Semester

Spring

Date of Graduation

2019

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Cosmin Dumitrescu

Committee Co-Chair

V'yacheslav Akkerman

Committee Member

V'yacheslav Akkerman

Committee Member

Arvind Thiruvengadam

Abstract

Chassis dynamometer and on-road testing are usually employed to test vehicle operation. Testing on a chassis dynamometer reduces data variability compared to on-road testing due to the controlled environment but it does not account for other important variables that affects real-world vehicle operation. This study used on-road testing to investigate the differences between two test fuels under real-world conditions. Three heavy-duty diesel vehicles were driven on different routes for a period of three months. Each vehicle was instrumented with flow meters to gather fuel consumption data, which was then compared to the fuel rate broadcasted by the engine control unit (ECU). Additionally, the driveshaft torque was measured using a strain gage and a torque transmitter, which was used to confirm that the output torque was correlated to the vehicle’s fuel consumption. Data from both the ECU and the sensors were stored on a portable activity measurement system (PAMS), which also collected global positioning system (GPS) data and ambient conditions. The experimental procedure was based on SAE J1321. Due to the proprietary nature of the data, specific results of the study were not shown. However, the thesis details the design of experiments, including the selection, installation, benefits, and limitations of using additional sensors to improve data analysis. It also discusses the data storage and methods used for data analysis with the considerably large data sets obtained in the study. For example, while ~4.5 million data points were collected for each vehicle and each month of testing, more than 55% of the data points were discarded due to idling, engine cutoff during downhill operation, and adverse weather conditions. With respect to data analysis, the principal component analysis (PCA) identified the variables that caused the most variability in the datasets. PCA and data binning were used to compare datasets and determine the differences between them. The results show that the route with the most interstate data supplied the highest number of usable data points. Moreover, the ECU fuel consumption was consistent with the flow meter data with an average percent error of 2.5%. Measuring the engine torque using a torque meter can be difficult for on-road testing due to the excessive vibration experienced by the sensor.

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