Date of Award

8-27-2019

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

Keywords

Battery fuel gauge evaluation, battery impedance, Battery management systems, Extended Kalman filter, Recursive least square, State of Charge

Supervisor

Balasingam, B.

Rights

info:eu-repo/semantics/openAccess

Abstract

In this thesis, we presents a novel approach for system identification of a Li-ion batteries. First, we we present a robust approach to estimate the equivalent circuit model (ECM) parameters of a Li-ion battery that offers several advantages over existing ones. Particularly, (i) The proposed approach depends only on measured voltage across and current through the battery; (ii) We theoretically derive the estimation error in terms of the voltage and current measurement errors; (iii) The proposed approach is unaffected by the effect of hysteresis in batteries; (iv) The proposed approach is applicable in time-varying conditions; and (v) The new ECM identification approach can be implemented for different ECM approximations with little change in the algorithm. The proposed algorithm was tested on simulated as well as real world battery data and found to be accurate within 1% uncertainty. Finally, we discuss about the battery fuel gauge (BFG). We suggest an improved BFG of an existing one that estimate ECM parameter as well as the state of charge (SOC) in real time. Then we use the BFG evaluation scheme to validate the performance of an improved BFG and compare its performance with its predecessor. The comparison shows the performance improvement of the improved BFG in objective terms - using the BFG evaluation metrics. Therefore, this thesis further aims to highlight the importance of employing objective performance analysis to quantify the performance of different versions of BFG being proposed in the literature and demonstrate its use case using simulated as well as real world data.

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