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

Contact details:

wasim.shoman at chalmers.se 

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 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 (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