Book Description

Electric vehicles (EVs) are widely considered a sustainable substitution to conventional vehicles to mitigate fossil fuel dependence and reduce tail-pipe emissions. However, limited ranges, long charging times, and lack of charging infrastructure have hindered EV's market acceptance. This calls for more investments in building charging stations and advancing battery and charging technologies to obviate issues associated with EVs and increase their market share and improve sustainability. This study introduces modeling frameworks to optimize fast-charging infrastructure locations at the network level to address the challenges associated with EVs. Furthermore, it investigates the required charging investments for the current and future EV market shares, technology advancements, and seasonal demand variations. First, this study seeks an optimal configuration for plug-in electric vehicle charging infrastructure that supports their long-distance intercity trips at the network level. A mathematical optimization model is proposed which minimizes the total system cost and considers the range anxiety, multiple refueling, maximum capacity, charging delay, and detour time. This study considers the impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles considering a user equilibrium framework. This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations in intercity networks. Then, the study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations, as well as different battery sizes and charger technologies, the main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. This study incorporates the developed intercity model to suggest the optimal locations of EV fast chargers to be implemented in Michigan.Next, this study introduces an integrated framework for urban fast-charging infrastructure to address the range anxiety issue in urban networks. Unlike intercity trips that start with fully charged batteries, urban trips might start with any state of charge because of home/work chargers' unavailability, being part of a trip chain, and forgetting to charge overnight. A mesoscopic simulation tool is incorporated to generate trip trajectories, and a state-of-the-art tool is developed to simulate charging behavior based on various trip attributes for these trajectories. The resulting temporal charging demand is the key element in finding the optimum charging infrastructure. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Finally, this study generates forecasting models to estimate the number of chargers and charging stations to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan.