Robust battery fuel gauge algorithm development, part 3: State of charge tracking

Document Type

Conference Proceeding

Publication Date

1-20-2014

Publication Title

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

First Page

110

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

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.

DOI

10.1109/ICRERA.2014.7016540

ISBN

9781479937950

Link to Full Text

Share

COinS