The Journal of Engine Research

The Journal of Engine Research

Fuel consumption optimization of a series hybrid electric vehicle utilizing fuzzy logic control

Document Type : Original Article

Authors
Mohsen Moghimi 1
Mohammad Mahdi Teymoori 1
Iman Chitsaz 2
Meysam Salehi 3
1 MSc Student, Mechanical Engineering Department, Isfahan University of Technology, Isfahan, Iran
2 Faculty of Mechanical Engineering Department, Isfahan University of Technology, Isfahan, Iran
3 Calibration department of Irankhodro Powertrain Company, Tehran, Iran
10.22034/er.2022.701022
Abstract
The controller of the hybrid electric vehicle determines the combustion engine start-stop time, the operation points, and regenerative brake energy. The Controlling approach of hybrid electric vehicles controls the amount of needed fuel in every driving situation. In the present study, the thermostat strategy is implemented along with fuzzy logic control and compared to the classic thermostat strategy. The fuel consumption is compared in two different strategies. GT-power and Simulink software are implemented to simulate the series hybrid electric model. The numerical model is compared and validated with experimental data. The validated numerical model calculates the vehicle fuel consumption in the new European driving cycle. Results show that the use of fuzzy logic control reduces the fuel consumption of series hybrid electric vehicle 4 percent compared to the classical control strategy.
Keywords
Optimization
Series hybrid vehicle
Fuzzy logic
Controller
Fuel consumption
[1] C. Mi, M. A. Masrur, and D. W. Gao, Hybrid Electric Vehicles: Principles and Applications with Practical Perspectives, Hybrid Electr. Veh. Princ. Appl. with Pract. Perspect., 2011
[2] A. Brahma, Y. Guezennec, and G. Rizzoni, Optimal energy management in series hybrid electric vehicles, American Control Conference, 2000
[3] L. V. Pérez and E. A. Pilotta, Optimal power split in a hybrid electric vehicle using direct transcription of an optimal control problem, Mathematics and Computers in Simulation, 2009
[4] J. Torres, R. Gonzalez, A. Gimenez, and J. Lopez, Energy management strategy for plug-in hybrid electric vehicles. A comparative study, Applied Energy, 2014.
[5] Shabbir W., Control strategies for series hybrid electric vehicles. PhD thesis, Imperial College London, 2015
[6] H. Yoo, S.-K. Sul, Y. Park, and J. Jeong, System integration and power-flow management for a series hybrid electric vehicle using supercapacitors and batteries, IEEE Transactions on Industry Applications, 2008
[7] S. Di Cairano, W. Liang, I. V. Kolmanovsky, M. L. Kuang, and A. M. Phillips, Engine power smoothing energy management strategy for a series hybrid electric vehicle, American Control Conference, 2011
[8] S. E. G. Mehrdad Ehsani, Yimin Gao, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, 2005.
[9] S.H.Mahyiddin et al., Fuzzy logic energy management system of series hybrid electric vehicle, IET Conf. Publ., 2016
[10] J. Gao, F. Sun, H. He, G. G. Zhu, and E. G. Strangas, A comparative study of supervisory control strategies for a series hybrid electric vehicle, Asia-Pacific Power Energy Eng. Conf., 2009
[11] https://global.nissannews.com/en/releases/.
[12] http://www.woodfieldnissan.com/blog/nissan-set-to-spread-the-note-e-power-across-the-sea/.
[13]https://theicct.org/sites/default/files/publications/EU-PHEV_ICCT-Briefing-Paper_280717
The Journal of Engine Research
Volume 68, Issue 68
Autumn 2022
Pages 23-30

  • Receive Date 04 February 2022
  • Revise Date 19 May 2022
  • Accept Date 19 May 2022