Medium-duty Electric Vehicle Infrastructure Planning and Operations in Urban Transportation Networks

This dissertation focuses on the electrification of freight and transit vehicles as a sustainable solution to mitigate greenhouse gas emissions. The primary objectives of this dissertation focuses on efficiently addressing Electric Vehicle Supply Equipment Location and Capacity Allocation (EVSELCA) problems, optimizing Single Depot Electric Vehicle Scheduling Problem (SDEVSP) for urban transit systems, and optimizing the Electric Bus Scheduling and Charger Location (EBSCL). By addressing these objectives, this dissertation aims to contribute to the advancement of sustainable and environmentally responsible transportation in the context of heavy electric vehicles. In this regard, first, this dissertation introduces a mixed-integer linear programming (MILP) model for EVSELCA. This model optimizes the locations and number and type of chargers, aiming to minimize strategic investment costs. Second, the research proposes a two-step solution to SDEVSP. In the first step, an integer programming model generates blocks of consecutive trips. The second step introduces an MILP, which involves chaining these blocks to create efficient bus runs, optimizing recharging between blocks, and ensuring next day operability constraints are satisfied. Finally, the dissertation presents an integrated model that optimizes both electric vehicle scheduling and charger location. Each developed model undergoes computational performance analysis, and a comprehensive case study is designed to provide key managerial and policy insights, along with extensive sensitivity analyses aiming to identify crucial parametric levers.