Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications

Farshid Naseri, Ebrahim Farjah*, Teymoor Ghanbari, Zahra Kazemi, Erik Schaltz, Jean-Luc Schanen

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstrakt

Online accurate estimation of supercapacitor State-of-Health (SoH) and State-of-Energy (SoE) is essential to achieve efficient energy management and real-time condition monitoring in Electric Vehicle (EV) applications. In this paper, for the first time, Unscented Kalman Filter (UKF) is used for online parameter and state estimation of the supercapacitor. In the proposed method, a nonlinear state-space model of the supercapacitor is developed, which takes the capacitance variation and selfdischarge effects into account. The observability of the considered model is analytically confirmed using a graphical approach (GA). The SoH and SoE are then estimated based on the supercapacitor online identified model with the designed UKF. The proposed method provides better estimation accuracy over KF and Extended KF (EKF) algorithms since the linearization errors during the filtering process are avoided. The effectiveness of the proposed approach is demonstrated through several experiments on a laboratory testbed. An overall estimation error below 0.5% is achieved with the proposed method. In addition, Hardware-in-the-Loop (HIL) experiments are conducted and real-time feasibility of the proposed method is guaranteed.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Industrial Electronics
Antal sider10
ISSN0278-0046
DOI
StatusE-pub ahead of print - 2020

Fingeraftryk Dyk ned i forskningsemnerne om 'Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications'. Sammen danner de et unikt fingeraftryk.

  • Citationsformater