Robust battery fuel gauge algorithm development, part 1: Online parameter 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

98

Keywords

adaptive nonlinear filtering, Battery fuel gauge (BFG), Battery management system (BMS), extended Kalman filter (EKF), online system identification, reduced order filtering, state of charge (SOC)

Last Page

103

Abstract

In this paper, we present a novel voltage drop model for battery SOC tracking and develop a robust, realtime approach for model parameter estimation. The proposed model avoids the need to model hysteresis voltage that hard to model and estimate in practical applications. Another advantage of the proposed voltage drop model is that the parameters of the model is estimated linearly, regardless of the model complexity, i.e., number of RC elements considered in the model. We identify the presence of correlated noise that has been so far ignored in the literature and use it to enhance the accuracy of model identification. The proposed parameter approach enables a robust initialization/re-initialization strategy for continuous operation of the battery fuel gauge (BFG). The performance of the online parameter estimation scheme was evaluated through objective measures.

DOI

10.1109/ICRERA.2014.7016538

ISBN

9781479937950

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