Public charging facility requirements for long-haul trucks in the EU: a trip chain approach

Abstract of the research: This research presents a trip chain-based model that evaluates the LBET's charging requirements in the year 2030 for the European continent. Following EU truck driver regulations, the research converts a four-step origin-destination (OD) matrix into LHT trip chains. We show that in our main case scenario of 15% LBET share, a minimum of 28800 slow (≤ 100 kW) and 9800 fast (≥ 1 mW) charging points are required to meet that energy demand, corresponding to daily energy requirements of 112 GWh. On average, the slow to fast charging points ratio is 3. Fast and slow charging points serve 12 and 2 LBETs daily, respectively. Our model suggests that it will be necessary to place charging stations every 25-35 km on highways where demand for charging is required. The methodology: We develop a method for placement of 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 LHT is identified using the ETISplus dataset. We develop a travel pattern for the long haul truck (LHT) 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 LHT. Break and rest locations for all moving LHTs are aggregated to suggest energy requirements if assuming these LHTs are BETs. The aggregated energy to charge stopped LBETs is used to identify the number and type of chargers within each suggested charging station. Datasets 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 infromation about the locations of suggested charging stations, number and type of chargers, and 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 '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): Name Description Data Type Unit 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_ region National name of NUTS-3 destination region String - Edge_path_E_road List of the network edge IDs of the shortest path between the O-D pair, determined with Dijkstra's algorithm String - Distance_from_origin_ region_to_E_road Distance from the geometric centre of the origin region to the closest network node Float Kilometres [km] Distance_within_E_ road Distance of the shortest edge path between the O-D pair Float Kilometres [km] Distance_from_E_ road_to_destination_ region Distance from the geometric centre of the destination region to the closest network node Float Kilometres [km] Total_distance Sum of Distance_from_origin_region_to_E_road, Distance_within_E_road and Distance_from_E_road_to_destination_region Float Kilometres [km] Traffic_flow_trucks_ 2010 Number of trucks that drive between the O-D pair in 2010 Float Number of trucks Traffic_flow_trucks_ 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_ 2030 Number of trucks that drive between the O-D pair according to the forecast for 2030 Float Number of trucks Traffic_flow_tons_ 2010 Number of tons that are transported between the O-D pair in 2010 according to ETISplus Integer Tons [t] Traffic_flow_tons_ 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_ 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)) Name Description Data Type Unit 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 (04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) Name Description Data Type Unit Network_Edge_ID Unique edge ID Integer (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