FUZZY TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

  • PU J.-H. (Institute of Automotive Engineering, Shanghai Jiao Tong University) ;
  • YIN C.-L. (Institute of Automotive Engineering, Shanghai Jiao Tong University) ;
  • ZHANG J.-W. (Institute of Automotive Engineering, Shanghai Jiao Tong University)
  • Published : 2005.10.01

Abstract

This paper presents a novel design of a fuzzy control strategy (FCS) based on torque distribution for parallel hybrid electric vehicles (HEVs). An empirical load-regulating vehicle operation strategy is developed on the basis of analysis of the components efficiency map data and the overall energy conversion efficiency. The aim of the strategy is to optimize the fuel economy and balance the battery state-of-charge (SOC), while satisfying the vehicle performance and drivability requirements. In order to accomplish this strategy, a fuzzy inference engine with a rule-base extracted from the empirical strategy is designed, which works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the driver torque request between the engine and the motor. Simulation results reveal that compared with the conventional strategy which uses precise threshold parameters the proposed FCS improves fuel economy as well as maintains better battery SOC within its operation range.

Keywords

References

  1. Baumann, B. M., Washington, G., Glenn, B. C. and Rizzoni, G. (2000). Mechatronic design and control of hybrid electric vehicles. IEEE/ASME Trans. on Mechatronics 5, 1, 58-72 https://doi.org/10.1109/3516.828590
  2. Delprat, S., Guerra, T. M. and Rimaux, J. (2002). Control strategies for hybrid vehicles: optimal control. Proc. the 56th IEEE Vehicular Technology Conference, Vancouver, Canada, 3, 1681-1685 https://doi.org/10.1109/VETECF.2002.1040502
  3. Ehsani, M., Gao, Y. and Butler, K. L. (1999). Application of electrically peaking hybrid (ELPH) propulsion system to a full-size passenger car with simulated design verification. IEEE Trans. on Vehicular Technology 48, 6, 1779-1787 https://doi.org/10.1109/25.806770
  4. Galdi, V., Ippolito, L., Piccolo, A. and Vaccaro, A. (2001). Multiobjective optimization for fuel economy and emissions of HEV using the goal-attainment method. Proc. the 18th International Electric Vehicle Symposium, Berlin, Germany
  5. Gerhardt, J., Honninger, H. and Bischof, H. (1998). A new approach to functional and software structure for engine management systems - BOSCH ME7. SAE Paper No. 98P-178(49)
  6. Jalil, N., Kheir, N. A. and Salman, M. (1997). A rule-based energy management strategy for a series hybrid vehicle. Proc. the American Control Conference, Albuquerque, New Mexico, USA, 689-693
  7. 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
  8. Kimura, A., Abe, T. and Sasaki, S. (1999). Drive force control of a parallel-series hybrid system. JSAE Review 20, 3, 337-341 https://doi.org/10.1016/S0389-4304(99)00017-X
  9. Koo, E. S., Lee, H. D., Sul, S. K. and Kim J. S. (1998). Torque control strategy for a parallel-hybrid vehicle using fuzzy logic. Proc. the 1998 IEEE Industry Applications Conference, St.Louis, MO, USA, 1715-1720
  10. Lee, H. D., Sul, S. K. (1998). Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle. IEEE Trans. on Industrial Electronics 45, 4, 625-632 https://doi.org/10.1109/41.704891
  11. Lin, C. C., Filipi, Z., Wang, Y. et al (2001). Integrated, feed-forward hybrid electric vehicle simulation in Simulink and its use for power management studies. SAE Paper No. 2001-01-1334
  12. Lin, C. C., Peng, H., Grizzle, J. W. and Kang J.-M. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Trans. on Control Systems Technology 11, 6, 839-849 https://doi.org/10.1109/TCST.2003.815606
  13. Paganelli, G., Ercole, G., Brahma, A., Guezennec, Y. and Rizzoni, G. (2001). General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Review 22, 4, 511-518 https://doi.org/10.1016/S0389-4304(01)00138-2
  14. Paganelli, G., Guerra, T. M., Delprat, S., Santin, J.-J., Delhom, M. and Combes, E. (2000). Simulation and assessment of power control strategies for a parallel hybrid car. Proc. Institution Mechanical Engineers, Part D: J. Automobile Engineering 214, 7, 705-717 https://doi.org/10.1243/0954407001527583
  15. Passino, K. M. and Yurkovich, S. (1998). Fuzzy Control. Addison-Wesley, Menlo Park, California
  16. Pu, J. H., Yin, C. L., Zhang, J. W. and Ma D. Z. (2004). Modeling and development of the control strategy for a hybrid car (in Chinese). J. Shanghai Jiaotong University 38, 11, 1917-1921
  17. Schouten, N. J., Salman, M. A. and Kheir, N. A. (2002). Fuzzy logic control for parallel hybrid vehicles. IEEE Trans. on Control Systems Technology 10, 3, 460-468 https://doi.org/10.1109/87.998036
  18. Schouten, N. J., Salman, M. A. and Kheir, N. A. (2003). Energy management strategies for parallel hybrid vehicles using fuzzy logic. Control Engineering Practice 11, 2, 171-177 https://doi.org/10.1016/S0967-0661(02)00072-2
  19. Tong, Y., Ouyang, M. and Zhang, J. (2003). Real-time simulation and research on control algorithm of parallel hybrid electric vehicle (in Chinese). Chinese J. Mechanical Engineering 39, 10, 156- 161 https://doi.org/10.3901/JME.2003.10.156
  20. Wipke, K. B., Cuddy, M. R. and Burch, S. D. (1999). ADVISOR 2.1 : a user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans. on Vehicular Technology 48, 6, 1751-1761 https://doi.org/10.1109/25.806767