{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/31451047"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/31451047","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Planning and Optimization of Charging Strategies for Electric Vehicles: From Infrastructure to Behavioral","abstract":"The transition to electric vehicles (EVs), including both publicly operated battery electric buses (BEBs) and privately owned EVs, is a pivotal step toward achieving sustainable and efficient urban mobility. This dissertation addresses critical dimensions of this transition, integrating advanced optimization methods with empirical behavioral research across three interconnected studies. The first study tackles the Problem of Locating and Allocating Charging Equipment for Battery Electric Buses (PLACE-BEB), a core challenge in planning for transit electrification. Recognizing the stochastic nature of charging demand across the network, a Mixed-Integer Non-Linear Programming (MINLP) model is developed to determine the optimal siting and allocation of slow and fast chargers. The framework explicitly incorporates queueing delays through an M/M/s system, treating the number of chargers as a decision variable to account for congestion and wait times. To improve computational tractability, especially for large-scale instances, Simulated Annealing (SA) and Genetic Algorithm (GA) heuristics are designed and benchmarked. A case study involving the Chicago public transit system demonstrates the model’s effectiveness and underscores the importance of jointly considering garage and on-route charging locations. The findings highlight that minimizing waiting time, through strategic charger placement and allocation, is crucial for ensuring the economic and operational viability of BEB deployment. Expanding upon this planning foundation, the second study proposes a comprehensive Mixed-Integer Linear Programming (MILP) framework to optimize the scheduling and charging strategies of a mixed fleet of BEBs and Diesel Buses (DBs). The model determines the optimal fleet composition, trip assignments, and partial charging strategies at both garages and terminal stations, accommodating both slow and fast chargers. A dynamic queuing mechanism is introduced to replace traditional first-come, first-served protocols, improving charger utilization and overall system efficiency. Applied to real-world data from Chicago’s CTA and Pace systems, the model reveals that while BEBs have higher acquisition costs, they can effectively serve more trips due to their lower operational costs and optimized charging schedules. The results demonstrate that mixed-fleet electrification, supported by smart scheduling and flexible charging, can lead to substantial cost savings and improved service reliability. While the first two studies employ optimization frameworks to resolve infrastructure and operational challenges, the third study complements these by investigating behavioral factors influencing EV adoption and usage, an essential yet often overlooked aspect of transportation electrification. Unlike the previous optimization-centric chapters, this ongoing research draws on original data from a comprehensive online survey we designed and administered, capturing public attitudes toward EVs, autonomous vehicles, and associated mobility trends. Preliminary analyses have employed Structural Equation Modeling (SEM) to explore how latent constructs such as ``Range Anxiety,'' ``Convenience,'' ``Environmentally Friendly,'' and ``Living Urban'' influence charging location choices. Ongoing efforts aim to refine and deepen this analysis by explicitly modeling ``Range Anxiety'' as a latent psychological construct using advanced methods (SEM or Hybrid Choice Modeling). This planned modeling will quantify the influence of range anxiety on EV adoption intentions and driving behaviors, providing critical behavioral insights to complement infrastructure and operational optimization. Together, these three studies present a comprehensive framework for advancing sustainable transportation, bridging infrastructure planning, operations research, and behavioral analysis. By integrating optimization models with behavioral insights, this dissertation provides actionable tools and knowledge for transit agencies, policymakers, and urban planners aiming to accelerate electrification while ensuring reliability, cost-effectiveness, and user acceptance.","abstract_html":"The transition to electric vehicles (EVs), including both publicly operated battery electric buses (BEBs) and privately owned EVs, is a pivotal step toward achieving sustainable and efficient urban mobility. This dissertation addresses critical dimensions of this transition, integrating advanced optimization methods with empirical behavioral research across three interconnected studies. The first study tackles the Problem of Locating and Allocating Charging Equipment for Battery Electric Buses (PLACE-BEB), a core challenge in planning for transit electrification. Recognizing the stochastic nature of charging demand across the network, a Mixed-Integer Non-Linear Programming (MINLP) model is developed to determine the optimal siting and allocation of slow and fast chargers. The framework explicitly incorporates queueing delays through an M/M/s system, treating the number of chargers as a decision variable to account for congestion and wait times. To improve computational tractability, especially for large-scale instances, Simulated Annealing (SA) and Genetic Algorithm (GA) heuristics are designed and benchmarked. A case study involving the Chicago public transit system demonstrates the model’s effectiveness and underscores the importance of jointly considering garage and on-route charging locations. The findings highlight that minimizing waiting time, through strategic charger placement and allocation, is crucial for ensuring the economic and operational viability of BEB deployment. Expanding upon this planning foundation, the second study proposes a comprehensive Mixed-Integer Linear Programming (MILP) framework to optimize the scheduling and charging