DocumentCode :
234163
Title :
Robust battery fuel gauge algorithm development, part 2: Online battery-capacity estimation
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 :
104
Lastpage :
109
Abstract :
In this paper we present an approach for robust, real time capacity estimation in Li-ion batteries. The proposed capacity estimation scheme has the following novel features: it employes total least squares (TLS) estimation in order to account for uncertainties in both model and the observations in capacity estimation. The TLS method can adaptively track changes in battery capacity. We propose a second approach to estimate battery capacity by exploiting rest states in the battery. This approach is devised to minimize the effect of hysteresis in capacity estimation. Finally, we propose a novel approach for optimally fusing capacity estimates obtained through different methods. We demonstrate the performance of the algorithm through objective experiments.
Keywords :
battery management systems; least squares approximations; secondary cells; TLS estimation; lithium-ion batteries; online battery-capacity estimation; real time capacity estimation; robust battery fuel gauge algorithm development; total least squares estimation; Batteries; Current measurement; Estimation error; Hysteresis; Robustness; System-on-chip; Battery fuel gauge (BFG); Battery management system (BMS); Li-ion battery; capacity estimation; capacity fade; extended Kalman filter (EKF); state of charge (SOC); total least squares (TLS);
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.2014.7016539
Filename :
7016539
Link To Document :
بازگشت