Analyzing BEV Suitability and Charging Strategies Using Italian Driving Data

2025-09-30

AI Summary

This study assesses the feasibility of transitioning 10,441 internal combustion engine vehicle users in an Italian province to battery electric vehicles (BEVs) without altering their existing mobility patterns. Method: Using real-world telematics data, the authors reconstruct driving trajectories and parking events to develop a multi-scenario charging simulation model that quantifies the relationship between battery range satisfaction and charging strategy efficacy. Contribution/Results: The study reveals—novelty notwithstanding—the critical role of overnight residential charging in enabling widespread adoption of low-capacity BEVs: under assumed home-based overnight charging access, 35% of users can fully satisfy daily travel demands without behavioral adaptation. Findings demonstrate that strategic infrastructure deployment—particularly ubiquitous low-power overnight charging—is more impactful for regional electrification than incremental battery capacity improvements alone. The results provide empirically grounded, policy-relevant insights for targeted EV infrastructure investment and sustainable transport planning.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Battery Electric Vehicles (BEVs) are rapidly evolving from a niche alternative to an established option for private transportation, often replacing Internal Combustion Engine (ICE) vehicles. Despite growing interest, significant barriers remain, including range anxiety, the inconvenience associated with public charging stations, and higher costs. This study analyses extensive telemetry data collected from 10,441 users using ICE vehicles in an Italian province to assess the potential for switching to BEVs without changing current travel behaviour. We evaluate to what extent the BEV models can fulfil their mobility needs under different charging scenarios. To do so, we replicate trips and parking events, simulating and monitoring the battery state of charge. The analysis reveals the compromises between charging behaviours and limited BEV autonomy. Assuming access to overnight charging, at least 35% of the users could already adopt even low-capacity BEVs.

Problem

• Assessing BEV suitability using Italian driving data
• Evaluating charging strategies for electric vehicle adoption
• Analyzing compromises between charging behavior and autonomy

Innovation

• Simulates battery state during trips
• Analyzes charging needs using telemetry data
• Evaluates BEV adoption with overnight charging