strategies of a mixed fleet of BEBs and Diesel Buses (DBs). The model determines the optimal fleet composition, trip assignments, and partial charging strategies at both garages and terminal stations, accommodating both slow and fast chargers. A dynamic queuing mechanism is introduced to replace traditional first-come, first-served protocols, improving charger utilization and overall system efficiency. Applied to real-world data from Chicago’s CTA and Pace systems, the model reveals that while BEBs have higher acquisition costs, they can effectively serve more trips due to their lower operational costs and optimized charging schedules. The results demonstrate that mixed-fleet electrification, supported by smart scheduling and flexible charging, can lead to substantial cost savings and improved service reliability. While the first two studies employ optimization frameworks to resolve infrastructure and operational challenges, the third study complements these by investigating behavioral factors influencing EV adoption and usage, an essential yet often overlooked aspect of transportation electrification. Unlike the previous optimization-centric chapters, this ongoing research draws on original data from a comprehensive online survey we designed and administered, capturing public attitudes toward EVs, autonomous vehicles, and associated mobility trends. Preliminary analyses have employed Structural Equation Modeling (SEM) to explore how latent constructs such as ``Range Anxiety,'' ``Convenience,'' ``Environmentally Friendly,'' and ``Living Urban'' influence charging location choices. Ongoing efforts aim to refine and deepen this analysis by explicitly modeling ``Range Anxiety'' as a latent psychological construct using advanced methods (SEM or Hybrid Choice Modeling). This planned modeling will quantify the influence of range anxiety on EV adoption intentions and driving behaviors, providing critical behavioral insights to complement infrastructure and operational optimization. Together, these three studies present a comprehensive framework for advancing sustainable transportation, bridging infrastructure planning, operations research, and behavioral analysis. By integrating optimization models with behavioral insights, this dissertation provides actionable tools and knowledge for transit agencies, policymakers, and urban planners aiming to accelerate electrification while ensuring reliability, cost-effectiveness, and user acceptance.","abstract_has_math":false,"creators":["Sadjad Bazarnovi (23291308)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-01T00:00:00Z","date_published":"2025-12-01T00:00:00Z","updated_at":"2026-07-27T21:34:19Z","subjects":["Transportation"],"languages":[],"rights":["In Copyright"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.31451047.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sadjad Bazarnovi (23291308)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Planning_and_Optimization_of_Charging_Strategies_for_Electric_Vehicles_From_Infrastructure_to_Behavioral/31451047"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Transportation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.31451047.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The transition to electric vehicles (EVs), including both publicly operated battery electric buses (BEBs) and privately owned EVs, is a pivotal step toward achieving sustainable and efficient urban mobility. This dissertation addresses critical dimensions of this transition, integrating advanced optimization methods with empirical behavioral research across three interconnected studies. The first study tackles the Problem of Locating and Allocating Charging Equipment for Battery Electric Buses (PLACE-BEB), a core challenge in planning for transit electrification. Recognizing the stochastic nature of charging demand across the network, a Mixed-Integer Non-Linear Programming (MINLP) model is developed to determine the optimal siting and allocation of slow and fast chargers. The framework explicitly incorporates queueing delays through an M/M/s system, treating the number of chargers as a decision variable to account for congestion and wait times. To improve computational tractability, especially for large-scale instances, Simulated Annealing (SA) and Genetic Algorithm (GA) heuristics are designed and benchmarked. A case study involving the Chicago public transit system demonstrates the model’s effectiveness and underscores the importance of jointly considering garage and on-route charging locations. The findings highlight that minimizing waiting time, through strategic charger placement and allocation, is crucial for ensuring the economic and operational viability of BEB deployment. Expanding upon this planning foundation, the second study proposes a comprehensive Mixed-Integer Linear Programming (MILP) framework to optimize the scheduling and charging strategies of a mixed fleet of BEBs and Diesel Buses (DBs). The model determines the optimal fleet composition, trip assignments, and partial charging strategies at both garages and terminal stations, accommodating both slow and fast chargers. A dynamic queuing mechanism is introduced to replace traditional first-come, first-served protocols, improving charger utilization and overall system efficiency. Applied to real-world data from Chicago’s CTA and Pace systems, the model reveals that while BEBs have higher acquisition costs, they can effectively serve more trips due to their lower operational costs and optimized charging schedules. The results demonstrate that mixed-fleet electrification, supported by smart scheduling and flexible charging, can lead to substantial cost savings and improved service reliability. While the first two studies employ optimization frameworks to resolve infrastructure and operational challenges, the third study complements these by investigating behavioral factors influencing EV adoption and usage, an essential yet often overlooked aspect of transportation electrification. Unlike the previous optimization-centric chapters, this ongoing research draws on original data from