SOC estimation of HEV/EV battery using series kalman filter

Atsushi Baba, Shuichi Adachi

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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 (HEVs) and electric vehicles (EVs). 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 in the presence of measurement noise. To address these three issues, a model-based approach that uses a cascaded combination of two Kalman filters, "series Kalman filters," is proposed and implemented. This approach is verified by performing a series of simulations in an HEV operating environment. The ultimate goal is to design a state estimator capable of accurately estimating the state of any kind of batteries under every possible user condition.

Original languageEnglish
Pages (from-to)53-62
Number of pages10
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Issue number2
Publication statusPublished - 2014 Apr
Externally publishedYes


  • Kalman filter
  • electric vehicle (EV)
  • hybrid electric vehicle (HEV)
  • parameter estimation
  • rechargeable battery
  • state of charge (SOC)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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