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Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy
Technical Paper
2019-01-1212
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction. These various velocity predictions are used as an input into an Optimal EMS derivation algorithm to derive an engine torque and engine speed control strategy that improves FE compared to current vehicle operation. Dynamic programming is used as the Optimal EMS because it provides a globally optimal control which is preferable for this investigatory study. The vehicle model used is real world validated and represents a 2017 Toyota Prius Prime operating in charge sustaining mode. The results show that actual vehicle velocity prediction from the prediction model achieves 85% of the FE improvement from an Optimal EMS derived using perfect 10 second prediction.
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Gaikwad, T., Asher, Z., Liu, K., Huang, M. et al., "Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy," SAE Technical Paper 2019-01-1212, 2019, https://doi.org/10.4271/2019-01-1212.Data Sets - Support Documents
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References
- EIA, 2017 2017 243
- Reidmiller , D.R. , Avery , C.W. , Easterling , D.R. , Kunkel , K.E. et al. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment II 2017
- Pierre-Louis , K. Greenhouse Gas Emissions Accelerate Like a ‘Speeding Freight Train’ in 2018 The new York times [New York] 2018 A1
- UNFCCC 2015 25
- Atabani , A.E. , Badruddin , I.A. , Mekhilef , S. , and Silitonga , A.S. A Review on Global Fuel Economy Standards, Labels and Technologies in the Transportation Sector Renewable Sustainable Energy Rev. 15 4586 4610 2011
- International Energy Agency 2015
- 2017
- Septon , K.K. 2017
- Birky , A. , Laughlin , M. , Tartaglia , K. , Price , R. et al. 2018
- 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 Transportation Research Part C: Emerging Technologies 45 41 63 2014
- Demuth , H.B. , Beale , M.H. , De Jess , O. , and Hagan , M.T. Neural Network Design 2nd USA Martin Hagan 2014
- Bender , F.A. , Kaszynski , M. , and Sawodny , O. Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles IEEE Trans Veh Technol 62 3581 3592 2013
- Donateo , T. , Pacella , D. , and Laforgia , D. Development of an Energy Management Strategy for Plug-In Series Hybrid Electric Vehicle Based on the Prediction of the Future Driving Cycles by ICT Technologies and Optimized Maps SAE Technical Paper 2011-01-0892 2011 10.4271/2011-01-0892
- Sun , C. , Moura , S.J. , Hu , X. , Hedrick , J.K. , and Sun , F. Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles IEEE Trans Control Syst Technol 23 1075 1086 2015
- Sun , C. , Hu , X. , Moura , S.J. , and Sun , F. Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles IEEE Trans Control Syst Technol 23 1197 1204 2015
- Gong , Q. , Li , Y , and Peng , Z. Power Management of Plug-In Hybrid Electric Vehicles Using Neural Network Based trip Modeling 2009 American Control Conference ieeexplore.ieee.org 2009 4601 4606
- Rezaei , A. and Burl , J.B. Prediction of Vehicle Velocity for Model Predictive Control IFAC-PapersOnLine 48 257 262 2015
- Gong , Q. , Li , Y. , and Peng , Z.R. Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles IEEE Trans Veh Technol 57 3393 3401 2008
- Mohd Zulkefli , M.A. , 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
- Sun , C. , Sun , F. , and He , H. Investigating Adaptive-ECMS with Velocity Forecast Ability for Hybrid Electric Vehicles Appl Energy 185 Part 2 1644 1653 2017
- Zhang , P. , Yan , F. , and Du , C. A Comprehensive Analysis of Energy Management Strategies for Hybrid Electric Vehicles Based on Bibliometrics Renewable Sustainable Energy Rev 48 88 104 2015
- Asher , Z.D. , Wifvat , V. , Navarro , A. , Samuelsen , S. , and Bradley , T. The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management SAE Technical Paper 2017-26-0106 2017 10.4271/2017-26-0106
- Zhang , P. , Yan , F. , and Du , C. A Comprehensive Analysis of Energy Man-880 Agement Strategies for Hybrid Electric Vehicles Based on Bibliometrics Renewable Sustainable Energy Rev. 48 88 104 2015
- Asher , Z.D. , Tunnell , J.A. , Baker , D.A. , and Fitzgerald , R.J. Enabling Prediction for Optimal Fuel Economy Vehicle Control SAE Technical Paper 2018-01-1015 2018 2018 10.4271/2018-01-1015
- 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. PP 1 14 2017
- Rajamani , R. Vehicle Dynamics and Control Springer Science & Business Media 2011
- 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 Transactions on Control Systems Technology 19 3 545 555 2011
- Liu , K. , Asher , Z. , Gong , X. , Huang , M. et al. Vehicle Velocity Prediction and Energy Management Strategy Part 1:Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning SAE Technical Paper 2019-01-1051 2019
- Baker , D. , Asher , Z.D. , and Bradley , T. V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel Economy SAE Technical Paper 2018-01-1000 2018 10.4271/2018-01-1000
- Baker , D. , Asher , Z. , and Bradley , T. 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
- Asher , Z.D. , Cummings , T. , and Bradley , T.H. The Effect of Hill Planning and Route Type Identification Prediction Signal Quality on Hybrid Vehicle Fuel Economy SAE Technical Paper 2016-01-1240 2016 10.4271/2016-01-1240