This thesis proposes a planning framework for Autonomous Electric Vehicle (AEV) charging. The
framework is intended to help transportation decision-makers determine Electric Vehicle (EV) charging facility locations and capacities for the mixed fleet of Autonomous Vehicle (AV) and Human-driven Vehicle (HDV). The
bi-level nature of the framework captures the decision-making processes of the
transportation agency decision-makers and travelers, thereby providing solid
theoretical and practical foundations for the EV charging network design. At
the upper level, the decision-makers seek to determine the locations and
operating capacities of the EV charging facilities, in a manner that minimizes
total travel time and construction costs subject to budgetary limitations. In
addition, the transportation decision-makers provide AV-exclusive lanes to
encourage AV users to reduce travel time, particularly at wireless-charging
lanes, as well as other reasons, including safety. At the lower level, the
travelers seek to minimize their travel time by selecting their preferred
vehicle type (AV vs. HDV) and route. In measuring the users delay costs, the
thesis considered network user equilibrium because the framework is designed
for urban networks where travelers route choice affects their travel time. The
bi-level model is solved using the Non-Dominated Sorting Genetic Algorithm
(NSGA-II) algorithm.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14512059 |
| Date | 29 April 2021 |
| Creators | Amir Davatgari (10724118) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Location_planning_for_electric_charging_stations_and_wireless_facilities_in_the_era_of_autonomous_vehicle_operations/14512059 |
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