GPS Data Filtration Method for Drive Cycle Analysis Applications

Duran, Adam; Earleywine, Matthew

doi:10.4271/2012-01-0743

Cited by 47 publications

(29 citation statements)

References 16 publications

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“…The raw drive cycles were filtered and analyzed using NREL's Drive Cycle Rapid Investigation, Visualization, and Evaluation tool (DRIVE) [18,19] to generate individual drive cycle summary statistics. Using NREL's DRIVE tool, each drive cycle collected was characterized according to a total of 172 unique metrics, including key metrics such as operating speed distribution, stops per mile, acceleration rate, and vehicle kinetic intensity, a measure of the energy consumption nature of a cycle [6].…”

Section: Figure 1 Isaac Industries Data Logging Devicementioning

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: Figure 1 Isaac Industries Data Logging Devicementioning

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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“…and Houston, TX travel studies [4]. The data were recorded using on-board global positioning system data acquisition systems filtered down to second-by-second acceleration and velocity histories per the methods described in [5].…”

Section: Discussionmentioning

confidence: 99%

Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles

Neubauer

Wood

2013

SAE Technical Paper Series

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Hybrid electric vehicles, plug-in hybrid electric vehicles, and battery electric vehicles offer the potential to reduce both oil imports and greenhouse gases, as well as to offer a financial benefit to the driver. However, assessing these potential benefits is complicated by several factors, including the driving habits of the operator. We focus on driver aggression, i.e., the level of acceleration and velocity characteristic of travel, to (1) assess its variation within large, real-world drive datasets, (2) quantify its effect on both vehicle efficiency and economics for multiple vehicle types, (3) compare these results to those of standard drive cycles commonly used in the industry, and (4) create a representative drive cycle for future analyses where standard drive cycles are lacking. INTRODUCTIONHybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gases, as well as to offer a financial benefit to the driver. However, assessing these potential benefits is complicated by several factors, including the local climate, the cleanliness of the grid supplying electricity (for PHEVs and BEVs), and the driving habits of the operator, among many other things. Driving habits can be divided into two topics for consideration: (1) trip patterns, i.e., the distribution of trip times and distances, and (2) aggression, i.e., the level of acceleration and the velocity characteristics of travel. Investigation of trip patterns in [1] and [2] found that the variation in the distribution of daily miles traveled observed in real-world, multi-day drive data [3] produces significant variation in gasoline savings and total cost of ownership.Herein, we focus on driver aggression to (1) assess its variation within large, real-world drive datasets, (2) quantify its effect on both vehicle efficiency and economics for multiple vehicle types, (3) compare these results to those of standard drive cycles commonly used in the industry, and (4) create a representative drive cycle for future analyses where standard drive cycles are lacking. By doing so, we aim to supply an approach for vehicle performance simulation and testing that accurately captures the variation between different drivers, in particular for high-level techno-economic analyses performed using the National Renewable Energy Laboratory's (NREL's) Battery Ownership Model. This work is supported by the U.S. Department of Energy's Vehicle Technologies Program. ANALYSISIn this study, we apply a high-resolution vehicle simulator to real-world drive data to calculate a distribution of vehicle efficiencies for multiple vehicle types and operational modes. We then analyze these distributions and compare them to standard drive cycles commonly employed in the industry. From there, we synthesize and validate an artificial drive cycle that characterizes the average of the complete set of real-world drive data. We also describe in detail a set of associated scaling f...

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“…The NEDC is an authoritative "modal" or "polygonal" cycle that consists of artificially constructed micro-tips [14]. In these kinds of micro-trips, values, such as speed and acceleration, are not based on realistic on-road data.…”

Section: Construction and Comparison Of Proposed Driving Cyclesmentioning

confidence: 99%

Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis

Xiao

Wang

et al. 2017

Energies

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A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery's state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies.

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GPS Data Filtration Method for Drive Cycle Analysis Applications

Cited by 47 publications

References 16 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

Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles

Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis

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