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Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction
Technical Paper
2020-01-5071
ISSN: 0148-7191, e-ISSN: 2688-3627
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Automotive Technical Papers
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English
Abstract
Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust, which allows fuel economy (FE) improvements in hybrid electric vehicles (HEVs) through optimal energy management strategies (EMS). Velocity predictions generated between 5 and 30 s predictions could be implemented using model predictive control (MPC), but the performance of MPC must be well understood. Also, the vulnerability of predictive optimal EMS to velocity prediction accuracy should be addressed. Before an optimal EMS can be implemented, its overall performance must be evaluated and benchmarked against relevant velocity prediction metrics. A real-world highway drive cycle (DC) in the high-fidelity, controls-oriented 2017 Toyota Prius Prime model operating in charge-sustaining mode was utilized to observe FE realization. We propose three important metrics for comparison to no velocity prediction control: (1) perfect full DC prediction using dynamic programming (DP), (2) 10 s prediction horizon MPC, (3) 10 s constant velocity prediction. The very first optimal EMS requires the whole DC prediction in advance, whereas the rest of the two strategies only require limited horizon velocity prediction. These different velocity predictions are input into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. Our results show that the constant velocity prediction algorithm outperformed the baseline control strategy, but underperformed the MPC strategy. We also show that using a 10 s prediction window, MPC strategy provided FE improvement results very close to the full DC prediction case. So with the advancement in perception systems, MPC could be implementable in real vehicles. Also, constant velocity prediction results are good enough that optimization should be implemented in vehicle controllers with MPC type of framework. In future work, we seek to combine these models with velocity prediction models to demonstrate FE improvements on a physical vehicle.
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Citation
Patil, A., Motallebiaraghi, F., Meyer, R., and Asher, Z., "Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction," SAE Technical Paper 2020-01-5071, 2020, https://doi.org/10.4271/2020-01-5071.Also In
References
- International Energy Agency 2015
- U.S Department of Energy 2016
- International Energy Agency 2015
- Thrun , S. Toward robotic cars Communication ACM 53 99 106 2010
- 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
- Asher , Z. , Patil , A. , Wifvat , V. , Frank , A. et al. Identification and Review of the Research Gaps Preventing a Realization of Optimal Energy Management Strategies in Vehicles SAE Int. J. Alt. Power. 8 2 2019 https://doi.org/10.4271/08-08-20-0009
- Tunnell , J. , Asher , Z.D. , Pasricha , S. , and Bradley , T.H. Toward Improving Vehicle Fuel Economy with ADAS SAE Int. J. of CAV 1 2 81 92 2018 https://doi.org/10.4271/12-01-02-0005
- Baker , D. , Asher , Z.D. , and Bradley , T. V2V Communication Based Real-World Velocity Predictions for Improved HEV Fuel E SAE Technical Paper 2018-01-1000 2018 https://doi.org/10.4271/2018-01-1000
- Zhang , P. , Yan , F. , and Du , C. A Comprehensive Analysis of Energy Management Strategies for Hybrid Electric Vehicles Based on Bibliometrics Renewable Sustainable Energy Review 48 88 104 2015/8
- Onori , S. , Serrao , L. , and Rizzoni , G. Hybrid Electric Vehicles: Energy Management Strategies 13 Berlin Heidelberg Springer 2016
- Wu , G. , Qi , X. , Barth , M. , and Boriboonsomsin , K. 2016
- 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 https://doi.org/10.4271/2017-26-0106
- Onori , S. , Serrao , L. , and Rizzoni , G. Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles ASME 2010 Dynamic Systems and Control Conference 2011 499 505 Wang , J. IEEE J. Quantum Electron.
