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SOC Estimation of HEV/EV Battery Using Series Kalman Filter
Author(s) -
Baba Atsushi,
Adachi Shuichi
Publication year - 2014
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.22511
Subject(s) - kalman filter , battery (electricity) , estimator , state of charge , series (stratigraphy) , electric vehicle , state (computer science) , extended kalman filter , computer science , engineering , automotive engineering , algorithm , power (physics) , artificial intelligence , mathematics , paleontology , statistics , physics , quantum mechanics , biology
SUMMARY 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. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(2): 53–62, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22511