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Estimation of Fuel Economy on Real-World Routes for Next-Generation Connected and Automated Hybrid Powertrains
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.
This paper describes a methodology to benchmark the fuel consumption reduction potential of a Level 1 Connected and Automated Vehicle (CAV) with advanced cylinder deactivation and 48V mild hybridization, in the presence of variability induced by route characteristics, traffic and driver behavior. An Intelligent Driving system utilizes advanced route information available from the navigation system and GPS, as well as a V2X communication module to determine the optimal vehicle velocity and battery state of charge profiles that aim at minimizing fuel consumption along a driver-selected route without sacrificing travel time. Since the presence of traffic and the behavior of different drivers strongly affects the fuel consumption and vehicle travel time, a Monte Carlo simulation is conducted to determine the statistical distribution of the results when introducing variability in the inputs. Fuel efficiency benefits are dependent on the route characteristics, traffic conditions and driver behavior. For the route evaluated in this paper, numerical results show as much as 15% to 19% reduction in fuel consumption, compared to a mild hybrid baseline vehicle without cylinder deactivation and CAV features.
- Shobhit Gupta - The Ohio State University
- Shreshta Rajakumar Deshpande - The Ohio State University
- Daniela Tufano - Universita Degli Studi di Napoli
- Marcello Canova - The Ohio State University
- Giorgio Rizzoni - The Ohio State University
- Karim Aggoune - Delphi Technologies, Inc.
- Pete Olin - Delphi Technologies, Inc.
- John Kirwan - Delphi Technologies, Inc.
CitationGupta, S., Rajakumar Deshpande, S., Tufano, D., Canova, M. et al., "Estimation of Fuel Economy on Real-World Routes for Next-Generation Connected and Automated Hybrid Powertrains," SAE Technical Paper 2020-01-0593, 2020, https://doi.org/10.4271/2020-01-0593.
Data Sets - Support Documents
|Unnamed Dataset 1|
- 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 10.1109/TVT.2011.2158565
- Lukic , S.M. and Emadi , A. Effects of Drivetrain Hybridization on Fuel Economy and Dynamic IEEE transactions on vehicular technology 53 2 385 389 2004 10.1109/TVT.2004.823525
- Goldman Sachs Global Investment Research Cars 2025 2015
- IHS Markit Light Duty Vehicle Sales Forecast October, 2019
- Mersky , A.C. and Samaras , C. Fuel Economy Testing of Autonomous Vehicles Transportation Research Part C: Emerging Technologies 65 31 48 2016 10.1016/j.trc.2016.01.001
- Tunnell , J. , Asher , Z. , Pasricha , S. , and Bradley , T. Toward Improving Vehicle Fuel Economy with ADAS SAE Intl. J CAV 1 2 81 92 2018 https://doi.org/10.4271/12-01-02-0005
- 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
- Liu , Z. , Ivanco , A. , and Filipi , Z.S. Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48V Mild Hybrid Vehicle SAE International Journal of Alternative Powertrains 5 2 249 258 2016 https://doi.org/10.4271/2016-01-1166
- Hellman , K.H. and Murrell , J.D. Development of Adjustment Factors for the EPA City and Highway MPG Values SAE Technical Paper 840496 1984 https://doi.org/10.4271/840496
- Metropolis , N. and Ulam , S. The Monte Carlo Method Journal of the American statistical association 44 247 335 341 1949
- Pan , W. , Xue , Y. , He , H.D. , and Lu , W.Z. Impacts of Traffic Congestion on Fuel Rate, Dissipation and Particle Emission in a Single Lane Based on Nasch Model Physica A: Statistical Mechanics and its Applications 503 154 162 2018 10.1016/j.physa.2018.02.199
- Gupta , S. , Deshpande , S.R. , Tulpule , P. , Canova , M. , and Rizzoni , G. An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes IFAC-Papers OnLine 52 5 574 579 2019 10.1016/j.ifacol.2019.09.091
- 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 2010 1928 10.1098/rsta.2010.0084
- Treiber , M. , Hennecke , A. , and Helbing , D. Congested Traffic States in Empirical Observations and Microscopic Simulations Physical Review E 62 2 2000 10.1103/PhysRevE.62.1805
- Kuypers , M. Application of 48 Volt for Mild Hybrid Vehicles and High Power Loads SAE Technical Paper 2014-01-1790 2014 https://doi.org/10.4271/2014-01-1790
- Eisazadeh-Far , K. and Younkins , M. Fuel Economy Gains through Dynamic-Skip-Fire in Spark Ignition Engines SAE Technical Paper 2016-01-0672 2016 https://doi.org/10.4271/2016-01-0672
- 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 10.1109/TCST.2004.824312
- Hiraoka , T. , Kunimatsu , T. , Nishihara , O. and Kumamoto , H. Modeling of Driver Following Behavior Based on Minimum-Jerk Theory Proc. 12th World Congress ITS 2005
- Itkonen , T.H. , Pekkanen , J. , Lappi , O. , Kosonen , I. et al. Trade-Off between Jerk and Time Headway as an Indicator of Driving Style PLoS One 12 10 e0185856 2017 10.1371/journal.pone.0185856
- Ping , P. , Qin , W. , Xu , Y. , Miyajima , C. , and Takeda , K. Impact of Driver Behavior on Fuel Consumption: Classification, Evaluation and Prediction Using Machine Learning IEEE Access 7 78515 78532 2019 10.1109/ACCESS.2019.2920489
- Higgs , B. and Abbas , M. Segmentation and Clustering of Car Following Behavior: Recognition of Driving Patterns IEEE Transactions on Intelligent Transportation Systems 16 1 81 90 2014 10.1109/TITS.2014.2326082
- Santhosh , D. and Srinivas , V.V. Bivariate Frequency Analysis of Floods Using a Diffusion Based Kernel Density Estimator Water Resources Research 49 12 8328 8343 2013 10.1002/2011WR010777
- Katsaros , K. , Kernchen , R. , Dianati , M. , Rieck , D. , and Zinoviou , C. Application of Vehicular Communications for Improving the Efficiency of Traffic in Urban Areas Wireless Communications and Mobile Computing 11 12 1657 1667 2011 10.1002/wcm.1233
- Di Fillipi , A. , Stockar , S. , Onori , S. , Canova , M. , and Guezennec , Y. Model-Based Life Estimation of li-Ion Batteries in PHEVs Using Large Scale Vehicle Simulations: An Introductory Study 2010 IEEE Vehicle Power and Propulsion Conference 2010 1 6 10.1109/VPPC.2010.5729020