a comprehensive online survey we designed and administered, capturing public attitudes toward EVs, autonomous vehicles, and associated mobility trends. Preliminary analyses have employed Structural Equation Modeling (SEM) to explore how latent constructs such as ``Range Anxiety,'' ``Convenience,'' ``Environmentally Friendly,'' and ``Living Urban'' influence charging location choices. Ongoing efforts aim to refine and deepen this analysis by explicitly modeling ``Range Anxiety'' as a latent psychological construct using advanced methods (SEM or Hybrid Choice Modeling). This planned modeling will quantify the influence of range anxiety on EV adoption intentions and driving behaviors, providing critical behavioral insights to complement infrastructure and operational optimization. Together, these three studies present a comprehensive framework for advancing sustainable transportation, bridging infrastructure planning, operations research, and behavioral analysis. By integrating optimization models with behavioral insights, this dissertation provides actionable tools and knowledge for transit agencies, policymakers, and urban planners aiming to accelerate electrification while ensuring reliability, cost-effectiveness, and user acceptance."]},{"key":"dc:title","label":"Title","values":["Planning and Optimization of Charging Strategies for Electric Vehicles: From Infrastructure to Behavioral"]}]}],"canonical_facts":{"dc:creator":["Sadjad Bazarnovi (23291308)"],"dc:date":["2025-12-01T00:00:00Z"],"dc:description":["The transition to electric vehicles (EVs), including both publicly operated battery electric buses (BEBs) and privately owned EVs, is a pivotal step toward achieving sustainable and efficient urban mobility. This dissertation addresses critical dimensions of this transition, integrating advanced optimization methods with empirical behavioral research across three interconnected studies. The first study tackles the Problem of Locating and Allocating Charging Equipment for Battery Electric Buses (PLACE-BEB), a core challenge in planning for transit electrification. Recognizing the stochastic nature of charging demand across the network, a Mixed-Integer Non-Linear Programming (MINLP) model is developed to determine the optimal siting and allocation of slow and fast chargers. The framework explicitly incorporates queueing delays through an M/M/s system, treating the number of chargers as a decision variable to account for congestion and wait times. To improve computational tractability, especially for large-scale instances, Simulated Annealing (SA) and Genetic Algorithm (GA) heuristics are designed and benchmarked. A case study involving the Chicago public transit system demonstrates the model’s effectiveness and underscores the importance of jointly considering garage and on-route charging locations. The findings highlight that minimizing waiting time, through strategic charger placement and allocation, is crucial for ensuring the economic and operational viability of BEB deployment. Expanding upon this planning foundation, the second study proposes a comprehensive Mixed-Integer Linear Programming (MILP) framework to optimize the scheduling and charging strategies of a mixed fleet of BEBs and Diesel Buses (DBs). The model determines the optimal fleet composition, trip assignments, and partial charging strategies at both garages and terminal stations, accommodating both slow and fast chargers. A dynamic queuing mechanism is introduced to replace traditional first-come, first-served protocols, improving charger utilization and overall system efficiency. Applied to real-world data from Chicago’s CTA and Pace systems, the model reveals that while BEBs have higher acquisition costs, they can effectively serve more trips due to their lower operational costs and optimized charging schedules. The results demonstrate that mixed-fleet electrification, supported by smart scheduling and flexible charging, can lead to substantial cost savings and improved service reliability. While the first two studies employ optimization frameworks to resolve infrastructure and operational challenges, the third study complements these by investigating behavioral factors influencing EV adoption and usage, an essential yet often overlooked aspect of transportation electrification. Unlike the previous optimization-centric chapters, this ongoing research draws on original data from a comprehensive online survey we designed and administered, capturing public attitudes toward EVs, autonomous vehicles, and associated mobility trends. Preliminary analyses have employed Structural Equation Modeling (SEM) to explore how latent constructs such as ``Range Anxiety,'' ``Convenience,'' ``Environmentally Friendly,'' and ``Living Urban'' influence charging location choices. Ongoing efforts aim to refine and deepen this analysis by explicitly modeling ``Range Anxiety'' as a latent psychological construct using advanced methods (SEM or Hybrid Choice Modeling). This planned modeling will quantify the influence of range anxiety on EV adoption intentions and driving behaviors, providing critical behavioral insights to complement infrastructure and operational optimization. Together, these three studies present a comprehensive framework for advancing sustainable transportation, bridging infrastructure planning, operations research, and behavioral analysis. By integrating optimization models with behavioral insights, this dissertation provides actionable tools and knowledge for transit agencies, policymakers, and urban planners aiming to accelerate electrification while ensuring reliability, cost-effectiveness, and user acceptance."],"dc:identifier":["10.25417/uic.31451047.v1"],"dc:relation":["https://figshare.com/articles/thesis/Planning_and_Optimization_of_Charging_Strategies_for_Electric_Vehicles_From_Infrastructure_to_Behavioral/31451047"],"dc:rights":["In Copyright"],"dc:subject":["Transportation"],"dc:title":["Planning and Optimization of Charging Strategies for Electric Vehicles: From Infrastructure to Behavioral"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T21:34:19Z"}