DocumentCode :
234167
Title :
Robust battery fuel gauge algorithm development, part 3: State of charge tracking
Author :
Balasingam, B. ; Avvari, G.V. ; Pattipati, B. ; Pattipati, K. ; Bar-Shalom, Y.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Connectiut, Storrs, CT, USA
fYear :
2014
fDate :
19-22 Oct. 2014
Firstpage :
110
Lastpage :
115
Abstract :
In this paper, we present a novel SOC tracking algorithm for Li-ion batteries. The proposed approach employs a voltage drop model that avoid the need for modeling the hysteresis effect in the battery. Our proposed model results in a novel reduced order (single state) filtering for SOC tracking where no additional variables need to be tracked regardless of the level of complexity of the battery equivalent model. We identify the presence of correlated noise that has been so far ignored in the literature and use this for improved SOC tracking. The proposed approach performs within 1% or better SOC tracking accuracy based on both simulated as well as HIL evaluations.
Keywords :
Kalman filters; adaptive filters; electric potential; hysteresis; nonlinear filters; reduced order systems; secondary cells; HIL evaluation; Li-ion battery; SOC tracking; adaptive nonlinear filtering; battery equivalent model; correlated noise; extended Kalman filter; hysteresis effect; reduced order filtering; robust battery fuel gauge algorithm; state of charge tracking; voltage drop model; Batteries; Battery charge measurement; Current measurement; Hysteresis; Integrated circuit modeling; System-on-chip; Voltage measurement; Battery fuel gauge (BFG); Battery management system (BMS); adaptive nonlinear filtering; extended Kalman filter (EKF); online system identification; reduced order filtering; state of charge (SOC);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Renewable Energy Research and Application (ICRERA), 2014 International Conference on
Conference_Location :
Milwaukee, WI
Type :
conf
DOI :
10.1109/ICRERA.2014.7016540
Filename :
7016540
Link To Document :
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