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Skip to Search Results- 4Ansari, Amir
- 3Shahbakhti, Mahdi
- 2Abediasl, Hamidreza
- 1Hosseini, Vahid
- 1Koch, Charles Robert
- 1Liu, Yang
- 2Artificial Neural Networks
- 2Instantaneous Fuel Consumption
- 2Machine Learning
- 2Random Forest
- 1Abstract
- 1CO2 Emissions
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2022-06-01
Ansari, Amir, Abediasl, Hamidreza, Shahbakhti, Mahdi
Conference Abstract. Part of the Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022.
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Estimating Instantaneous Fuel Consumption of Vehicles By Using Machine Learning And Real-Time On-Board Diagnostics (OBD) Data
Download2022-06-01
Ansari, Amir, Abediasl, Hamidreza, Patel, Parth Rakeshkumar, Hosseini, Vahid, Koch, Charles Robert, Shahbakhti, Mahdi
Estimation of instantaneous fuel consumption of fleet vehicles to identify the causes of high fuel consumption and determine the optimum vehicle type for different applications and driving cycles is essential for the design of an intelligent fleet management system. Developing a practical and...
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Spring 2024
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...
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2022-06-01
Liu, Yang, Ansari, Amir, Shahbakhti, Mahdi
A driving cycle represents the operating conditions of a vehicle as a function of vehicle speed and time. It is used for assessment of vehicle energy consumption, tailpipe emissions, and driving behavior. A driving cycle depends on a vehicle application, geographical regions, and driving zones...