Adopting EVs: A Data-Driven Simulation of Real-World User Mobility

The transition to electric vehicles (EVs) is a critical step in sustainable urban mobility, addressing environmental concerns such as carbon emissions and air pollution. Despite rapid EV market growth, challenges like costs, range limitations, and insufficient charging infrastructure persist. This thesis uses real-world driving data from 1,000 insurance customers to assess EV adoption feasibility, focusing on user-specific trip patterns. A custom-designed simulator forms the core of the study, evaluating EV feasibility by processing inputs: trip data, user parameters (e.g., anxiety thresholds, minimum parking durations), vehicle parameters (e.g., battery capacity, consumption per road type, maximum charging power), and grid parameters (e.g., AC and DC charging powers). The simulator replicates trips through two main steps: Simulating the Trip: Calculates energy consumption, checks SoC sufficiency, and flags trips with low or insufficient SoC. Charging Process: Manages charging events during parking intervals to replenish energy. Key outputs include SoC before and after trips, trip energy consumption, energy gained during parking, and metrics like feasible trip percentages, distances under anxiety, and days with anxiety or unsatisfied trips, offering detailed insights into EV adoption for varying user behaviors and vehicle specifications. The study applies the simulator in three phases, each increasing in complexity: Abstract Scenarios: Establishes baseline feasibility using generalized EV characteristics. Behavioral Variations: Models nine user profiles with diverse charging preferences such as time-of-day effects, weekday variations, and state-of-charge thresholds. Real Market Vehicles: Incorporates specifications of 50 EV models, including vehicle prices, to deliver realistic, user-focused results and cost analysis. A two-level cost analysis enriches the findings: Aggregated Cost Analysis: Evaluates daily charging costs and costs per kilometer under different scenarios, considering both home and public street charging with price bounds to reflect variations. Monthly Cost Tracking: Examines how costs evolve over time, comparing them with other performance metrics to identify user behaviors. Key findings show that larger battery capacities and faster charging reduce range anxiety and improve trip satisfaction. However, among slow AC charging options, higher power rates (e.g., 11 kW vs. 22 kW) have little impact during overnight charging, as extended charging times compensate for lower power output. Notably, based on the sample analyzed in this study, even with slow AC charging, a casual driver with 28% utilization can achieve over 70% feasibility using the most affordable EV (Dacia Spring Electric 45). Drivers dependent on public charging or with irregular travel patterns require targeted infrastructure enhancements. While home charging remains the most cost-effective, public DC fast charging offers flexibility for long-distance travel. Behavioral trends and performance metrics tracked over time further highlight adoption challenges and opportunities. In conclusion, tailored charging strategies are vital for diverse user needs and optimal EV adoption. EV feasibility heavily depends on user travel patterns; in some cases, behavioral adjustments are necessary for effective transitions. By offering actionable insights, this study supports sustainable urban mobility and provides guidance for policymakers, EV manufacturers, and infrastructure developers.