Robust battery fuel gauge algorithm development, part 2: Online battery-capacity estimation

Document Type

Conference Proceeding

Publication Date

1-20-2014

Publication Title

3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014

First Page

104

Keywords

Battery fuel gauge (BFG), Battery management system (BMS), capacity estimation, capacity fade, extended Kalman filter (EKF), Li-ion battery, state of charge (SOC), total least squares (TLS)

Last Page

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.

DOI

10.1109/ICRERA.2014.7016539

ISBN

9781479937950

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