Reaching net-zero carbon emissions by 2050 is an overarching goal for sustainable development in the United States. Electric Vehicles (EVs) are being developed to reduce energy consumption during on-road operations and have become the cornerstone of sustainable transportation systems. This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data. This study first conducted a comprehensive and detailed analysis of the historical EV ownership and use from the year 2000 to 2021 under the Puget Sound region, covering aspects of household demographics and travel pattern differences between EV-households and non-EV-households, as well as the trend identification and quantification of multiple factors in deciding EV adoption and use. Following evidence from the analysis a modeling framework was developed and experimented for future year EV growth prediction at both macroscale and microscale levels, the macroscale model forecasts the total number of EV sales in specific future years based on EV technology development, EV market production and supply, the existence of supporting policy and rebates program, as well as external environmental factors, then the microscale model identifies the candidate EV-purchasing households through the measurement of similarities between EV-households and non-EV-households, with the dynamics of the factor influence reflected in the rescaling of the weights while calculating the similarity. Following the model outputs multiple scenarios regarding technology, policy and market were designed and proposed for model sensitivity analysis, specifically, how are the outputs affected based on changes in different input components. The proposed research methodology will supplement the existing studies on EV expansion and penetration over time, and will specifically account for parameter-driven preference dynamics. The analysis results will provide substantial details on the identification of influential factors on EV adoption, while prediction results will also provide substantial research findings on understanding the future EV market and possible impacts and turbulences.
