A Probabilistic Capital Cost Modeling Framework for Reducing Financial Uncertainty in EV Charging Infrastructure Deployment

Abstract: Accurate estimate of the capital cost of electric vehicle (EV) charging infrastructure remains a major challenge for, developers, and public agencies pursuing low-carbon transportation goals. Existing cost estimates vary widely across studies due to differences in project conditions, site characteristics, electrical system requirements, and methodological assumptions. These inconsistencies create significant financial risk and impede timely investment decisions, particularly for Level 2 and DC fast charging projects. This study proposes a probabilistic, component-based capital cost model that allows users to configure only the infrastructure elements relevant to their project and generate realistic, uncertainty-aware cost estimates. The study answers this question: How can a probabilistic, configurable capital cost model reduce financial uncertainty in EV charging infrastructure planning? The conceptual framework organizes capital cost drivers into four interacting components equipment costs, installation and labor, civil work and permitting, and electrical infrastructure upgrades such as switchgear, transformers, conductors, and conduits. Each component is parameterized with probability distributions based on empirical data, industry estimates, or expert judgment. Monte Carlo Simulation is used to propagate uncertainty through the model by generating thousands of capital cost outcomes. This approach replaces traditional single-value estimates with probabilistic outputs, such as confidence intervals, cost-risk profiles, and sensitivity rankings that identify high-impact cost drivers. Expected outcomes include a transparent and reproducible capital cost estimation framework, a reduction in investor and agency financial risk through uncertainty quantification, and a configurable tool that improves evidence-based decision-making for sustainable EV infrastructure deployment.