Drive Cycle Fuel Consumption Variability of Plug-In Hybrid Electric Vehicles Due to Aggressive Driving

Carlson, Richard Barney; Lohse-Busch, Henning; Duoba, Michael; Shidore, Neeraj

doi:10.4271/2009-01-1335

Cited by 43 publications

(29 citation statements)

References 3 publications

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“…In particular, it will be important to evaluate how well the method predicts in-use performance for non-blended PHEVs that possess high electric power capability. As discussed, the proposed method utilizes a simplifying assumption that the PHEV will deplete its stored battery energy over the same distance in real-world cycles as in the dynamometer tests, which may not be the case in reality [10]. This paper described how the UF application may help offset such differences, but further evaluation will need to confirm how well this works out.…”

Section: Results: Comparing Adjusted Dynamometer Results With In-mentioning

confidence: 99%

Deriving in-use PHEV fuel economy predictions from standardized test cycle results

Gonder

Brooker

Carlson

et al. 2009

2009 IEEE Vehicle Power and Propulsion Conference

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Abstract-Plug-in hybrid electric vehicles (PHEVs) have potential to reduce or eliminate the United States' dependence on foreign oil. Quantifying the amount of petroleum each uses, however, is challenging. To estimate in-use fuel economy for conventional vehicles, the U.S. Environmental Protection Agency (EPA) conducts chassis dynamometer tests on standard historic drive cycles and then adjusts the resulting "raw" fuel economy measurements downward. Various publications, such as the forthcoming update to the SAE J1711 recommended practice for PHEV fuel economy testing, address the challenges of applying standard test procedures to PHEVs. This paper explores the issue of how to apply an adjustment method to such raw PHEV dynamometer test results in order to more closely estimate the inuse fuel and electricity consumption characteristics of these vehicles. The paper discusses two possible adjustment methods, and evaluates one method by applying it to dynamometer data and comparing the result to in-use fleet data (on an aftermarket conversion PHEV). The paper also presents the methodologies used to collect the data needed for this comparison. The predictions using the proposed method are shown to provide close agreement with the in-use observations when the actual fleet charging characteristics are included.

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Section: Results: Comparing Adjusted Dynamometer Results With In-mentioning

confidence: 99%

Deriving in-use PHEV fuel economy predictions from standardized test cycle results

Gonder

Brooker

Carlson

et al. 2009

2009 IEEE Vehicle Power and Propulsion Conference

View full text Add to dashboard Cite

show abstract

“…For example, studies have shown that vehicle energy consumption rates can be quite sensitive to driving aggressiveness (high acceleration rates and higher speeds) [7,8].…”

Section: Driving Characteristicsmentioning

confidence: 99%

Correlating Dynamometer Testing to In-Use Fleet Results of Plug-In Hybrid Electric Vehicles

Smart

White

Duoba

2009

WEVJ

Self Cite

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The Untied States Department of Energy is engaged in the research, development and deployment of PHEV technology through the "Advanced Vehicle Testing Activity." In this program, data has been collected from a Prius PHEV converted by Hymotion TM using industry standard dynamometer testing procedures and from in-use operation of fleets. Differences in fuel-only consumption rates observed in the on-road data compared to the dynamometer results were fairly dramatic. Correlations of the driving style and conditions of dynamometer testing and on-road conditions were made possible by comprehensive onroad loggers. A model was developed to infer driving intensity from the on-road dataset from data load measurement during dynamometer testing. The reasons for the shortfall are detailed in the paper. They range from driver aggressiveness, accessory loads, ambient temperature (weather), and the high sensitivity the control system exhibits with increased acceleration pedal demand and higher average cruising speeds.

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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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Drive Cycle Fuel Consumption Variability of Plug-In Hybrid Electric Vehicles Due to Aggressive Driving

Cited by 43 publications

References 3 publications

Deriving in-use PHEV fuel economy predictions from standardized test cycle results

Deriving in-use PHEV fuel economy predictions from standardized test cycle results

Correlating Dynamometer Testing to In-Use Fleet Results of Plug-In Hybrid Electric Vehicles

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

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