracting the net charge flow from the charge in a fully charged cell

Summary

Introduction

Reliable and accurate state of charge (SOC) estimate of lithium-ion battery plays a crucial role in battery management systems (BMS) [1,2,3,4,5,6]. For SOC estimate, the traditional linear KF-aware algorithms can overcome the error accumulation effect of the coulomb counting method, but it does not depend on an accurate initial SOC value [3,4]. The accuracy of this method relies on the establishment of a battery equivalent circuit mode (ECM), and some physical properties of the battery model are nonlinear [5,6]. For this reason, the extended KF (EKF) algorithm is proposed in this paper using first-order Taylor series expansion to improve the performance of conventional KF algorithms, which implements recursive filtering by linearizing nonlinear functions. To circumvent first-order approximation errors of EKF, the UKF algorithm was developed by applying nonlinear system equations to the standard KF by means of unscented transformation (UT) naturally [7,8,9,10,11]

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Conclusion