ROUTE
The geographic aspects of movement (of a vehicle) from an origin to a destination location in a road network.
TRANSPORT ROUTE / TRIP
Transport of goods or people with a vehicle along a route. Note that this is a somewhat unconventional terminology that emphasizes the geographic aspects of the movement along the route but refers to a specific transport trip where a specific vehicle transports a specific set of goods or people along the route.
TRANSPORT WEIGHT
The combined weight (mass) of the goods/people and the vehicle of a transport route.
TRANSPORT WORK
Transport weight times the length of the transport route. It is measured in ton-km (tkm).
TRANSPORT ELECTRIFICATION SCENARIO ASSUMPTIONS
The transport electrification scenario consists of an electric vehicle-, an energy use-, and a charging (behavior) model. Optionally, the scenario can include an initial network of existing or planned stations of the customer and / or competitors. To specify a transport electrification scenario, one must make assumptions about these components of the scenario.
ELECTRIC VEHICLE
An electric vehicle is specified by its battery size, its energy use, and its maximum charging power.
ENERGY USE / CONSUMPTION
The amount of energy that a vehicle propulsion system needs to move a unit of transport weight over a unit distance. Energy use depends on many factors that include the energy efficiency of the propulsion system, the transport weight, the vehicle speed and acceleration, the road incline or road elevation profile and the road surface type, the aerodynamic drag coefficient or form of the vehicle, and its cargo, and the weather.
However, for long-term charging infrastructure planning purposes for typical electric heavy-duty trucks (e.g. Scania or Volvo) and heavy freight transports, the average energy use is approximately 0.035 kWh/tkm.
In mountainous geographies, very cold or hot climates, urban or heavy traffic areas with more stop-and-go traffic or distribution transport with several stops along the route the average energy use can be 10 to 30 percent higher.
The average energy use is also slightly higher for transports with lower combined weight. In the extreme case the private cars, the energy use can be as high as 0.4 kWh/tkm.
CHARGING BEHAVIOR
The charging model describes when, where and how much vehicles charge. The Gordian charging model assumes a certain level of initial State of Charge (SoC) primarily from depot charging but potentially also from prior transport trips. The charging amount at stations is limited by the battery size, the arrival SoC, the maximum rest-stop duration, the charging power, and the energy that is needed to operate the route. The charging model allows two options: vehicles either stop to charge at all stations along their routes or only when their SoC is below a threshold.
CO2 EMISSION
The amount of CO2 that is emitted by the vehicle. CO2 emissions depend on the fuel type and engine, but according to this report by Transport and Environment, the average CO2 emissions of a modern heavy duty diesel truck is about 50g/tkm or 1.43kg/kWh. More accurate CO2 emissions can be obtained based on more detailed vehicle models and transport assignments by tools like NTMCalc Basic 4.0 by Network for Transport Measures.
ROUTE CATCHMENT
The route catchment of an existing / planned charging infrastructure location is the set of transport route parts where the energy used along the transport routes towards the location could be replenished at the location according to the transport electrification scenario. See this post for an illustration of the SoC of a vehicle as it uses energy along a transport route.
DEMAND IN ISOLATION
The energy that vehicles would charge at the location if the network consisted of one station at the location according to the transport electrification scenario.
DEMAND IN NETWORK
The expected maximum energy that vehicles would charge at the location accounts for the demand lost to other stations in the network according to the transport electrification scenario.
This is a maximum value assuming that all transport routes can be operated in fully electric mode. In particular, it is assumed that even vehicles that cannot complete their transport routes in fully electric mode from start to finish with a positive SoC on the analyzed network of stations will stop and charge according to the transport electrification scenario as they pass the station at the analyzed location.
This means that it is assumed that there exist other stations outside of the network that also enable these vehicles to complete their transport routes. In a sense, this represents an optimistic demand estimate in that competing stations outside of the network do not inflict demand loss on the location being analyzed but enable the full electric operations of transport routes that, otherwise, on the analyzed network according to the transport electrification scenario, would not be possible. For more information and an illustration, see the post Route based network impacts on charging demand.
DEMAND FROM FULLY ELECTRIC
The expected energy that that vehicles would charge at the location accounts for the demand lost to other stations in the network in the given transport electrification scenario. In contrast to ‘DEMAND IN NETWORK, this value only includes the demand from transport routes that can be operated in fully electric mode from start to finish on the analyzed network according to the transport electrification scenario. In a sense, this represents a more realistic demand estimate in that the demand is only from transport routes that the analyzed network enables the electric operations of according to the transport electrification scenario. If not all the stations are part of the analyzed network, then the actual demand at the location will be lower because, more often than not, additional stations (and an increase in charging supply) reduce the demand for the stations in a network. For more information and an illustration, see the post Route based network impacts on charging demand.
TOTAL NETWORK DEMAND
Sum of the ‘DEMAND IN NETWORK’ values of all the stations of the network.
LOSSES DUE TO CANIBALIZATION
The amount of demand that is lost at a station due to the presence (i.e., charging supply) of other stations in the network according to the transport electrification scenario. For a location it is calculated as the difference between ‘DEMAND IN ISOLATION’ and the ‘DEMAND IN NETOWORK’. Typically for strategic placement of cooperating stations of a network one tries to minimize this value while considering other criteria and objectives.
LOSSES INFLICTED VIA CANIBALIZATION
The difference between the ‘TOTAL NETWORK DEMAND’ without- and with the station at the analyzed location according to the electrification scenario. Typically for strategic placement of cooperating stations of a network one tries to minimize this value while considering other criteria and objectives.
CO2 DISPLACEMENT FROM CHARGING AT A LOCATION
The amount of CO2 that would have been emitted if the transport work that was electrified by a station at the location would have been performed with diesel trucks. It is calculated by converting ‘DEMAND IN NETWORK’ (kWh) to CO2 using the conversion 1.43kg CO2 per kWh.
INCREASE IN NUMBER OF ELECTRIC ROUTES BY ADDING THIS LOCATION TO THE NETWORK
The number of transport routes that become possible to operate in fully electric mode by the addition of a station at the analyzed location according to the transport electrification scenario.
INCREASE IN TON-KILOMETER (TKM) OF ELECTRIFIED ROUTES BY ADDING THIS LOCATION TO THE NETWORK
The transport work in tkm of the transport routes that become possible to operate in fully electric mode by the addition of a station at the analyzed location according to the transport electrification scenario
PREVALENCE OF SHORT AND LONG TRUCK STOPPINGS (ACEA)
The frequency of different types of short and long truck stoppings for regional and long-haul trucks in the close proximity of the location. The truck stoppings (stop locations with their frequency and duration) and their classification has been derived by Fraunhofer on behalf of the European Automobile Manufacturers Association (Association des Constructeurs Européens d’Automobiles – ACEA).
Truck stoppings were extracted and characterized by analyzing from the GPS logs of trucks from seven truck OEMs in Europe the stops of 170,000 trucks in regional operations (defined as operations where 90% of truck locations are within 200 km of the truck’s home base) and 230,000 trucks in non-regional / long-haul operations.
The rational for the analysis and in importance of this data set for charging infrastructure planning is that ideally due to the prolonged “refueling” times the charging of battery electric trucks should utilize these truck stoppings to minimize the negative impacts logistics operations. A detailed report of the ACEA truck stopping analysis can be found here.