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Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control
Technical Paper
2019-01-1213
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program. We describe the NEXTCAR program objectives, including the mechanization and build of a demonstration vehicle. As the focus is on real-world fuel economy benefits, the criteria for, and development of, a set of route scenarios is described. In order to be able to develop the necessary optimization logic, and evaluate the benefits on route scenarios beyond those tested in-vehicle, a simulation model of the vehicle and the optimization controls has been developed and is discussed, including correlation testing results and simulated fuel economy benefits. Finally, initial results from the development vehicle running route scenarios on a test track are presented.
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Authors
- Pete Olin - Delphi Technologies
- Karim Aggoune - Delphi Technologies
- Li Tang - Delphi Technologies
- Keith Confer - Delphi Technologies
- John Kirwan - Delphi Technologies
- Shreshta Rajakumar Deshpande - Ohio State University
- Shobhit Gupta - Ohio State University
- Punit Tulpule - Ohio State University
- Marcello Canova - Ohio State University
- Giorgio Rizzoni - Ohio State University
Topic
Citation
Olin, P., Aggoune, K., Tang, L., Confer, K. et al., "Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control," SAE Technical Paper 2019-01-1213, 2019, https://doi.org/10.4271/2019-01-1213.Data Sets - Support Documents
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References
- U.S. Energy Information Administration 2018
- IHS Markit November, 2018
- Goldman Sachs Global Investment Research 2015
- Stockar , S. , Marano , V. , Canova , M. , Rizzoni , G. et al. Energy-Optimal Control of Plug-In Hybrid Electric Vehicles for Real-World Driving Cycles IEEE Transactions on Vehicular Technology 60 7 2949 2962 2011
- Lukic , S.M. and Emadi , A. Effects of Drivetrain Hybridization on Fuel Economy and Dynamic Performance of Parallel Hybrid Electric Vehicles IEEE Trans. Veh. Technol. 53 385 389 2004
- Serrao , L. , Onori , S. , and Rizzoni , G. A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles ASME. J. Dyn. Sys., Meas., Control. 133 3 031012-031012-9 2011
- Pisu , P. and Rizzoni , G. A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 15 3 506 518 2007
- Lu , D. , Ouyang , M. , Gu , J. , and Li , J. Optimal Velocity Control for a Battery Electric Vehicle Driven by Permanent Magnet Synchronous Motors Mathematical Problems in Engineering 2014 2014
- Ozatay , E. , Onori , S. , Wollaeger , J. , Ozguner , U. et al. Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution IEEE Transactions on Intelligent Transportation Systems 15 6 2491 2505 2014
- Hellstrom , E. , Ivarsson , M. , Aslund , J. , and Nielsen , L. Look-Ahead Control for Heavy Trucks to Minimize Trip Time and Fuel Consumption Control Engineering Practice 17 2 245 254 2009
- Gausemeier , S. , Jaker , K. et al. Multi-Objective Optimization of a Vehicle Velocity Profile by Means of Dynamic Programming IFAC Proceedings Volumes 43 7 366 371 2010
- Levermore , T. , Sahinkaya , M. , Zweiri , Y. , and Neaves , B. Realtime Velocity Optimization to Minimize Energy Use in Passenger Vehicles Energies 10 1 30 2016
- https://arpa-e.energy.gov/?q=arpa-e-programs/nextcar
- Eriksson , L. and Nielsen , L. Modeling and Control of Engines and Drivelines John Wiley & Sons 2014
- Sciarretta , A. , Back , M. , and Guzzella , L. Optimal Control of Parallel Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 12 3 352 363 2004
- Younkins , M. , Ortiz-Soto , E. , Kirwan , J. , Confer , K. et al. Advances in Dynamic Skip Fire: eDSF and mDSF 27th Aachen Colloquium Automobile and Engine Technology 2018
- Treiber , M. , Hennecke , A. , and Helbing , D. Congested Traffic States in Empirical Observations and Microscopic Simulations Physical Review E 62 2 2000
- Kesting , A. , Treiber , M. , and Helbing , D. Enhanced Intelligent Driver Model to Access the Impact of Driving Strategies on Traffic Capacity Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 368 1928 2010
- Tamilarasan , S. , Jung , D. , and Guvenc , L. Drive Scenario Generation Based on Metrics for Evaluating an Autonomous Vehicle Controller SAE Technical Paper 2018-01-0034 2018 10.4271/2018-01-0034