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Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles
Technical Paper
2016-01-2226
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Advanced Driver Assistance Systems (ADAS) is an essential aspect of the automotive technology in this era of technological revolution, where the goal is to make vehicles more convenient, safe, and energy efficient. Taking advantage of more degrees of freedom available within vehicle “energy management” allows more margin to maximize efficiency in the propulsion systems. It is envisioned by this research that future fuel economy regulations will consider the potential benefits of emerging connectivity and automation technologies of vehicle’s fuel consumption. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevation are obtained by use of Geographic Information System (GIS) maps in order to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.
The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade. This control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the propulsion system. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications.
The paper presents multiple control strategies designed, implemented, and tested using real world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes.
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Citation
Alzorgan, M., Carroll, J., Al-Masalmeh, E., and Mayyas, A., "Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles," SAE Technical Paper 2016-01-2226, 2016, https://doi.org/10.4271/2016-01-2226.Also In
References
- Sciarretta , Antonio , and Guzzella Lino Control of hybrid electric vehicles Control systems, IEEE 27 2 2007 60 70 10.1109/mcs.2007.338280
- Ozatay , Engin , Onori Simona , Wollaeger James , Ozguner Umit , Rizzoni Giorgio , Filev Dimitar , Michelini John , and Di Cairano Stefano Cloud-based velocity profile optimization for everyday driving: A dynamic-programming-based solution IEEE Transactions on Intelligent Transportation Systems 15 6 2014 2491 2505
- Han , J. , Kum D. , and Park Y. Impact of hilly road information on fuel economy of FCHEV based on parameterization of hilly roads International Journal of Automotive Technology 15 2 2014 283 290
- Zhang , Chen , Vahidi Ardalan , Pisu Pierluigi , Li Xiaopeng , and Tennant Keith Role of terrain preview in energy management of hybrid electric vehicles Vehicular Technology, IEEE Transactions on 59 3 2010 1139 1147 10.1109/tvt.2009.2038707
- Zhang , Chen , and Vahidi Ardalan Route preview in energy management of plug-in hybrid vehicles Control Systems Technology, IEEE Transactions on 20 2 2012 546 553 10.1109/tcst.2011.2115242
- Heppeler , Gunter , Sonntag Marcus , and Sawodny Oliver Fuel efficiency analysis for simultaneous optimization of the velocity trajectory and the energy management in hybrid electric vehicles IF AC Proceedings Volumes 47 3 2014 6612 6617
- Koehler , Stefan , Viehl Alexander , Bringmann Oliver , and Rosenstiel Wolfgang Optimized recuperation strategy for (hybrid) electric vehicles based on intelligent sensors Control, Automation and Systems (ICCAS), 2012 12th International Conference on 218 223 IEEE 2012
- Hu , Jia , Shao Yunli , Sun Zongxuan , Wang Meng , Bared Joe , and Huang Peter Integrated optimal eco-driving on rolling terrain for hybrid electric vehicle with vehicle-infrastructure communication Transportation Research Part C: Emerging Technologies 68 2016 228 244
- Wood , Eric , Burton E. , Duran A. , and Gonder J. Appending High-Resolution Elevation Data to GPS Speed Traces for Vehicle Energy Modeling and Simulation National Renew Energy Lab 2014
- Safeera , N. , and Chitharanjan K. Survey on Intelligence Based Electric Vehicle Control Strategies
- Mayyas , A. , Prucka , R. , Pisu , P. , and Haque , I. Chassis Dynamometer as a Development Platform for Vehicle Hardware In-the-Loop “VHiL” SAE Int. J. Commer. Veh. 6 1 257 267 2013 10.4271/2013-01-9018
- Mayyas , Abdel Raouf , Prucka Robert , Haque Imtiaz , and Pisu Pierluigi Model-based automotive system integration: using vehicle hardware in-the-loop simulation for an integration of advanced hybrid electric powertrain International Journal of Electric and Hybrid Vehicles 5 3 2013 215 232 10.1504/ijehv.2013.057606
- Yuan , Zou , Teng Liu , Fengchun Sun , and Peng Huei Comparative study of dynamic programming and Pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle Energies 6 4 2013 2305 2318
- Pérez , Laura V. , Guillermo R. Bossio , Moitre Diego , and García Guillermo O. Optimization of power management in an hybrid electric vehicle using dynamic programming Mathematics and Computers in Simulation 73 1 2006 244 254 10.1016/j.matcom.2006.06.016
- Sinoquet , Delphine , Rousseau Gregory , and Milhau Yohan Design optimization and optimal control for hybrid vehicles Optimization and Engineering 12 1 2 2011 199 213 10.1007/s11081-009-9100-8
- Lopp , Sean , Wood Eric , and Duran Adam Evaluating the Impact of Road Grade on Simulated Commercial Vehicle Fuel Economy Using Real-World Drive Cycles NREL (National Renewable Energy Laboratory (NREL) 2015