Data Management for Geographically and Temporally Rich Plug-in Hybrid Vehicle “Big Data”
1
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA
2
Electric Power Research Institute, Palo Alto, CA, USA
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2016, 8(1), 293-304; https://doi.org/10.3390/wevj8010293
Published: 25 March 2016
Abstract
The Electric Power Research Institute (EPRI) and its project partners have developed some of the highest resolution and most complete Light- and Medium-Duty Plug-in Hybrid Electric Vehicle Truck operational data for Odyne and Via trucks. This data was collected through a CDMA / GMS transmitter plugged into the CAN communication bus of the fleet. This paper discusses the process of transforming these raw datasets into a scientific database of driving and charging events using data quality management, filtering, processing and decision support tool development. The result is a dataset with demonstrable utility for vehicle design, policy analysis, and operator feedback.
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MDPI and ACS Style
Vore, S.; Kosowski, M.; Wilkins, Z.; Bradley, T.H. Data Management for Geographically and Temporally Rich Plug-in Hybrid Vehicle “Big Data”. World Electr. Veh. J. 2016, 8, 293-304. https://doi.org/10.3390/wevj8010293
AMA Style
Vore S, Kosowski M, Wilkins Z, Bradley TH. Data Management for Geographically and Temporally Rich Plug-in Hybrid Vehicle “Big Data”. World Electric Vehicle Journal. 2016; 8(1):293-304. https://doi.org/10.3390/wevj8010293
Chicago/Turabian StyleVore, Spencer, Mark Kosowski, Zachary Wilkins, and Thomas H. Bradley. 2016. "Data Management for Geographically and Temporally Rich Plug-in Hybrid Vehicle “Big Data”" World Electric Vehicle Journal 8, no. 1: 293-304. https://doi.org/10.3390/wevj8010293
