A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)387-399
Journal / PublicationApplied Energy
Volume164
Early online date22 Dec 2015
Publication statusPublished - 15 Feb 2016

Abstract

Accurate state-of-charge (SOC) estimation is critical for the safety and reliability of battery management systems in electric vehicles. Because SOC cannot be directly measured and SOC estimation is affected by many factors, such as ambient temperature, battery aging, and current rate, a robust SOC estimation approach is necessary to be developed so as to deal with time-varying and nonlinear battery systems. In this paper, three popular model-based filtering algorithms, including extended Kalman filter, unscented Kalman filter, and particle filter, are respectively used to estimate SOC and their performances regarding to tracking accuracy, computation time, robustness against uncertainty of initial values of SOC, and battery degradation, are compared. To evaluate the performances of these algorithms, a new combined dynamic loading profile composed of the dynamic stress test, the federal urban driving schedule and the US06 is proposed. The comparison results showed that the unscented Kalman filter is the most robust to different initial values of SOC, while the particle filter owns the fastest convergence ability when an initial guess of SOC is far from a true initial SOC.

Research Area(s)

  • Degradation, Extended Kalman filter, Lithium-ion batteries, Particle filter, State of charge, Unscented Kalman filter

Citation Format(s)

A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile. / Yang, Fangfang; Xing, Yinjiao; Wang, Dong; Tsui, Kwok-Leung.

In: Applied Energy, Vol. 164, 15.02.2016, p. 387-399.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review