Term of Award

Fall 2017

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

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

Department

Department of Electrical Engineering

Committee Chair

Adel El Shahat

Committee Member 1

Mohammad Ahad

Committee Member 2

Rami Haddad

Abstract

Energy storage has been utilized in many forms and applications from a flashlight to the Space Shuttle. There is a worldwide effort to develop battery model with high energy level and power densities for a variety range of applications, including hybrid electric vehicles (HEV) and photovoltaic system (PV). To improve battery technology, understanding the battery modeling is very important. So, modeling the thermal behavior of a battery is a vital consideration before designing an effective thermal management system which will operate safely and prolong the lifespan of an energy storage system. The first part of this work focused on the aging model of lithium-ion battery and a simple thermal model of lithium-ion and lead-acid battery using MATLAB/Simulink. After that, an artificial neural network model (ANN) is developed to predict various characteristics at wide temperature range. In this case, comparisons between the training/testing data outputs and targets validating both models with a regression accuracy of 99.839% and 98.727% respectively for Li-ion and Lead-Acid battery while it is 99.912% for the aging model of Li-ion battery. In the end, this energy storage device is used to interconnect with HOMER. This HOMER project aims at designing a solar-wind hybrid power system for Statesboro, Georgia. The cost analysis is performed utilizing HOMER software based on solar irradiance, wind speed, and residential load profile. The proposed HOMER model, using solar & wind with the grid was more cost efficient as the cost of energy (COE) was found 0.0618$/kWh where the average residential electricity rate in Statesboro is 0.116$/kWh. As a result of using this model, the total cost is reduced by 46.72% compared to other conventional power systems. In the second part of HOMER simulation, while comparing among three types of storage devices, another minimum COE is found using wind with grid connection. As the wind speed is good enough for Statesboro, Georgia, simulation shows that minimum COE is 0.0499$/kWh, 0.0386$/kWh and 0.0633$/kWh respectively for Li-ion, Lead-acid, and Vanadium.

Research Data and Supplementary Material

No

Available for download on Saturday, November 17, 2018

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