This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Energy-Optimal Deceleration Planning System for Regenerative Braking of Electrified Vehicles with Connectivity and Automation
Technical Paper
2020-01-0582
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events perceived by map and navigation information, machine vision and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while in real time considering preceding vehicles. A practical force balance relationship based on an electrified powertrain is explicitly utilized for building a cost function of the associated optimal control problem. The optimal inputs are parameterized on each computation node from a set of available deceleration profiles resulting from a deceleration time model which are configured by real-world test drivings. Also, to maximize energy recuperation and avoid front collision and jittering, the proposed EDPS uses a hierarchical control architecture with two layers: long-sighted planning system considering the entire scope of deceleration events and real-time replanning system considering the run-time look-ahead information. Experiments which are based on a real-world driving data obtained from a plug-in hybrid vehicle (PHEV) indicate that the regenerative energy of EDPS has been improved over average 33 % when comparing to the existing system driven by a human driver without connectivity and automation benefits, and the results also show that EDPS generates feasible deceleration profiles.
Recommended Content
Authors
Topic
Citation
Kim, D., Eo, J., Kim, Y., Guanetti, J. et al., "Energy-Optimal Deceleration Planning System for Regenerative Braking of Electrified Vehicles with Connectivity and Automation," SAE Technical Paper 2020-01-0582, 2020, https://doi.org/10.4271/2020-01-0582.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 |
Also In
References
- Boulanger , A.G. , Chu , A.C. , Maxx , S. , and Waltz , D.L. Vehicle Electrification: Status and Issues Proceedings of the IEEE 99 6 1116 1138 2011
- Rauh , J. and Ammon , D. System Dynamics of Electrified Vehicles: Some Facts, Thoughts, and Challenges Vehicle System Dynamics 49 7 1005 1020 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. Contr. Syst. Techn. 20 6 1490 1505 Nov. 2012
- Ozatay , E. , Onori , S. , Wollaeger , J. , Ozguner , U. et al. Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution IEEE Trans. Intellig. Transport. Syst. 15 6 2491 2505 Dec 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 May 2004
- Sciarretta , A. , Nunzio , G.D. , and Ojeda , L.L. Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem IEEE Contr. Syst. Mag. 35 5 71 90 2015
- Rizzoni , G. and Onori , S. Energy Management of Hybrid Electric Vehicles: 15 Years of Development at the Ohio State University Oil & Gas Science and Technology-Revue d’IFP Energies Nouvelles 70 1 41 54 2015
- 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. Contr. Syst. Techn. 22 3 1018 1031 May 2014
- Sun , C. , Moura , S.J. , Hu , X. , Hedrick , J.K. et al. Dynamic Traffic Feedback Data Enabled Energy Management in Plug-In Hybrid Electric Vehicles IEEE Transactions on Control Systems Technology 23 3 1075 1086 2015
- Lu , S. , Xue , F. , Ting , T. , and Du , Y. Speed Trajectory Optimisation for Electric Vehicles in Eco-Approach and Departure Using Linear Programming IET International Conference on Intelligent and Connected Vehicles (ICV 2016) 2016
- Qi , M. , Barth , X. , Wu , G. , Boriboonsomsin , K. et al. Energy Impact of Connected Eco-Driving on Electric Vehicles Springer 2018 97 111
- Guanetti , J. , Kim , Y. , and Borrelli , F. Control of Connected and Automated Vehicles: State of the Art and Future Challenges Annual Rev. Control 45 1 2018
- Bae , S. , Kim , Y. , Guanetti , J. , Borrelli , F. et al. Design and Implementation of Ecological Adaptive Cruise Control for Autonomous Driving with Communication to Traffic Lights American Control Conference (ACC 2019) 2019 4628 4634
- Wan , N. , Vahidi , A. , and Luckow , A. Optimal Speed Advisory for Connected Vehicles in Arterial Roads and the Impact on Mixed Traffic Transport. Research Part C: Emerg. Techn. 69 548 563 2016
- Koch-Groeber , H. and Wang , J. Criteria for Coasting on Highways for Passenger Cars SAE Technical Paper 2014-01-1157 2014 https://doi.org/10.4271/2014-01-1157
- Guzzella , L. and Sciarretta , A. Vehicle Propulsion Systems: Introduction to Modeling and optimization Third Berlin Springer 2013
- Akcelik , R. and Biggs , D.C. Acceleration Profile Models for Vehicles in Road Rraffic Transport. Science 21 1 1987