GPS Data Filtration Method for Drive Cycle Analysis Applications

Global Positioning System (GPS) data acquisition devices have proven useful tools for gathering real-world driving data and statistics. The data collected by these devices provide valuable information in studying driving habits and conditions. When used jointly with vehicle simulation software, the data are invaluable in analyzing vehicle fuel use and performance, aiding in the design of more advanced and efficient vehicle technologies. However, when employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the captured raw data samples. Common sources of error in GPS data include sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which combine to limit the quality of field data for use in downstream applications. Unaddressed, these errors significantly impact the reliability of source data and limit the effectiveness of traditional drive cycle analysis approaches and vehicle simulation software. Without reliable speed and time information, the validity of derived metrics for drive cycles, such as acceleration, power, and distance become questionable. This study explores some of the common sources of error present in collected raw GPS data and presents a detailed filtering process designed to correct for these issues. To illustrate the effectiveness of the proposed filtration process across the range of vehicle vocations, test data from both lightand medium/heavy-duty applications are examined. Graphical comparisons of raw and filtered cycles are presented, and statistical analyses performed to determine the effects of the proposed filtration process on raw data. Finally, the paper concludes with an evaluation of the overall benefits of data filtration on raw GPS data and presents potential areas for continued research. INTRODUCTION The cost-effective nature and ease of installation associated with GPS data acquisition systems (DASs) have aided in onboard global positioning system (GPS) data logging rapidly becoming one of the more popular methods for collecting real-world vehicle operating information [1, 2, 3, 4]. The coupled vehicle speed-time data captured by these devices are of particular interest when performing vehicle simulation and drive cycle analysis [5, 6, 7, 8, 9]. However, due to the sensitivity of these downstream applications to both the quality and integrity of GPS source data, the unique operating behavior and errors inherently associated with GPS data loggers have fostered a need for a novel filtration process. The ideal filtration method for improving the quality of raw GPS data is one that minimizes the effects of GPS data logging errors such as sudden signal loss, data spiking, signal white noise, and zero speed drift while maintaining the integrity of the raw source data [10, 11, 12]. This means removing or replacing any erroneous data points while simultaneously minimizing the total amount of data altered. The ideal filtration method must also be flexible enough for application to GPS data sourced from different logging devices. As such, any proposed filtration method must take into account differences in data quality available through different GPS DASs as well as the different vehicle vocations or types in which the loggers may be mounted. The GPS data filtration approach presented here consists of seven distinct filters designed and arranged specifically to meet these criteria. In an attempt to account for the common errors associated with the analysis of GPS speed-time data, a common GPS data processing method has been developed in the Matlab environment that employs a series of linearly progressing logic-based data filters [13]. GPS Data Filtration Method for Drive Cycle Analysis Applications 2012-01-0743 Published 04/16/2012 Adam Duran and Matthew Earleywine National Renewable Energy Laboratory doi:10.4271/2012-01-0743 NREL/CP-5400-53865. Posted with permission. Presented at the SAE 2012 World Congress.