Quantitatively assessing readiness of Electric Vehicle charging infrastructure

Electric Vehicles (EV) are growing in popularity, affordability, and battery range. The United States will need massive investment in charging infrastructure to meet the demand that is forecasted, an expensive and complex endeavor. The current tools available to policy makers and planners are often computationally intensive and micro-level focused. To effectively orchestrate system-wide planning, agencies will need analysis tools and frameworks that can help guide infrastructure planning, investment, and system development. This thesis develops and demonstrates an accessible, straightforward readiness index and analysis framework to quantitatively assess the readiness of electric vehicle charging infrastructure given charging demand. The index was applied to zip codes across the United States to evaluate the readiness of their EV charging given projected charging demand in 2030. Results showed that readiness varies greatly across socio-demographic indicators such as income, race, education, and age. This work demonstrates that it is possible to create analysis tools to provide insights that can be tailored to meet planning objectives, such as geographic scale, level of granularity, and time horizons.