Masters Theses

Keywords and Phrases

Battery electric vehicle; Discrete-Event; Modeling; Optimization; Simulation

Abstract

"With climate change concerns escalating, international agreements such as the Kyoto Protocol, the US Presidential Policy, and the Paris Agreement aim to reduce greenhouse gas (GHG) emissions, targeting significant reductions by 2050. The mining sector, a notable contributor to GHG emissions primarily through diesel-powered material haulage, emits approximately 68 million tons of CO2 annually. Transitioning to Battery Electric Trucks (BETs) presents a viable mitigation strategy by replacing diesel trucks with electric alternatives, thus eliminating CO2 emissions.

However, the effectiveness of BETs hinges on optimized battery swapping and charging procedures. This study employs Discrete Event Simulation (DES), a computational methodology for simulating system operations as discrete events, to optimize these procedures in underground mining. The approach entails developing a DES model to evaluate and enhance battery swapping and charging efficiency, focusing on critical metrics like truck availability, charging unit utilization, queues generated during battery charging, and battery wait times post-charging.

Using Arena® software, a DES model was created to replicate the overall system and evaluate the key performance metrics. The base case scenario ensured 100% truck availability but had inefficiencies in charger utilization and battery waiting times. Scenario 53, involving eight batteries, four trucks, and four chargers, emerged as the most efficient, balancing charger utilization and battery waiting time while maintaining 100% truck availability without queues at the charging station. This finding is crucial for the mining industry as BETs gain prevalence, offering a sustainable solution for reducing GHG emissions" -- Abstract, p. iii

Advisor(s)

Frimpong, Samuel

Committee Member(s)

Awuah-Offei, Kwame 1975-
Galecki, Greg

Department(s)

Mining Engineering

Degree Name

M.S. in Mining Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

xi, 107 pages

Note about bibliography

Includes_bibliographical_references_(pages 97-106)

Rights

©2024 Albert Einstein Amponsem , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12370

Electronic OCLC #

1460010206

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