Impact of Real-World Drive Cycles on PHEV Battery Requirements

Fellah, Mohammed; Singh, Gurhari; Rousseau, Aymeric; Pagerit, Sylvain; Nam, Edward K.; Hoffman, George

doi:10.4271/2009-01-1383

Cited by 26 publications

(8 citation statements)

References 4 publications

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“…The class 6 and 7 school buses, with usage, operational drive/duty cycles have shown the ability to typical capacities of 71 to 84 passengers, were located in the accurately represent vehicle operating information in an states of Washington, New York, and Colorado. Although efficient and streamlined manner well suited to downstream smaller-sized vehicles exist that fall under the vocational analysis [8,9,10,11,12,13,14,15,16,17]. Typically category of school buses, no data was collected or examined consisting of speed and time information, these seemingly associated with this special subset, as vehicles falling within simple pieces of information, when properly understood, this group display operating characteristics dissimilar to their have been used to assess the value of new technology and to larger counterparts.…”

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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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: 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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“…Drive cycle has been shown to dramatically impact fuel consumption and emissions production for MD and HD vehicles [16,17,18,19,20,21]. As such, controlled laboratory test procedures representative of real-world operating…”

Section: Development Of a Representative Transientmentioning

confidence: 99%

Leveraging Big Data Analysis Techniques for U.S. Vocational Vehicle Drive Cycle Characterization, Segmentation, and Development

Duran

Phillips

Perr-Sauer

et al. 2018

SAE Technical Paper Series

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(NREL) performed a series of in-depth analyses to characterize on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of mediumand heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium-and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real-world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical drive cycle data captured from medium-and heavy-duty vehicles operating across the United States. The data encompass existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/ data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topographies ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. This paper includes the results of the statistical analysis performed by NREL and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work is also included.

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“…Hence, it is necessary to develop a unique driving cycle for each typical city and region. Moreover, many researchers have found that the energy consumption of an EV for a real-world driving cycle greatly differs from that based on the international standard driving cycles [19,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Zhao

et al. 2018

Journal of Advanced Transportation

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This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.

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Impact of Real-World Drive Cycles on PHEV Battery Requirements

Cited by 26 publications

References 4 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

Leveraging Big Data Analysis Techniques for U.S. Vocational Vehicle Drive Cycle Characterization, Segmentation, and Development

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

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