Frequency response-based initial parameter estimation for SOC of lithium-ion battery

Kohei Natori, Keisuke Mizuno, Toru Namerikawa, Sabrina Sartori, Frank Eliassen

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, we propose a novel parameter initial value estimation method of Lithium-ion battery for state of charge (SOC) estimation by using Local Regression Modeling (LRM) and Vector Fitting (VF). To estimate SOC accurately using nonlinear Extended Kalman Filter(EKF), an adequate set of initial parameters of the battery model are required. Therefore, we apply LRM and VF method to derive the initial parameters and the battery model from a constructed database of frequency response data. In addition, we demonstrate the SOC estimation method using EKF by the determined initial value and model. Finally, the effectiveness of the proposed method is shown via several experimental results.

Original languageEnglish
Pages (from-to)12695-12700
Number of pages6
JournalIFAC-PapersOnLine
Volume53
Issue number2
DOIs
Publication statusPublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 2020 Jul 122020 Jul 17

Keywords

  • Battery
  • Extended kalman filter
  • Local regression modeling
  • SOC
  • Vector fitting

ASJC Scopus subject areas

  • Control and Systems Engineering

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