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In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

This research project compares the in-use and laboratory-derived fuel economy of a medium-duty hybrid electric drivetrain with “engine off at idle” capability to a conventional drivetrain in a typical commercial package delivery application. Vehicles in this study included eleven model year 2010 Freightliner P100H hybrids that were placed in service at a United Parcel Service (UPS) facility in Minneapolis, Minn., during the first half of 2010. These hybrid vehicles were evaluated for 18 months against eleven model year 2010 Freightliner P100D diesels that were placed in service at the same facility a couple months after the hybrids. Both vehicle study groups use the same model year 2009 Cummins ISB 200 HP engine. The vehicles of interest were chosen by comparing the average daily mileage of the hybrid group to that of a similar size and usage diesel group.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

In this paper, researchers at the National Renewable Energy Laboratory present the results of simulation studies to evaluate potential fuel savings as a result of improvements to vehicle rolling resistance, coefficient of drag, and vehicle weight as well as hybridization for four powertrains for medium-duty parcel delivery vehicles. The vehicles will be modeled and simulated over 1,290 real-world driving trips to determine the fuel savings potential based on improvements to each technology and to identify best use cases for each platform. The results of impacts of new technologies on fuel saving will be presented, and the most favorable driving routes on which to adopt them will be explored.

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed.

When developing and designing new technology for integrated vehicle systems deployment, standard cycles have long existed for chassis dynamometer testing and tuning of the powertrain. However, to this day with recent developments and advancements in plug-in hybrid and battery electric vehicle technology, no true “work day” cycles exist with which to tune and measure energy storage control and thermal management systems. To address these issues and in support of development of a range-extended pickup and delivery Class 6 commercial vehicle, researchers at the National Renewable Energy Laboratory in collaboration with Cummins analyzed 78,000 days of operational data captured from more than 260 vehicles operating across the United States to characterize the typical daily performance requirements associated with Class 6 commercial pickup and delivery operation.