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
Technical Paper
2020-01-0585
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
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.
Recommended Content
| Journal Article | Co-Simulation of Multiple Software Packages for Model Based Control Development and Full Vehicle System Evaluation |
| Journal Article | Automated Model Initialization Using Test Data |
| Technical Paper | Automotive Performance Optimization |
Authors
Topic
Citation
Hegde, 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
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 |
Also In
References
- 2019 https://www.nrel.gov/tsdc
- Duran , A. and Earleywine , M. GPS Data Filtration Method for Drive Cycle Analysis Applications SAE Technical Paper 2012-01-0743 2012 https://doi.org/10.4271/2012-01-0743
- Wood , E. , Burton , E. , Duran , A. , and Gonder , J. 2014
- 2019 https://www.nrel.gov/fastsim
- 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 https://doi.org/10.4271/2013-01-1453
- 2019 https://www.nrel.gov/drive
- 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 https://doi.org/10.4271/2015-01-0973
- Gonder , J. , Brooker , A. , Wood , E. , Moniot , M. et al. 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 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 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 IET Conference Publications 2013
- 2013
- https://saemobilus.sae.org/content/j2951_201111 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 IEEE Intelligent Vehicles Symposium, Proceedings 2017 1242 1248
- Holden , J. , Wood , E.W. , Zhu , L. , Gonder , J.D. et al. 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 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 10.1177/0361198118797805
- Wei , J. , Dolan , J.M. , and Litkouhi , B. A Prediction- and Cost Function-Based Algorithm for Robust Autonomous Freeway Driving 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 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 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 Proceedings of the American Control Conference 2019 4621 4627
- 2019 https://vires.com/vtd-vires-virtual-test-drive/