System Dynamics Modeling of Global Electric Vehicle Adoption: Market Transition Time Toward a Single-Dominant Transport Technology

This study presents a system dynamics framework to estimate the global transition time toward electric vehicle (EV) dominance. The model, adapted from the 'Growth of a Field' archetype, captures the mutual reinforcement between EV adoption, charging infrastructure deployment, and cost reductions via learning curves. By solving a system of differential equations in Python, we simulate the nonlinear feedbacks that drive technological diffusion within a finite market. The model explicitly represents the dynamics of the EV fleet, charging infrastructure stock, and cumulative production, where adoption is influenced by infrastructure availability and declining battery costs. Sensitivity analysis reveals how variations in the base adoption rate—representing early policy and behavioral factors—affect tipping points. For instance, doubling the initial adoption propensity reduces the time to 50% market penetration from 30 to 20 years. Monte Carlo simulations are incorporated to assess probabilistic forecasts and the robustness of transition timelines under uncertainty. The results highlight infrastructure deployment as a critical bottleneck and quantify the leverage of early incentives. This framework provides a transparent, extensible tool for strategic planning in the automotive and energy sectors.