This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data
Technical Paper
2019-01-0161
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data. The K-means clustering method was employed to evaluate the sensitivity of new factors to the energy consumption of PHEV, in which the sensitivity was defined through the Pearson correlation coefficient and covariance. Moreover, an optimal energy management strategy based on the sensitivity results and constrained optimization functions was used to optimize the speed-curve of PHEV. Furthermore, the 100 simulation tests based on MATLAB/Simulink platform were carried out to validate the feasibility of the proposed approach. The results indicate that 60% of the factors extracted from this paper show a strong sensitivity to the energy consumption of PHEV and the optimization method costs less computational time, leading to a better energy consumption performance. The proposed methodology is valuable for the research on real-time energy management of electric vehicles and speed control of intelligence vehicles under real-world road conditions.
Recommended Content
Authors
Topic
Citation
Xia, H., Li, T., Wang, B., He, P. et al., "Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data," SAE Technical Paper 2019-01-0161, 2019, https://doi.org/10.4271/2019-01-0161.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 | ||
| Unnamed Dataset 6 | ||
| Unnamed Dataset 7 | ||
| Unnamed Dataset 8 |
Also In
References
- Wirasingha , S.G. and Emadi , A. Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles [J] Vehicular Technology, IEEE Transactions on. 60 1 111 122 2011
- Park , J. and Park , J.H. Development of Equivalent Fuel Consumption Minimization Strategy for Hybrid Electric Vehicles [J] Int. J. Automot Technol. 13 5 835 843 2012
- Huang , Y.J. , Yin , C.L. , and Zhang , J.W. Design of an Energy Management Strategy for Parallel Hybrid Electric Vehicles Using a Logic Threshold and Instantaneous Optimization Method [J] Int J Automot Technol. 10 4 513 521 2009
- Phillips A.M. , Jankovic M. , Bailey K.E Vehicle System Controller Design for a Hybrid Electric Vehicle [C] Control Applications, Proceedings of the 2000 IEEE International Conference on. 2000 Anchorage, AK 297 302
- Lee , H. , Koo , E. , Sul , S. , and Kim , J. Torque Control Strategy for a Parallel-Hybrid Vehicle Using Fuzzy Logic [J] Industry Applications Magazine, IEEE 6 6 33 38 2000
- Lin , C. , Peng , H. , Grizzle , J.W. , and Kang , J. Power Management Strategy for a Parallel Hybrid Electric Truck [J] Control Systems Technology, IEEE Transactions on 11 6 839 849 2003
- Montazeri-Gh , M. , Poursamad , A. , and Ghalichi , B. Application of Genetic Algorithm for Optimization of Control Strategy in Parallel Hybrid Electric Vehicles [J] Journal of the Franklin Institute. 343 4-5 420 435 2006
- Salmasi , F.R. Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends [J] Vehicular Technology, IEEE Transactions on. 56 5 2393 2404 2007
- Gao , D.W. , Mi , C. , and Emadi , A. Modeling and Simulation of Electric and Hybrid Vehicles [J] Proceedings of the IEEE. 95 4 729 745 2007
- Lin C.C. , Peng H. Grizzle J.W. A Stochastic Control Strategy for Hybrid Electric Vehicles [C] American Control Conference, Proceedings of the 2004 2004 Boston, MA 4710 4715
- Baumann , B.M. , Washington , G. , Glenn , B.C. , and Rizzoni , G. Mechatronic Design and Control of Hybrid Electric Vehicles IEEE/ASME Transactions on Mechatronics 5 1 58 72 2000 10.1109/3516.828590
- Chan-Chiao , L. , Huei , P. , Grizzle , J.W. , and Jun-Mo , K. Power Management Strategy for a Parallel Hybrid Electric Truck IEEE Transactions on Control Systems Technology 11 6 839 849 2003 10.1109/TCST.2003.815606
- Sivak , M. and Schoettle , B. Eco-Driving: Strategic, Tactical, and Operational Decisions of the Driver that Influence Vehicle Fuel Economy [J] Transport Policy 22 3 96 99 2012
- He , X. , Wu , Y. , Zhang , S. , Tamor , M.A. et al. Individual Trip Chain Distributions for Passenger Cars: Implications for Market Acceptance of Battery Electric Vehicles and Energy Consumption by Plug-In Hybrid Electric Vehicles Applied Energy 180 650 660 2016
- Lã¡Rusdã Ttir , E.B. and Ulfarsson , G.F. Effect of Driving Behavior and Vehicle Characteristics on Energy Consumption of Road Vehicles Running on Alternative Energy Sources International Journal of Sustainable Transportation 9 8 592 601 2015
- Wan-you , H. , Yong , C. , et al. The Effect of Driving Manners on EV Energy Consumption [J] Transactions of Beijing Institute of Technology 33 02 144 150 2013
- Ping , J. Investigation of the Driving Cycle Construction of Mixed Roads in the City [D] Hefei University of Technology 2011
- Adak , P. , Sahu , R. , and Elumalai , S.P. Development of Emission Factors for Motorcycles and Shared Auto-Rickshaws Using Real-World Driving Cycle for a Typical Indian City [J] Science of the Total Environment 544 299 308 2016
- Dai Z , Niemeier D , and Eisinger D 2008
- Saleh , W. , Kumar , R. , Kirby , H. et al. Real World Driving Cycle for Motorcycles in Edinburgh [J] Transportation Research Part D Transport & Environment 14 5 326 333 2009
- Wang , Q. , Huo , H. , He , K. et al. Characterization of Vehicle Driving Patterns and Development of Driving Cycles in Chinese Cities [J] Transportation Research Part D Transport & Environment 13 5 289 297 2008
- Amirjamshidi , G. and Roorda , M.J. Development of Simulated Driving Cycles for Light, Medium, and Heavy Duty Trucks: Case of the Toronto Waterfront Area [J] Transportation Research Part D 34 1 255 266 2015
- Larsen , R.J. and Marx , M.L. An Introduction to Mathematical Statistics and Its Applications [M] Prentice-Hall 1981
- Macqueen , J. Some Methods for Classification and Analysis of Multivariate Observations [C] Proc. of Berkeley Symposium on Mathematical Statistics and Probability 281 297 1965
- Wang , B. , Xu , M. , and Li , Y. Study on the Economic and Environmental Benefits of Different EV Powertrain Topologies [J] Energy Conversion & Management 86 86 916 926 2014