This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.
CitationHegde, B., Muldoon, S., O'Keefe, M., and Gonder, J., "Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies," SAE Technical Paper 2020-01-0585, 2020, https://doi.org/10.4271/2020-01-0585.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
- “Transportation Secure Data Center,” National Renewable Energy Laboratory, 2019, https://www.nrel.gov/tsdc, accessed: Oct. 23, 2019.
- Duran, A. and Earleywine, M. , “GPS Data Filtration Method for Drive Cycle Analysis Applications,” SAE Technical Paper 2012-01-0743, 2012, doi:https://doi.org/10.4271/2012-01-0743.
- Wood, E., Burton, E., Duran, A., and Gonder, J. , “Appending High-Resolution Elevation Data to GPS Speed Traces for Vehicle Energy Modeling and Simulation,” Golden, CO, June 2014.
- “FASTSim: Future Automotive Systems Technology Simulator,” National Renewable Energy Laboratory, 2019, https://www.nrel.gov/fastsim, accessed Jan. 13, 2020.
- Neubauer, J.S. and Wood, E. , “Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles,” SAE Technical Paper 2013-01-1453, 2013, doi:https://doi.org/10.4271/2013-01-1453.
- “DRIVE: Drive-Cycle Rapid Investigation, Visualization, and Evaluation Analysis Tool,” nNational Renewable Energy Laboratory, 2019, https://www.nrel.gov/drive, accessed Jan. 13, 2020.
- Brooker, A., Gonder, J., Wang, L., Wood, E. et al. , “FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance,” SAE Technical Paper 2015-01-0973 21-23, 2015, doi:https://doi.org/10.4271/2015-01-0973.
- Gonder, J., Brooker, A., Wood, E., Moniot, M. et al. , “Future Automotive Systems Technology Simulator ( FASTSim ) Validation Report,” National Renewable Energy Laboratory, 2018.
- Gonder, J., Markel, T., Thornton, M., and Simpson, A. , “Using Global Ppsitioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles,” Transportation Research Record 26-32, 2007, doi:10.3141/2017-04.
- Wood, E., Gonder, J., and Jehlik, F. , “On-Road Validation of a Simplified Model for Estimating Real-World Fuel Economy,” SAE Int. J. Fuels Lubr. 10(2):528-536, 2017, doi:https://doi.org/10.4271/2017-01-0892.
- Mohan, G., Assadian, F., and Longo, S. , “Comparative Analysis of Forward-Facing Models vs Backward-Facing Models in Powertrain Component Sizing,” in IET Conference Publications, 2013.
- “J-2951 Dynamometer Repeatability Testing - Argonne National Laboratory Internal Study,” 2013.
- “Drive Quality Evaluation for Chassis Dynamometer Testing (J2951 Ground Vehicle Standard),” https://saemobilus.sae.org/content/j2951_201111, accessed Oct. 23, 2019.
- Zhu, L., Holden, J., Wood, E., and Gender, J. , “Green Routing Fuel Saving Opportunity Assessment: A Case Study Using Large-Scale Real-World Travel Data,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, 1242-1248.
- Holden, J., Wood, E.W., Zhu, L., Gonder, J.D. et al. , “Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data,” Montreal, Canada, 2017.
- Holden, J., Van Til, H., Wood, E., Zhu, L. et al. , “Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications,” Transportation Research Record 41-48, 2018, doi:10.1177/0361198118798286.
- Zhu, L., Holden, J.R., and Gonder, J.D. , “Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles,” Transportation Research Record: Journal of the Transportation Research Board 139-149, Dec. 2018, doi:10.1177/0361198118797805.
- Wei, J., Dolan, J.M., and Litkouhi, B. , “A Prediction- and Cost Function-Based Algorithm for Robust Autonomous Freeway Driving,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2010, 512-517.
- Hegde, B. , Look-Ahead Energy Management Strategies for Hybrid Vehicles (Columbus, OH: The Ohio State University, 2018).
- Hegde, B., Rajendran, A.V., Ahmed, Q., and Rizzoni, G. , “On Quantifying the Utility of Look-Ahead Data for Energy Management,” IFAC-PapersOnLine, 2018, doi:10.1016/j.ifacol.2018.10.011.
- Zhao, J., Wu, H., and Chang, C. , “Virtual Traffic Simulator for Connected and Automated Vehicles,” SAE Technical Paper 2019-01-0676 1-8, 2019, doi:https://doi.org/10.4271/2019-01-0676.
- Zhao, J., Hu, Y., Muldoon, S., and Chang, C.F. , “‘InfoRich’ Eco-Driving Control Strategy for Connected and Automated Vehicles,” in Proceedings of the American Control Conference, 2019, 4621-4627.
- “VTD - VIRES Virtual Test Drive,” VIRES Simulationstechnologie GmbH, 2019, https://vires.com/vtd-vires-virtual-test-drive/, accessed Jan. 24, 2020.