Spatio-temporal model results for battery-electric truck charging demand in Europe

Description

Methods (English)

VESUVIO combines four modelling stages: market diffusion and fleet activity, depot-based charging, long-haul en-route charging and aggregation to a regular H3 grid. Battery-electric truck stocks and vehicle-kilometres travelled are combined with segment-specific energy use and charging efficiency. Regional trucks are allocated to probable depot locations using freight activity and harmonised depot-size indicators. Long-haul en-route demand is derived from synthetic European origin-destination flows, highway routing, vehicle range and charging around mandatory rest opportunities. Depot and en-route charging profiles are generated separately at 15-minute resolution, scaled to weekly energy and summed within each hexagon.

The three scenarios represent uncertainty in BET market uptake, technical development, battery capacity, energy consumption, charging efficiency and operational feasibility. All scenarios use the same freight-system representation and spatial allocation methodology. Scenario differences therefore mainly change the magnitude and timing of demand, while the broad geography of hotspots remains comparatively stable.

Detailed methodological information, parameter assumptions, distribution fitting, null-model comparisons and sensitivity analyses are provided in the associated manuscript and supplementary material. The model results should be interpreted as scenario-based prospective estimates, not observations or exact site-level forecasts.

Table of contents (English)

The release consists of a detailed spatial layer and a compact country-level summary. Each detailed record represents a country-specific H3 cell for one model year and diffusion scenario and contains the corresponding charging-energy and peak-power outputs. The KPI file contains one row for each scenario, year and country combination. Its fields are grouped below.

• scenario, Year, country: diffusion scenario, model year and ISO alpha-2 country code.
• e_sumWeekly_sum_MWh: total modelled weekly charging energy across all active hexagons in the country.
• e_sumWeekly_min_MWh, e_sumWeekly_q5_MWh, e_sumWeekly_mean_MWh, e_sumWeekly_std_MWh, e_sumWeekly_med_MWh, e_sumWeekly_q95_MWh, e_sumWeekly_max_MWh: minimum, 5th percentile, mean, standard deviation, median, 95th percentile and maximum of weekly charging energy across active hexagons, in MWh per week.
• p_max_min_MW, p_max_q5_MW, p_max_mean_MW, p_max_std_MW, p_max_med_MW, p_max_q95_MW, p_max_max_MW: corresponding distributional statistics for the maximum simultaneous charging load of each active hexagon, in MW.
• share_REG_mean, share_REG_std, share_REG_med: mean, standard deviation and median of the hexagon-level share of charging energy assigned to regional-truck depot charging.
• share_LH_home_mean, share_LH_home_std, share_LH_home_med: corresponding statistics for long-haul charging at the home depot.
• share_LH_route_mean, share_LH_route_std, share_LH_route_med: corresponding statistics for long-haul en-route charging.
• num_hex: number of country-specific H3 cells in the modelled spatial set.
• num_actHexagons: number of active cells with non-zero modelled BET charging demand.
• coverage_hex: spatial coverage indicator for the country-specific hexagon set.
• coverage_actHex: coverage indicator for active demand cells.
• shareEnergy_hexTop1%, shareEnergy_hexTop5%, shareEnergy_hexTop10%: shares of national weekly charging energy captured by the highest-demand 1%, 5% and 10% of active hexagons, respectively.

Technical info (English)

Geographic and temporal coverage

The model covers the EU-27 plus Norway, Switzerland and the United Kingdom. Cyprus and Malta are not included because of negligible long-haul HDV freight volumes and insufficient depot and corridor data coverage. Outputs are available for 2030, 2035, 2040, 2045 and 2050 under pessimistic, medium and optimistic diffusion scenarios. The principal spatial unit is an H3 resolution-5 cell of approximately 250 km². A cell may appear in two national datasets if it contains charging-demand points on both sides of a national border; this preserves each country’s territorial demand without introducing artificial discontinuities.

Limitations and reuse notes

• The results are model outputs and depend on assumptions about market diffusion, vehicle technology, freight activity, charging behaviour and infrastructure availability.
• Charging profiles are simulation-based and do not fully represent regional operational heterogeneity, seasonal extremes or all forms of managed charging.
• Depot-location data may underrepresent smaller facilities. H3 aggregation reduces some location uncertainty but also hides variation within individual cells.
• The model estimates charging load at the grid connection, including charging losses, but does not model available local grid headroom, feeder topology, competing electricity demand or the capacity of individual connections.
• Hexagon-level peak power is the simultaneous aggregate of all modelled charging locations within a cell. It is not the required capacity of a single charging site.
• Concentration metrics depend on spatial resolution and use active hexagons as the denominator. They should not be interpreted as shares of national land area.
• Country-level fields labelled REG, LH_home and LH_route summarise hexagon-level shares. Users seeking aggregate national charging shares should calculate energy-weighted values from the detailed results rather than average these fields.
• The supplied KPI file currently mixes comma and point decimal notation. Values must be parsed carefully, and the file should preferably be normalised before publication.