DOI: 10.5281/zenodo.7858303
title: Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach

description: Contact details: wasim.shoman at chalmers.se waahh7 at gmail com <strong>Abstract of the research:</strong> Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> 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 km<sup>2</sup> 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. <strong>The methodology:</strong> 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. <strong>Datasets details</strong> 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. It is a shapefile with the following details for its fields: Name Description Data Type Unit DTN30/MainDTN number of electrified trucks in 2030 integer number ChE30 charged energy in Mega watt-hour from all charging (fast and slow) float Mega watt-hour ChERM charged energy in Megawatt hour with slow charging only (rest) float Mega watt-hour MDTN_R number of electrified trucks using slow chargers (rest) integer number ChEBM charged energy in Megawatt hour with fast charging only (break) float Mega watt-hour MDTN_B number of electrified trucks using fast chargers (break) integer number NSCh2pD number of slow chargers integer number NFCh30m number of fast chargers integer number TotCha Total number of chargers integer number The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. <strong>Name</strong> <strong>Description</strong> <strong>Data Type</strong> <strong>Unit</strong> ID_origin_region Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 Integer (9digits) - Name_origin_region National name of NUTS-3 region of origin String - ID_destination_region Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 Integer (9digits) - Name_destination_<br> region National name of NUTS-3 destination region String - Rest A value of ”1” indicates a rest stop Boolean - Break A value of ”1” indicates a break stop Boolean - ChaDisKM Charged range within a trip for stopped the truck Float km ChaEnekWh Charged energy within a trip for stopped the truck Float KWh MainDTN Number of stopped trucks for the main electrification scenario (15%) Float number ChE30M Charged energy for all stopped trucks Float MWh ChERM Charged energy for the trucks stopping for rest Float MWh MDTN_R Number of trucks stopping for rest Float number ChEBM Charged energy for the trucks stopping for break Float MWh MDTN_B Number of trucks stopping for break Float number geometry X, Y coordinates geometry - The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X): <strong>Name</strong> <strong>Description</strong> <strong>Data Type</strong> <strong>Unit</strong> ID_origin_region Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 Integer (9digits) - Name_origin_region National name of NUTS-3 region of origin String - ID_destination_region Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 Integer (9digits) - Name_destination_<br> region National name of NUTS-3 destination region String - Edge_path_E_road List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm String - Distance_from_origin_<br> region_to_E_road Distance from the geometric centre of the origin region to the closest network node Float Kilometres [km] Distance_within_E_<br> road Distance of the shortest edge path between the O-D pair Float Kilometres [km] Distance_from_E_<br> road_to_destination_<br> region Distance from the geometric centre of the destination region to the closest network node Float Kilometres [km] Total_distance Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em> Float Kilometres [km] Traffic_flow_trucks_<br> 2010 Number of trucks that drive between the O-D pair in 2010 Float Number of trucks Traffic_flow_trucks_<br> 2019 Number of trucks that drive between the O-D pair after they had been scaled to 2019 Float Number of trucks Traffic_flow_trucks_<br> 2030 Number of trucks that drive between the O-D pair according to the forecast for 2030 Float Number of trucks Traffic_flow_tons_<br> 2010 Number of tons that are transported between the O-D pair in 2010 according to ETISplus Integer Tons [t] Traffic_flow_tons_<br> 2019 Number of tons that are transported between the O-D pair after they had been scaled to 2019 Integer Tons [t] Traffic_flow_tons_<br> 2030 Number of tons that are transported between the O-D pair according to the forecast for 2030 Integer Tons [t] Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <strong>Name</strong> <strong>Description</strong> <strong>Data Type</strong> <strong>Unit</strong> Network_Node_ID Unique network node ID Integer (6 digits) - Network_Node_X Longitude of the location of network node Float Degrees Network_Node_Y Latitude of the location of network node Float Degrees ETISplus_Zone_ID ID of the NUTS-3 region in which the network node is located Integer - Country Unique country code of the country in which the network node is located (country codes are defined by ETISplus) String - Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <strong>Name</strong> <strong>Description</strong> <strong>Data Type</strong> <strong>Unit</strong> Network_Edge_ID Unique edge ID Integer<br> (7 digits) - Manually_Added Determines whether an edge had been manually added to the network (1) or not (0) Binary-integer - Distance Length of the network edge Float Kilometres [km] Network_Node_A_ID Unique ID of the network node that defines one end point of the network edge Integer - Network_Node_B_ID Unique ID of the network node that defines one end point of the network edge Integer - Traffic_flow_trucks_2019 Number of trucks that drive on the edge in 2019 (both highway directions combined) Float Number of trucks Traffic_flow_trucks_2030 Number of trucks that drive on the edge in 2030 (both highway directions combined) Float Number of trucks
description: {"references": ["https://www.sciencedirect.com/science/article/pii/S235234092101060X"]}