DOI: 10.5281/zenodo.7215614
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


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.


The 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.


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 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. 



	
		
			
			
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
			
			
			
-
			
		
		
			
			
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):



	
		
			
			
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 (Updated_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