Development of Refuse Vehicle Driving and Duty Cycles

Dembski, Nicholas; Rizzoni, Giorgio; Soliman, Ahmed; Fravert, Jim; Kelly, Kenneth

doi:10.4271/2005-01-1165

Cited by 24 publications

(22 citation statements)

References 3 publications

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“…To capture the in-use drive cycle information analyzed as part of this study and to attempt to characterize the operation of school buses across the country, NREL researchers instrumented school buses located in three unique geographic regions (Colorado, Washington, New York) with onboard mounted Isaac Instruments DRU 900 and DRU 908 data loggers configured to collect 1-Hz global positioning system (GPS) data. The data sampling rate of 1 Hz was chosen based on the results of previous studies [1,2,3,4,6,7]. In addition to the deployment of manually installed devices, data were also collected using preinstalled Zonar Systems telemetric GPS devices at the same 1-Hz sampling rate.…”

Section: Data Collection and Drive Cycle Analysismentioning

confidence: 99%

“…characteristics of school buses across the country and Understanding vehicle usage plays a fundamental role in examine the dynamics challenges associated with the school assessing the performance potential of new vehicle bus vocation, National Renewable Energy Laboratory technologies, leading to more informed decision making, (NREL) researchers, with the cooperation of local school better test procedures [1,2,3,4,5,6,7], more commercially districts and Zonar Systems, collected drive cycle information successful designs, and lowered manufacturing and operating on class 6 and 7 school buses operating in three unique expenses. Of the methods available for modeling vehicle geographic locations.…”

Section: Introductionmentioning

confidence: 99%

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A Statistical Characterization of School Bus Drive Cycles Collected via Onboard Logging Systems

Duran

Walkowicz

2013

SAE Int. J. Commer. Veh.

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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. Based on the results of these analyses, high, low, and average vehicle dynamics requirements were determined, resulting in the selection of representative standard chassis dynamometer test cycles for each condition.In this paper, the methodology and accompanying results of the large-scale duty-cycle statistical analysis are presented, including graphical and tabular representations of a number of relationships between key duty-cycle metrics observed within the larger data set. In addition to presenting the results of this analysis, conclusions are drawn and presented regarding potential applications of advanced vehicle technology as it relates specifically to school buses.

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Section: Data Collection and Drive Cycle Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Statistical Characterization of School Bus Drive Cycles Collected via Onboard Logging Systems

Duran

Walkowicz

2013

SAE Int. J. Commer. Veh.

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“…Various studies have been developed by using real drive cycles in order to estimate the fuel consumption of an ICE [6]- [10]. Commonly, standard drive cycles has been established Several studies are based on standard drive cycles [11]- [14].…”

Section: Ecmmentioning

confidence: 99%

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-This paper presents a new methodology to estimate the fuel economy in a hybrid electric refuse collector vehicle (RCV). This methodology is based on determinating the fuel consumption in a conventional RCV using real routes, through a quasi-static model that incorporates a mathematical modeling of each component of the powertrain, including the longitudinal dynamic of the conventional RCV. Moreover, a classification of different operational modes of a conventional RCV using real routes is proposed to determinate the required energy that must be provided by the hybrid electric counterpart. The optimal sizing of the energy storage system for the hybrid electric powertrain is presented in order to estimate the fuel consumption by using a real route. The error in the estimation of fuel consumption in a conventional RCV is less than 1.5% in the worst case scenario. The energy storage system sizing allows a 8.62% reduction in the hybrid electric refuse collector vehicle's fuel consumption, by considering 10% of hybridization.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

GPS Data Filtration Method for Drive Cycle Analysis Applications

Duran¹,

Earleywine²

2012

SAE Technical Paper Series

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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 light-and 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.

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Development of Refuse Vehicle Driving and Duty Cycles

Cited by 24 publications

References 3 publications

A Statistical Characterization of School Bus Drive Cycles Collected via Onboard Logging Systems

A Statistical Characterization of School Bus Drive Cycles Collected via Onboard Logging Systems

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

GPS Data Filtration Method for Drive Cycle Analysis Applications

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