Date of Award

Spring 2015

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer and Information Technology

First Advisor

James E. Dietz

Committee Chair

James E. Dietz

Committee Member 1

John A Springer

Committee Member 2

Robert Hamlen

Abstract

Vehicles such as buses, delivery trucks, mining equipment, and motorsport vehicles often repeat a highly defined pattern, route, or track during normal use. For these vehicles, standard dynamometer drive cycles are of little use. It was proposed that deriving a vehicle drive cycle from empirical data collected from on-board vehicle sensors would produce more accurate vehicle characteristic predictions for special purpose vehicles. This study answers the question "Is it possible to use recorded vehicle data to replicate a real world driving scenario for the purpose of vehicle diagnostics?" To reduce the complexity of the project, an electric go-kart was used as test vehicle. The go-kart was driven around the Purdue Gand Prix kart track. Data was collected from on-board sensors built into the vehicle motor controller. A turn by turn analysis of the recorded data is provided. A chassis dynamometer was redesigned to replicate the recorded drive cycle. The recorded drive cycle was replicated using the same test vehicle and the on-track data is compared to the in-lab data. During drive cycle re-creation, the system was found to have an average RPM error of 3.23% and an average current error of 7.89%. The comparison of the energy used on the track and in the lab test demonstrated that the cumulative energy used varied by only 0.49%.

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