SOC estimation of HEV/EV battery using series kalman filter

Atsushi Baba, Shuichi Adachi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper proposes a method of accurately estimating the state of charge (SOC) of rechargeable batteries in high fuel efficiency vehicles, such as hybrid electric vehicles (HEV s) and electric vehicles (EV s). Despite the importance of accurately estimating the SOC of batteries to achieve maximum efficiency and safety, no method thus far has been able to do so. This paper focuses on the simplification of a battery model, estimation of time-varying battery parameters, and estimation of SOC under measurement noises. To address these three issues, a model-based approach that uses a cascaded combination of two Kalman filters, ̈Series Kalman Filters̈ is proposed and implemented. This approach is verified by performing a series of simulations under an HEV operating environment. The ultimate goal is to design a state estimator capable of accurately estimating the state of any kinds of batteries under every possible user condition.

Original languageEnglish
JournalIEEJ Transactions on Industry Applications
Volume132
Issue number9
DOIs
Publication statusPublished - 2012

Fingerprint

Kalman filters
Secondary batteries
Hybrid vehicles
Electric vehicles

Keywords

  • Electric vehicle (EV)
  • Hybrid electric vehicle (HEV)
  • Kalman filter
  • Parameter estimation
  • Rechargeable battery
  • State of charge (SOC)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

Cite this

SOC estimation of HEV/EV battery using series kalman filter. / Baba, Atsushi; Adachi, Shuichi.

In: IEEJ Transactions on Industry Applications, Vol. 132, No. 9, 2012.

Research output: Contribution to journalArticle

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