SOC estimation of HEV/EV battery using series kalman filter

Atsushi Baba, Shuichi Adachi

研究成果: Article査読

3 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)907-914+7
ジャーナルieej transactions on industry applications
132
9
DOI
出版ステータスPublished - 2012

ASJC Scopus subject areas

  • 産業および生産工学
  • 電子工学および電気工学

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