Date of Award

9-28-2022

Publication Type

Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

Keywords

Lithium-ion Batteries, OCV Modeling, Tabular OCV Modeling

Supervisor

B.Balasingam

Supervisor

C.Lee

Rights

info:eu-repo/semantics/embargoedAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Battery management systems depend on open circuit voltage (OCV) characterizationfor state of charge (SOC) estimation in real-time. The traditional approach to OCVSOC characterization involves collecting OCV-SOC data from sample battery cellsand then fitting a polynomial model to this data. The parameters of these polynomialmodels are known as the OCV-parameters, or OCV-SOC parameters, in batterymanagement systems and are used for real-time SOC estimation. Even though thetraditional OCV-SOC characterization approaches are able to abstract the OCV-SOCbehavior of a battery in a few parameters, storage, and processing of these parametersonline demand significant memory in a practical battery management system. It isalso possible that these parameters need to be rounded before storage due to memoryconstraints in computationally restrictive applications. In this paper, several existingOCV-SOC parameters are compared in terms of their memory requirements andaccuracy losses due to rounding. Then, a tabularized OCV-SOC model is presentedas an alternative to polynomial models. Three different approaches are presented toconvert traditional high-precision OCV-SOC curves into tabular OCV-SOC models.It is shown that the proposed tabular OCV-SOC model requires significantly lessmemory and shows little sensitivity to rounding.

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