Data sets

Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach

Wasim Shoman; Sonia Yeh; Frances Sprei; Patrick Plötz; Daniel Speth

Abstract of the research:

Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO2 emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km2 square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.

Methodology:

We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.

Data set details:

The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. 

The first dataset titled ‘ChargerLocations’ contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030.


Synthetic European road freight transport flow data based on ETISplus

Daniel Speth, Verena Sauter, Patrick Plötz, Tim Singer

Abstract of the research:

This dataset describes estimated European truck traffic flows between 1,675 regions all over Europe and is based on the publicly available ETISplus project from 2010 (DOI: 10.13140/RG.2.2.16768.25605). The project collected Europe-wide freight volumes and calibrated the resulting origin-destination matrices with real world traffic flows. For the current dataset, the truck results of the ETISplus project were updated using current Eurostat data (https://ec.europa.eu/eurostat/web/transport/data/database). Additionally, a forecast was added for 2030. Using Dijkstra’s algorithm, the freight flows were finally transferred to the European highway network. Therefore, the dataset provides a synthetically generated truck traffic volume for each road section. The dataset can be a basis for developing, planning and sizing future road infrastructure, such as charging infrastructure for electric trucks. The dataset consists of four files: 01_Trucktrafficflow, 02_NUTS-3-Regions, 03_network-nodes, 04_network-edges. All of them are stored as comma separated values with commas as column separators and dots as decimal separators. The main dataset 01_Trucktrafficflow describes 1,514,573 directed transport flows in fifteen columns: (1) ID origin region, (2) name origin region, (3) ID destination region, (4) name destination region, (5) shortest path in the modeled E-road network, (6) distance from origin region to the E-road network, (7) distance within the E-road network, (8) distance from the E-road network to the destination region, (9) total distance, (10) road freight flow in tons for 2010, (11) road freight flow in tons for 2019, (12) road freight flow in tons for 2030, (13) truck traffic flow in number of vehicles for 2010, (14) truck traffic flow in number of vehicles for 2019, (15) truck traffic flow in number of vehicles for 2030. 02_NUTS-3-Regions contains a list with the regions under investigation. 03_network-nodes and 04_network-edges illustrate the highway network. The first contains the following information on each network node as columns: (1) node ID, (2) longitude of the location, (3) latitude of the location, (4) ID of the corresponding NUTS-3 region, (5) country code. The second contains information on the edges: (1) edge ID, (2) information whether the edge is manually added or part of the original ETISplus dataset, (3) length of the edge, (4) ID endpoint A, (5) ID endpoint B, (6) number of trucks in 2019 (both directions), (7) number of trucks in 2030 (both directions).