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V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy
Technical Paper
2018-01-1000
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE.
This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement.
This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability. This Optimal Energy Management Strategy (EMS) achieved up to a 6% FE improvement over the Baseline EMS and up to 85% of the FE benefit of perfect speed prediction. Additionally, the results from this prediction method are compared to the results of a previous study that incorporates only local vehicle information in speed predictions.
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Baker, D., Asher, Z., and Bradley, T., "V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy," SAE Technical Paper 2018-01-1000, 2018, https://doi.org/10.4271/2018-01-1000.Data Sets - Support Documents
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References
- Cook , J. , Nuccitelli , D. , Skuce , A. , Jacobs , P. et al. Quantifying the consensus on anthropogenic global warming in the scientific literature Energy Policy 73 706 708 2014
- Carlton , J.S. , Perry-hill , R. , Huber , M. , and Prokopy , L.S. The climate change consensus extends beyond climate scientists Environ. Res. Lett. 10 9 94025 2015
- International_Energy_Organization 2016
- U.S. Environmental Protection Agency 1 10 2012
- J. Rogelj , M. den Elzen , N. Höhne , T. Fransen , H. Fekete , H. Winkler , R. Schaeffer , F. Sha , K. Riahi , and M. Meinshausen Paris Agreement climate proposals need a boost to keep warming well below 2 °C Nature 534 7609 631 639 2016
- U.S. Environmental Protection Agency 2012
- Romm , J. The car and fuel of the future Energy Policy 34 17 2609 2614 2006
- Bradley , T.H. and Frank , A.A. Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles Renew. Sustain. Energy Rev. 13 1 115 128 2009
- Hannan , M.A. , Azidin , F.A. , and Mohamed , A. Hybrid electric vehicles and their challenges: A review Renew. Sustain. Energy Rev. 29 135 150 2014
- Ates , Y. , Erdinc , O. , Uzunoglu , M. , and Vural , B. Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy Int. J. Hydrogen Energy 35 2 774 783 2010
- Asher , Z. D. , Wifvat , V. , Navarro , A. , Samuelsen , S. , and Bradley , T. H. The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management SAE Techical Paper 2017-26-0106 2016 10.4271/2017-26-0106
- IEEE http://sites.ieee.org/connected-vehicles/2015/09/30/first-toyota-cars-to-include-v2v-and-v2i-communication-by-the-end-of-2015/ 2017
- USDOT 1 4 2016
- Zhang , F. , Xi , J. , and Langari , R. Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications IEEE Trans. Intell. Transp. Syst. 1 15 2016
- Zulkefli , M.A.M. , Zheng , J. , Sun , Z. , and Liu , H.X. Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration Transp. Res. Part C Emerg. Technol. 45 41 63 2014
- Valera , J.J. , Heriz , B. , Lux , G. , Caus , J. , and Bader , B. Driving cycle and road grade on-board predictions for the optimal energy management in EV-PHEVs EVS27 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium 2013 1 10
- Zulkefli , M.A.M. and Sun , Z. Real-time Powertrain Optimization Strategy for Connected Hybrid Electrical Vehicle ASME 2016 Dynamic Systems and Control Conference 1 10 2017
- He , Y. , Chowdhury , M. , Pisu , P. , and Ma , Y. An energy optimization strategy for power-split drivetrain plug-in hybrid electric vehicles Transp. Res. Part C Emerg. Technol. 22 29 41 2012
- Fu , L. , Ozguner , U. , Tulpule , P. , and Marano , V. Real-time energy management and sensitivity study for hybrid electric vehicles American Control Conference (ACC) 2011 2113 2118 2011
- Bender , F.A. , Kaszynski , M. , and Sawodny , O. Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles IEEE Trans. Veh. Technol. 62 8 3581 3592 2013
- Asher , Z.D. , Baker , D.A. , and Bradley , T.H. Prediction Error Applied to Hybrid Electric Vehicle Optimal Fuel Economy IEEE Trans. Control Syst. Technol. 1 14 2017
