Off-campus South Dakota State University users: To download campus access theses, please use the following link to log into our proxy server with your South Dakota State University ID and password.

Non-South Dakota State University users: Please talk to your librarian about requesting this thesis through interlibrary loan.

Document Type

Thesis - University Access Only

Award Date

2012

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

First Advisor

David W. Gailpeau

Abstract

Microgrids often require energy storage to improve the efficiency and economics of the available energy sources. Batteries are a common technology to fulfill these needs. Over time, a battery's state-of-health (SoH) decreases, which reduces its energy storage capacity and can compromise the microgrid's efficiency and reliability. The major requirements for a battery state-of-health measurement system include: 1) batteries must remain online during the state-of-health measurement; 2) minimal calibration required for each battery model; 3) state-of-health measurements can be obtained in less than two hours; and 4) state-of-health must be within 2% accuracy based the IEEE performance test. Traditional methods and some alternative methods require complex studies of the battery aging process or cannot be used when the batteries are under load. Extended Kalman Filters are able to track nonlinear systems and include unaccounted for system dynamics. The objectives of this research were to simulate the Extended Kalman Filter using Randle's battery circuit model and Bhangu et al 's state space model in a computer environment; design the hardware and software for a state-of-health measurement system; test the state-of-health measurement system against the IEEE standard; and examine the application of this technique in an embedded system. A static resistance was initially used in the battery model, but it could only identify relative state-of-health between batteries at high states-of-charge. The state-of-health error was at best 10% error using a 29.5A constant current load and at 100% state-of-charge. The Extended Kalman Filter did not converge when the state-of-charge was at 50% or below. A dynamic internal resistance gave a best case error of 10% using a 29.5A load at 75% state-of-charge, but it was unstable. Future work should include tracking how the circuit model resistances change with state-of-charge and current, as well as techniques that can calculate the circuit resistances dynamically.

Library of Congress Subject Headings

Storage batteries
Storage batteries -- Reliability
Kalman filtering

Description

Includes bibliographical references (pages 99-104)

Format

application/pdf

Number of Pages

115

Publisher

South Dakota State University

Rights

In Copyright - Non-Commercial Use Permitted
http://rightsstatements.org/vocab/InC-NC/1.0/

Share

COinS