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direction. Similar to most residential roads, there was a single lane in each direction.
The desired speed of the vehicles had a distribution that varied between 22 and 28
MPH (36 and 46 km/h), based on the assumption that the median desired speed would
be the speed limit, 25 MPH (41 km/h).
Deceleration into and launch from each stop sign was largely under the control of
the driver model, not constrained by the vehicle ahead. There was a dwell time at each
sign in which vehicle speed dropped to zero followed by a launch. The length of the
dwell was based on cross traffic and the driver characteristics of each individual car.
The scenarios were created using a road model with varying external conditions.
They were selected to explore the scenario space to determine which conditions were
significant factors for energy consumption. The factors used were as follows:
•
Road characteristics
•
Road Gradient
•
Number of lanes
•
Traffic characteristics
•
Vehicle flow rate
•
Vehicle mix (Number of trucks, buses, cars and battery electric vehicles)
•
Driver characteristics
•
Desired speed
•
Use of cruise control
•
Accessory load per unit time
Two types of energy consumption were considered in this analysis; propulsive energy
consumption and accessory energy consumption. Propulsive loads were computed
using maps of energy per distance travelled. Accessory energy consumption was in
units of energy per unit time and kept constant through any given scenario.
The independent variables for the energy maps were vehicle speed and accelera-
tion. Four maps were made for the BEV vehicle for different payloads weights; 1-4
occupants. The acceleration used in the energy calculation was the sum of road gra-
dient acceleration and vehicle acceleration.
We determined both experimentally and using the Student-T analysis that 125
BEV test vehicles were necessary to get sufficient statistical power. So each scenario
was run until 125 BEV had passed through the scenario. For each BEV the energy
consumption was computed for each time step, multiplied by the distance of each
time step and accumulated for the entire drive cycle. This was added to the energy
consumption attributable to the accessory loads and saved. The average and standard
deviation of all the drive cycles were then computed for each scenario, and the scena-
rios were plotted and compared to determine the main effects.
3
Results of the Energy Consumption Modeling
The results are presented in the following manner. First the results of energy con-
sumption under different scenarios for each of the road types are presented followed
by a comparison of these results across road types. The tables give the mean energy
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