Methods to analyze large automotive fleet-tracking datasets with application to light- and medium-duty plug-in hybrid electric vehicle work trucks

• Main Author: Vore, Spencer
• Language: EN
• Published: Colorado State University 2017
• Subjects: Mechanical engineering|Computer science
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=10196319

<p> This work seeks to define methodologies and techniques to analyze automotive fleet-tracking big data and provide sample results that have implications to the real world. To perform this work, vehicle fleet-tracking data from Odyne and Via Plug-in Hybrid Electric Trucks collected by the Electric Power Research Institute (EPRI) was used. Both CAN-communication bus signals and GPS data were recorded off of these vehicles with a second-by-second data collection rate. Colorado State University (CSU) was responsible for analyzing this data after it had been collected by EPRI and producing results with application to the real world. </p><p> A list of potential research questions is presented and an initial feasibility assessment is performed to determine how these questions might be answered using vehicle fleet-tracking data. Later, a subset of these questions are analyzed and answered in detail using the EPRI dataset. </p><p> The methodologies, techniques, and software used for this data analysis are described in detail. An algorithm that summarizes second-by-second vehicle tracking data into a list of higher-level driving and charging events is presented and utility factor (UF) curves and other statistics of interest are generated from this summarized event data. </p><p> In addition, another algorithm was built on the driving event identification algorithm to discretize the driving event data into approximately 90-second drive intervals. This allows for a regression model to be fit onto the data. A correlation between ambient temperature and equivalent vehicle fuel economy (in miles per gallon) is presented for Odyne and it is similar to the trend seen in conventional vehicle fuel economy vs. ambient temperature. It is also shown how ambient temperature variations can influence the vehicle fuel economy and there is a discussion about how changes in HVAC use could influence the fuel economy results. </p><p> It is also demonstrated how variations in the data analysis methodology can influence the final results. This provides evidence that vehicle fleet-tracking data analysis methodologies need to be defined to ensure that the data analysis results are of the highest quality. The questions and assumptions behind the presented analysis results are examined and a list of future work to address potential concerns and unanswered questions about the data analysis process is presented. Hopefully, this future work list will be beneficial to future vehicle data analysis projects. </p><p> The importance of using real-world driving data is demonstrated by comparing fuel economy results from our real-world data to the fuel economy calculated by EPA drive cycles. Utility factor curves calculated from the real-world data are also compared to standard utility factor curves that are presented in the SAE J2841 specification. Both of these comparisons showed a difference in real-world driving data, demonstrating the potential utility of evaluating vehicle technologies using the real-world big data techniques presented in this work. </p><p> Overall, this work documents some of the data analysis techniques that can be used for analyzing vehicle fleet-tracking big data and demonstrates the impact of the analysis results in the real world. It also provides evidence that the data analysis methodologies used to analyze vehicle fleet-tracking data need to be better defined and evaluated in future work.</p>