Driving Cycle and Driver Behavior Analysis for University of Alberta Fleet Vehicles
Creators: Liu, Yang
Publisher: name: University of Alberta Library
Year: 2024

<p>This thesis as a part of the University of Alberta Energy Management and Sustainable Operations (EMSO) program aims to design an intelligent management tool for the University of Alberta fleet vehicles to achieve the fuel consumption reduction, and driver behavior improvement. To achieve this goal, developing the fleet vehicles&#39; driving cycles is essential. The driving cycle is affected by factors such as vehicle application, driving area, etc. This thesis divided the fleet vehicles into four categories according to the vehicle applications and developed distinctive driving cycles for each category. </p>

<p>This study used Freematics one + OBD data loggers to collect real-world driving data of vehicles. The collected data are divided into Microtrips, which are trips between two consecutive times when the vehicle speed is zero. Multiple driving scenarios are generated by using an unsupervised clustering algorithm to classify Microtrips. Driving cycles for different vehicle categories are generated by combining data from different driving scenarios. </p>

<p>The results show that different vehicle categories have different driving cycle characteristics. Those vehicles running in the campus area (such as utility and trade, shuttle minibus, and University of Alberta police services category vehicles) have a low average velocity which are in the range of 17.5 km/h to 24.6 km/h. In contrast, the average operating velocity for highway-running vehicles, like casual rental category vehicles, is 51.9 km/h. The University of Alberta police services category vehicles have the largest ratio of idle time in the driving cycle which is 41.1 \%, but other category vehicles’ idle time ratio to the driving cycle are in the range of 17.0 \% to 27.4 \%.</p>

<p>Driver behavior, especially driver aggressiveness, directly affects a vehicle&#39;s fuel consumption (FC). Two shuttle minibuses and one Ford Escape Plug-In Hybrid Electric Vehicle (PHEV) with three fixed drivers were selected to do the test. The driving route was fixed and the vehicle model between shuttle minibuses was identical. The collected data was then used to develop and assess driving aggressiveness (DA).  Different from the traditional statistical analysis method, this thesis adopts the frequency domain analysis method to analyze DA and apply a quantitative DA evaluation metric. According to the frequency of occurrence of driving data in different driving situations, revised average fuel consumption (RAFC) was used to analyze the effect of DA on FC. </p>

<p>The results show that DA can have adverse impact on certain driving scenarios. The bigger the DA value the greater the average fuel consumption, but the RAFC value may be different. When the DA value is close to 1, the driver drives more aggressively and causes more fuel consumption. On the contrary, when DA is close to 0, the driver drives smoothly and consumes less fuel. To reduce driver aggressiveness and achieve economic driving with low fuel consumption, future research may focus on driver training programs for key driving scenarios including urban driving scenario and highway driving scenario.</p>