EXPLORING THE FUEL ECONOMY POTENTIAL OF ISG HYBRID ELECTRIC VEHICLES THROUGH DYNAMIC PROGRAMMING

  • Ao, G.Q. (Institute of Automotive Electronic Technology, Shanghai Jiao Tong University) ;
  • Qiang, J.X. (Institute of Automotive Electronic Technology, Shanghai Jiao Tong University) ;
  • Zhong, H. (Institute of Automotive Electronic Technology, Shanghai Jiao Tong University) ;
  • Yang, L. (Institute of Automotive Electronic Technology, Shanghai Jiao Tong University) ;
  • Zhuo, B. (Institute of Automotive Electronic Technology, Shanghai Jiao Tong University)
  • Published : 2007.12.01

Abstract

Hybrid electric vehicles(HEV) combined with more than one power sources have great potential to improve fuel economy and reduce pollutant emissions. The Integrated Starter Generator(ISG) HEV researched in this paper is a two energy sources vehicle, with a conventional internal combustion engine(ICE) and an energy storage system(batteries). In order to investigate the potential of diesel engine hybrid electric vehicles in fuel economy improvement and emissions reduction, a Dynamic Programming(DP) based supervisory controller is developed to allocate the power requirement between ICE and batteries with the objective of minimizing a weighted cost function over given drive cycles. A fuel-economy-only case and a fuel & emissions case can be achieved by changing specific weighting factors. The simulation results of the fuel-economy-only case show that there is a 45.1% fuel saving potential for this ISG HEV compared to a conventional transit bus. The test results present a 39.6% improvement in fuel economy which validates the simulation results. Compared to the fuel-economy-only case, the fuel & emissions case further reduces the pollutant emissions at a cost of 3.2% and 4.5% of fuel consumption with respect to the simulation and test result respectively.

Keywords

References

  1. Ao, G. Q., Zhong, H., Yang, L., Qiang, J. X., and Zhuo, B. (2006). Fuzzy logic based control for ISG hybrid electric vehicle. Intelligent Systems Design and Applications, ISDA '06. 6th Int.Conf., 1, 274−279
  2. Baumann, B. M., Washington, G., Glenn, B. C. and Rizzoni, G. (2000). Mechatronic design and control of hybrid electric vehicles. IEEE/ASME Trans. Mechatronics 5, 1, 58−72
  3. Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming. Princeton University Press. New Jersey
  4. Bertsekas, D. (2002). Lecture Slides on Dynamic Programming. Based on Lectures Given at MIT. Cambridge. Massachusetts
  5. Bertsekas, D. P. (2005). Dynamic Programming and Optimal Control. 3rd edn. Athena Scientific. New Hampshire
  6. Chan, C. C. (2002). The state of the art of electric and hybrid vehicles. Proc. IEEE 90, 2, 247–275
  7. Cho, B. and Vaughan, N. D. (2006a). Dynamic simulation model of a hybrid powertrain and controller using cosimulation Part I: Powertrain modeling. Int. J. Automotive Technology 7, 4, 459−468
  8. Cho, B. and Vaughan, N. D. (2006b). Dynamic simulation model of a hybrid powertrain and controller using cosimulation-Part II: Control strategy. Int. J. Automotive Technology 7, 7, 785−793
  9. CNASTC (2005). Test methods for energy consumption of high-duty hybrid electric vehicles. Chinese National Automobile Standardization Technology Committee. Standard No: GB/T 19754-2005
  10. Delprat, S., Guerra, T. M. and Rimaux, J. (2002). Control strategies for hybrid vehicles optimal control. Proc. 56th IEEE Vehicular Technology Conf., 3, Vancouver, Canada, 1681−1685
  11. Demirdöven, N. and Deutch, J. (2004). Hybrid cars now, fuel cell cars later. Science, 305, 974 https://doi.org/10.1126/science.1093965
  12. Galdi, V., Ippolito, L., Piccolo, A. and Vaccaro, A. (2001). Multi-objective optimization for fuel economy and emissions of HEV using the goal-attainment method. Proc. 18th Int. Electric Vehicle Symp., Berlin, Germany
  13. Itagaki, K., Teratani, T., Kuramochi, K., Nakamura, S., Tachibana, T., Nakao, H. and Kamijo, Y. (2002). Development of the Toyota mild-hybrid system (THS-M). SAE Paper No. 2002-01-0990
  14. Johnson, V. H., Wipke, K. B., and Rausen, D. J. (2000). HEV control strategy for real-time optimization of fuel economy and emissions. SAE Paper No. 2000-01-1543
  15. Kaoru, A., Shigetaka, K., Shigemasa, K., Hiromitsu, S. and Yoshio, Y. (2000). Development of integrated motor assist hybrid system: development of the 'Insight', a personal hybrid coupe. SAE Paper No. 2000-01-2216, Government/Industry Meeting, Washington, D.C., USA
  16. Lin, C. C., Zoran, F., Wang, Y. S., Loucas, L., Peng, H., Assanis, De., and Stein, J. (2001). Integrated, feed-forward hybrid electric vehicle simulation in SIMULINK and its use for power management studies. SAE Paper No. 2001-01-1334
  17. Lin, C. C., Peng, H., Grizzle, J. W. and Kang, J. M. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Tran. Control Systems Technology 11, 6, 839−849
  18. Mohebbi, M., Charkhgard, M. and Farrokhi, M. (2005). Optimal neuro-fuzzy control of parallel hybrid electric vehicles. Vehicle Power and Propulsion. IEEE Conf. 7, 9, 26−30
  19. Perez, L. V., Bossio, G. R., Moitre, D. and Garcia, G. (2006). Optimization of power management in an hybrid electric vehicle using dynamic programming. Mathematics and Computers in Simulation 73, 1−4, 244−254
  20. Pu, J. H., Yin, C. L. and Zhang, J. W. (2005). Fuzzy torque control strategy for parallel hybrid electric vehicles. Int. J. Automotive Technology 6, 5, 529−536
  21. SAE J1711 (1999). Recommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles. SAE Int
  22. Schouten, N. J., Salman, M. A. and Kheir, N. A. (2002). Fuzzy logic control for parallel hybrid vehicles. IEEE Tran. Control System Technology 10, 3, 460–468