- Bellman , R. Dynamic Programming and Lagrange Multipliers Proc. Natl. Acad. Sci. U. S. A 42 10 767 769 1956
- Sciarretta , A. and Guzzella , L. Control of hybrid electric vehicles IEEE Control Syst. Mag. 27 2 60 70 2007
- Sundström , O. and Guzzella , L. A generic dynamic programming Matlab function Proc. IEEE Int. Conf. Control Appl. 7 1625 1630 2009
- Lyshevski , S.E. and Yokomoto , C. Control 1998
- Brahma , A. , Guezennec , Y. , and Rizzoni , G. Optimal energy management in series hybrid electric vehicles Proceedings of the 2000 American Control Conference 1 6 60 64 2000
- Lin , C.-C. , Kang , J.-M. , Grizzle , J. W. , and Peng , H. Energy management strategy for a parallel hybrid electric truck Am. Control Conf. 2001 4 2878 2883 2001
- Rajamani , R. Vehicle Dynamics and Control Second Springer 2006
- B. de Jager , T. van Keulen , and J. Kessels Optimal Control of Hybrid Vehicles 2013
- Zhang , P. , Yan , F. , and Du , C. A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics Renew. Sustain. Energy Rev. 48 88 104 Aug. 2015
- Moura , S.J. , Fathy , H.K. , Callaway , D.S. , and Stein , J.L. A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles IEEE Trans. Control Syst. Technol. 19 3 545 555 May 2011
- Opila , D.F. , Wang , X. , McGee , R. , Gillespie , R.B. et al. An energy management controller to optimally trade off fuel economy and drivability for hybrid vehicles IEEE Trans. Control Syst. Technol. 20 6 1490 1505 2012
- Wang , Y. and Sun , Z. SDP-based extremum seeking energy management strategy for a power-split hybrid electric vehicle Am. Control Conf. 553 558 2012
- Opila , D.F. , Wang , X. , McGee , R. , and Grizzle , J.W. Real-Time Implementation and Hardware Testing of a Hybrid Vehicle Energy Management Controller Based on Stochastic Dynamic Programming J. Dyn. Syst. Meas. Control 135 March 2013 21002-1 21002-11 2012
- Moura , S.J. , Stein , J.L. , and Fathy , H.K. Battery-health conscious power management in plug-in hybrid electric vehicles via electrochemical modeling and stochastic control IEEE Trans. Control Syst. Technol. 21 3 679 694 2013
- Di Cairano , S. , Bernardini , D. , Bemporad , A. , and Kolmanovsky , I.V. Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management IEEE Trans. Control Syst. Technol. 22 3 1018 1031 May 2014
- Borhan , H. , Vahidi , A. , Phillips , A.M. , Kuang , M.L. et al. MPC-based energy management of a power-split hybrid electric vehicle IEEE Trans. Control Syst. Technol. 20 3 593 603 2012
- Yan , F. , Wang , J. , and Huang , K. Hybrid electric vehicle model predictive control torque-split strategy incorporating engine transient characteristics IEEE Trans. Veh. Technol. 61 6 2458 2467 2012
- Poramapojana , P. , and Chen , B. Proc. 2012 IEEE/ASME 8th IEEE/ASME Int. Conf. Mechatron. Embed. Syst. Appl. 148 153 2012
- Baker , D. Development of Predictive Energy Management Strategies for Hybrid Electric Vehicles Colorado State University 2017
- Baker , D. , Asher , Z. , and Bradley , T. H. Investigation of Vehicle Speed Prediction from Neural Network Fit of Real World Driving Data for Improved Engine On/Off Control of the EcoCAR3 Hybrid Camaro SAE Technical Paper 2017-01-1262 2017 10.4271/2017-01-1262
- A. N. Laboratory www.anl.gov/energy-systems/group/downloadable-dynamometer-database/hybrid-electric-vehicles/2010-toyota-prius
- Sun , C. , Sun , F. , and He , H. Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles Appl. Energy 185 1644 1653 2017
- Morton , J. , Wheeler , T.A. , and Kochenderfer , M.J. Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior 2016 1 10
- Jing , J. , Kurt , A. , Ozatay , E. , Michelini , J. , Filev , D. , and Ozguner , U. Vehicle Speed Prediction in a Convoy Using V2V Communication IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC 2015 2861 2868 2015
- https://www.gm.com/mol/m-2017-mar-0309-v2v.html 2017
- Møller , M.F. A scaled conjugate gradient algorithm for fast supervised learning Neural Networks 6 4 525 533 1993
- M. H. Beale , M. T. Hagan , and H. B. Demuth Neural Network Toolbox TM Getting Started Guide How to Contact MathWorks 2015