An empirical approach to the re-creation of vehicle drive cycles
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%.
Degree
M.S.
Advisors
Dietz, Purdue University.
Subject Area
Computer Engineering|Automotive engineering|Electrical engineering
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