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    Optimizing the Path Towards Clean Energy Transition: A Study on Li-Ion Battery Recycling and Shared Electric Transport

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    azu_etd_21822_sip1_m.pdf
    Embargo:
    2031-01-01
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    Author
    Saha, Apurba Kumar
    Issue Date
    2024
    Keywords
    Autonomous electric vehicle
    Carsharing
    Government policy
    Li-ion battery recycling
    Optimization
    Sustainability
    Advisor
    Jin, Hongyue
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Release after 01/01/2031
    Abstract
    This dissertation explores two critical components essential for advancing the clean energy transition: the sustainable management of Li-ion battery (LIB) recycling and the design of autonomous electric carsharing systems. These topics address the growing demand for renewable energy storage and electric mobility while tackling the environmental and economic challenges posed by the end-of-life management of LIBs. The first chapter investigates the optimal reverse logistics supply chain for the collection and recycling of spent LIBs, which are increasingly used in renewable energy storage and electric vehicles. A bi-level mixed integer optimization model is developed to evaluate the economic and environmental benefits of large-scale LIB recycling in the United States. The study also assesses the impact of government policies on recycling adoption and material recovery, crucial for securing critical resources like lithium, cobalt, and nickel. The results indicate that without government intervention, up to 57% of available feedstock can be collected over a ten-year period. However, with proper policy measures, such as mandating a 90% collection rate and providing subsidies, feedstock collection could rise to 94%. The second chapter delves deeper into the economic uncertainties that hinder the development of a robust LIB recycling infrastructure in the United States. A two-stage stochastic model is proposed to optimize the profitability of a reverse logistics network in the face of fluctuating feedstock volumes and material prices. A sample average approximation framework is used to solve the model. The results suggest adding buffer capacity to the selected facilities to manage uncertainties. The study also compares the effectiveness of government incentive policies under deterministic and stochastic environments. In the third chapter, the dissertation shifts focus to the optimization of a station-based autonomous electric carsharing system designed to reduce carbon emissions while maintaining profitability. A bi-objective optimization model is developed to determine the placement and capacity of parking stations and to plan vehicle assignments for handling trips. Using a case study, the model reveals a trade-off between profitability and carbon reduction, with profit-maximizing strategies leading to higher emissions, while prioritizing sustainability results in lower profitability. This analysis provides a framework for operators to balance economic and environmental objectives, demonstrating how smart design of electric shared transport systems can contribute to cleaner urban mobility. Overall, this dissertation presents a comprehensive approach to optimizing two key sectors—Li-ion battery recycling and electric carsharing systems—that are vital for achieving a clean energy transition. Through advanced optimization models, policy recommendations, and strategic insights, this work contributes to the broader goal of promoting sustainable energy solutions and reducing carbon emissions in the face of increasing global energy demand.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Systems & Industrial Engineering
    Degree Grantor
    University of Arizona
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    Dissertations

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