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Fuel Consumption Estimation and Analysis of the University of Alberta Fleet Vehicles
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- Author / Creator
- Ansari, Amir
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This thesis aims to create a platform to estimate and monitor the University of Alberta
(UAlberta) fleet vehicles’ fuel consumption and Carbon Dioxide (CO2) emissions. The
main objective is to collect and analyze fleet vehicles information to reduce energy
consumption and greenhouse gas emissions from university vehicles. To this end, this
thesis creates a data collection platform for real-time monitoring and analysis of fleet
activity, utilizing onboard diagnostics (OBD) data from each vehicle. By processing
the collected data, this thesis seeks to identify the causes of high fuel consumption in
the fleet and determine the optimal vehicle type for different applications and driving
cycles.
Two machine learning methods, including random forest (RF) and artificial neu-
ral network (ANN), were investigated to estimate fuel consumption based on OBD
and actual fuel consumption data. The study used data from a Ford Escape plug-in
hybrid electric vehicle (PHEV) and a Ford F-350 vehicle during real-world urban and
highway driving on a 100-km route. The machine learning models utilized OBD pa-
rameters such as engine load, engine speed, intake manifold absolute pressure, air-fuel
equivalence ratio, and throttle position. The validation results indicated that the RF
model was more accurate than the ANN model, achieving an estimation accuracy
of 99% for the Ford Escape PHEV and 100% for the Ford F-350. These findings
confirm that utilizing machine learning models can effectively estimate vehicular fuel
consumption; thus, these models can be used to monitor fleet vehicles’ energy con-
sumption, and design strategies to reduce the fuel consumption from the UAlberta
fleet vehicles.
Additionally, this thesis investigated the energy consumption and cost of a con-
ventional vehicle (Ford Escape S) with an internal combustion engine (ICE) and a
PHEV (Ford Escape PHEV) from the UAlberta fleet. The vehicles were driven 243
times on a 20-km route in Edmonton, Canada, during 2021 - 2022. The route in-
cluded both urban and highway areas. The research also explored the impact of
ambient temperature (Tamb) on the operations and energy consumption of the vehi-
cles, considering different powertrains and electrification levels. This study reveals
that for warm start tests, the total energy consumption increased by decreasing the
Tamb from 32 °C to -24 °C. Modes that entail continuous operation of the electric
motor are especially affected. Among the modes, Auto EV (i.e., electric and hybrid
electric) mode demonstrated the highest increase in energy consumption, rising by
almost 452% when the Tamb drops from 29°C to -24°C. Similarly, during cold start
tests, there was an increase in energy consumption as the Tamb decreased from 29 °C
to -18 °C. The mode that showed the highest increase in energy consumption was EV
Now (i.e., all-electric) mode, with an increase of 527% by reducing the Tamb from 29
°C to -13 °C.
The thesis also examined the effect of start-stop technology on conventional vehi-
cles’ energy consumption and operational costs. To conduct the study, three vehicles
from the UAlberta fleet were tested, and the effect of start-stop technology was eval-
uated on four different applications of UAlberta fleet vehicles. The findings indicated
that the energy and cost savings achieved by vehicles equipped with start-stop tech-
nology could be significant, depending on the vehicle drive cycle and idling percentage,
as well as engine size. The fuel savings are anticipated to increase during the cold
season operation of fleet vehicles. -
- Graduation date
- Spring 2024
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.