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- All Subjects: Electric Vehicles
Description
Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of the vehicles have a range that is less than those powered by gasoline. These factors together create a "range anxiety" in drivers, which causes the drivers to worry about the utility of alternative-fuel and electric vehicles and makes them less likely to purchase these vehicles. For the new vehicle technologies to thrive it is critical that range anxiety is minimized and performance is increased as much as possible through proper routing and scheduling. In the case of long distance trips taken by individual vehicles, the routes must be chosen such that the vehicles take the shortest routes while not running out of fuel on the trip. When many vehicles are to be routed during the day, if the refueling stations have limited capacity then care must be taken to avoid having too many vehicles arrive at the stations at any time. If the vehicles that will need to be routed in the future are unknown then this problem is stochastic. For fleets of vehicles serving scheduled operations, switching to alternative-fuels requires ensuring the schedules do not cause the vehicles to run out of fuel. This is especially problematic since the locations where the vehicles may refuel are limited due to the technology being new. This dissertation covers three related optimization problems: routing a single electric or alternative-fuel vehicle on a long distance trip, routing many electric vehicles in a network where the stations have limited capacity and the arrivals into the system are stochastic, and scheduling fleets of electric or alternative-fuel vehicles with limited locations to refuel. Different algorithms are proposed to solve each of the three problems, of which some are exact and some are heuristic. The algorithms are tested on both random data and data relating to the State of Arizona.
ContributorsAdler, Jonathan D (Author) / Mirchandani, Pitu B. (Thesis advisor) / Askin, Ronald (Committee member) / Gel, Esma (Committee member) / Xue, Guoliang (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Lithium ion batteries are quintessential components of modern life. They are used to power smart devices — phones, tablets, laptops, and are rapidly becoming major elements in the automotive industry. Demand projections for lithium are skyrocketing with production struggling to keep up pace. This drive is due mostly to the rapid adoption of electric vehicles; sales of electric vehicles in 2020 are more than double what they were only a year prior. With such staggering growth it is important to understand how lithium is sourced and what that means for the environment. Will production even be capable of meeting the demand as more industries make use of this valuable element? How will the environmental impact of lithium affect growth? This thesis attempts to answer these questions as the world looks to a decade of rapid growth for lithium ion batteries.
ContributorsMelton, John (Author) / Brian, Jennifer (Thesis director) / Karwat, Darshawn (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The Cognitive Decision Support (CDS) model is proposed. The model is widely applicable and scales to realistic, complex decision problems based on adaptive learning. The utility of a decision is discussed and four types of decisions associated with CDS model are identified. The CDS model is designed to learn decision utilities. Data enrichment is introduced to promote the effectiveness of learning. Grouping is introduced for large-scale decision learning. Introspection and adjustment are presented for adaptive learning. Triage recommendation is incorporated to indicate the trustworthiness of suggested decisions.
The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management.
Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning.
A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example.
The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management.
Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning.
A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example.
ContributorsKee, Seho (Author) / Runger, George C. (Thesis advisor) / Escobedo, Adolfo (Committee member) / Gel, Esma (Committee member) / Janakiram, Mani (Committee member) / Rogers, Dale (Committee member) / Arizona State University (Publisher)
Created2020
Description
The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has stressed the differences in the ways that engineers and designers approach problem solving and creating solutions, but I am most interested in marrying the two mindsets of designers and engineers to better solve problems creatively and efficiently.
This thesis focuses on the recent appearance of generative design technology into the world of industrial design and engineering as it relates to product development. An introduction to generative design discusses the uses and benefits of this tool for both designers and engineers and also addresses the challenges of this technology. The relevance of generative design to the world of product development is discussed as well as the implications of how this technology will change the roles of designers and engineers, and especially their traditional design processes. The remainder of this paper is divided into two elements. The first serves as documentation of my own exploration of using generative design software to solve a product design challenge and my reflections on the benefits and challenges of using this tool. The second element addresses the need for employing quantitiative methodologies within the generative design process to aid designers in selecting the most advantageous design option when presented with generative outcomes. Both sections aim to provide more context to this new design process and seek to answer questions about some of the ambiguous processes of generative design.
This thesis focuses on the recent appearance of generative design technology into the world of industrial design and engineering as it relates to product development. An introduction to generative design discusses the uses and benefits of this tool for both designers and engineers and also addresses the challenges of this technology. The relevance of generative design to the world of product development is discussed as well as the implications of how this technology will change the roles of designers and engineers, and especially their traditional design processes. The remainder of this paper is divided into two elements. The first serves as documentation of my own exploration of using generative design software to solve a product design challenge and my reflections on the benefits and challenges of using this tool. The second element addresses the need for employing quantitiative methodologies within the generative design process to aid designers in selecting the most advantageous design option when presented with generative outcomes. Both sections aim to provide more context to this new design process and seek to answer questions about some of the ambiguous processes of generative design.
ContributorsElgin, Mariah Crystal (Author) / Bacalzo, Dean (Thesis director) / Gel, Esma (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Dean, Herberger Institute for Design and the Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05