Fuel Consumption Estimation and Analysis of the University of Alberta Fleet Vehicles
Creators: Ansari, Amir
Publisher: name: University of Alberta Library
Year: 2024

<p>This thesis aims to create a platform to estimate and monitor the University of Alberta<br>
(UAlberta) fleet vehicles’ fuel consumption and Carbon Dioxide (CO2) emissions. The<br>
main objective is to collect and analyze fleet vehicles information to reduce energy<br>
consumption and greenhouse gas emissions from university vehicles. To this end, this<br>
thesis creates a data collection platform for real-time monitoring and analysis of fleet<br>
activity, utilizing onboard diagnostics (OBD) data from each vehicle. By processing<br>
the collected data, this thesis seeks to identify the causes of high fuel consumption in<br>
the fleet and determine the optimal vehicle type for different applications and driving<br>
cycles.<br>
Two machine learning methods, including random forest (RF) and artificial neu-<br>
ral network (ANN), were investigated to estimate fuel consumption based on OBD<br>
and actual fuel consumption data. The study used data from a Ford Escape plug-in<br>
hybrid electric vehicle (PHEV) and a Ford F-350 vehicle during real-world urban and<br>
highway driving on a 100-km route. The machine learning models utilized OBD pa-<br>
rameters such as engine load, engine speed, intake manifold absolute pressure, air-fuel<br>
equivalence ratio, and throttle position. The validation results indicated that the RF<br>
model was more accurate than the ANN model, achieving an estimation accuracy<br>
of 99% for the Ford Escape PHEV and 100% for the Ford F-350. These findings<br>
confirm that utilizing machine learning models can effectively estimate vehicular fuel<br>
consumption; thus, these models can be used to monitor fleet vehicles’ energy con-<br>
sumption, and design strategies to reduce the fuel consumption from the UAlberta<br>
fleet vehicles.<br>
Additionally, this thesis investigated the energy consumption and cost of a con-<br>
ventional vehicle (Ford Escape S) with an internal combustion engine (ICE) and a<br>
PHEV (Ford Escape PHEV) from the UAlberta fleet. The vehicles were driven 243<br>
times on a 20-km route in Edmonton, Canada, during 2021 - 2022. The route in-<br>
cluded both urban and highway areas. The research also explored the impact of<br>
ambient temperature (Tamb) on the operations and energy consumption of the vehi-<br>
cles, considering different powertrains and electrification levels. This study reveals<br>
that for warm start tests, the total energy consumption increased by decreasing the<br>
Tamb from 32 °C to -24 °C. Modes that entail continuous operation of the electric<br>
motor are especially affected. Among the modes, Auto EV (i.e., electric and hybrid<br>
electric) mode demonstrated the highest increase in energy consumption, rising by<br>
almost 452% when the Tamb drops from 29°C to -24°C. Similarly, during cold start<br>
tests, there was an increase in energy consumption as the Tamb decreased from 29 °C<br>
to -18 °C. The mode that showed the highest increase in energy consumption was EV<br>
Now (i.e., all-electric) mode, with an increase of 527% by reducing the Tamb from 29<br>
°C to -13 °C.<br>
The thesis also examined the effect of start-stop technology on conventional vehi-<br>
cles’ energy consumption and operational costs. To conduct the study, three vehicles<br>
from the UAlberta fleet were tested, and the effect of start-stop technology was eval-<br>
uated on four different applications of UAlberta fleet vehicles. The findings indicated<br>
that the energy and cost savings achieved by vehicles equipped with start-stop tech-<br>
nology could be significant, depending on the vehicle drive cycle and idling percentage,<br>
as well as engine size. The fuel savings are anticipated to increase during the cold<br>
season operation of fleet vehicles.</p>