- Borhan , H.A. , Vahidi , A. , Phillips , A.M. , Kuang , M.L. , and Kolmanovsky , I.V. Predictive Energy Management of a Power-Split Hybrid Electric Vehicle 2009 American Control Conference IEEE 2009 3970 3976
- Asher , Z.D. , Baker , D.A. , and Bradley , T.H. Prediction Error Applied to Hybrid Electric Vehicle Optimal Fuel Economy IEEE Transactions on Control Systems Technology 26 6 2121 2134 2017
- Rajamani , R. Vehicle Dynamics and Control Springer Science & Business Media 2011
- Asher , Z.D. , Trinko , D.A. , Payne , J.D. , Geller , B.M. , and Bradley , T.H. Real-Time Implementation of Optimal Energy Management in Hybrid Electric Vehicles: Globally Optimal Control of Acceleration Events Journal of Dynamic Systems, Measurement, and Control 142 8 2020
- Del Re , L. , Allgöwer , F. , Glielmo , L. , Guardiola , C. , and Kolmanovsky , I. Automotive Model Predictive Control: Models, Methods and Applications 402 Springer 2010
- Liu , K. , Asher , Z. , Gong , X. , Huang , M. , and Kolmanovsky , I. 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 https://doi.org/10.4271/2019-01-1051
- Gaikwad , T.D. , Asher , Z.D. , Liu , K. , Huang , M. , and Kolmanovsky , I. 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
- 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 2010
- Asher , Z.D. , Trinko , D.A. , and Bradley , T.H. Increasing the Fuel Economy of Connected and Autonomous Lithium-ion Electrified Vehicles Behaviour of Lithium-Ion Batteries in Electric Vehicles Cham Springer 2018 129 151
- Asher , Z.D. , Galang , A.A. , Briggs , W. , Johnston , B. et al. Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network SAE Technical Paper 2018-01-0315 2018 https://doi.org/10.4271/2018-01-0315
- Asher , Z.D. , Tunnell , J.A. , Baker , D.A. , Fitzgerald , R.J. et al. Enabling Prediction for Optimal Fuel Economy Vehicle Control SAE Technical Paper 2018-01-1015 2018 https://doi.org/10.4271/2018-01-1015
- Kirk , D.E. Optimal Control Theory: An Introduction Courier Corporation 2012
- Gong , Q. , Li , Y. , and Peng , Z.-R. Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles IEEE Transactions on Vehicular Technology 57 6 3393 3401 2008
- Xie , S. , Hu , X. , Xin , Z. , and Brighton , J. Pontryagin’s Minimum Principle-Based Model Predictive Control of Energy Management for a Plug-in Hybrid Electric Bus Applied Energy 236 893 905 2019
- Borhan , H. , Vahidi , A. , Phillips , A.M. , Kuang , M.L. et al. MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle IEEE Transactions on Control Systems Technology 20 3 593 603 2011
- Sun , C. , Hu , X. , Moura , S.J. , and Sun , F. Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 23 3 1197 1204 2014
- Fu , L. , Ümit , Ö. , Tulpule , P. , and Marano , V. Real-time Energy Management and Sensitivity Study for Hybrid Electric Vehicles Proceedings of the 2011 American Control Conference IEEE 2011 2113 2118
- Huang , M. , Zhang , S. , and Shibaike , Y. 2019
- Meyer , R. , DeCarlo , R.A. , Meckl , P.H. , Doktorcik , C. , and Pekarek , S. Hybrid Model Predictive Power Flow Control of a Fuel Cell-Battery Vehicle Proceedings of the 2011 American Control Conference IEEE 2011 2725 2731
- Meyer , R.T. , DeCarlo , R.A. , Meckl , P.H. , Doktorcik , C. , and Pekarek , S. Hybrid Model Predictive Power Management of a Fuel Cell-Battery Vehicle Asian Journal of Control 15 2 363 379 2013
- Meyer , R.T. , DeCarlo , R.A. , and Pekarek , S. Hybrid Model Predictive Power Management of a Battery-Supercapacitor Electric Vehicle Asian Journal of Control 18 1 150 165 2016