MODELLING OPERATIONAL ARCHETYPES FOR CORPORATE FLEET ELECTRIFICATION

Abstract

The decarbonisation of corporate vehicle fleets is a central challenge in achieving Europe’s climate neutrality targets under the European Green Deal. Although corporate vehicles constitute only a share of the total fleet, they account for a disproportionate fraction of total mileage and associated CO₂ emissions. Despite fiscal incentives and regulatory support, the adoption of battery electric vehicles (BEVs) within the corporate sector remains significantly below private uptake, primarily due to uncertainty about operational feasibility and charging constraints. This study presents a data-driven framework for assessing fleet electrification potential based on empirical driving data and simulation-based modelling. Using vehicle usage records from the carmonitor.eu telematics platform, the research identifies four distinct operational archetypes within corporate fleets, differentiated by travel intensity, trip fragmentation, and temporal driving structure. These archetypes are derived through a clustering methodology employing standardised behavioural indicators, principal component analysis (PCA), and k-means segmentation, validated by silhouette and Davies–Bouldin indices. Results demonstrate pronounced heterogeneity in fleet operation, with daily driving distances, trip frequency, and vehicle availability varying substantially across clusters. Scenario-based modelling reveals that electrification feasibility depends not only on total mileage but also on temporal driving regularity and charging opportunity windows. Vehicles with predictable daily cycles and long overnight parking are found to be most suitable for early electrification, while high-mileage or irregular-use vehicles require access to fast-charging infrastructure and larger battery capacities. The study concludes that segmenting corporate fleets by operational archetype provides a robust analytical foundation for transition planning, enabling tailored recommendations for vehicle selection, charging infrastructure, and total cost of ownership optimisation. By linking empirical usage data with simulation and scenario modelling, the paper contributes a replicable methodological approach for evidence-based fleet decarbonisation strategies across Europe.