The Optimization Of Control Strategy For Hybrid Electric Vehicle

Control strategy for hybrid electric vehicle is one of the key technologies. Supported by the major project "Research on Control Algorithm and Basis Technology for Electric Vehicles" under "High-Tech R&D Program of China", the energy management control strategies for series and parallel hybrid electric vehicles have been research. On this basis, a fuzzy logic control strategy has been build in order to distribute the torque between engine and motor reasonably. The simulation results of three cycles UDDS,CHINA and NEDC show that the fuel economy in fuzzy control strategy is better. It improves 9.3%,8.4%and 7.6% respectively compared with the electric assist control strategy. This has laid a good foundation for the application of a fuzzy logic control strategy in the actual hybrid electric vehicles.In this paper, combined the benefits of batteries and ultra capacitors, a hybrid power system has been model and the corresponding control strategy in order to avoid the high current and improve the recovery of braking energy has been research. The simulations of the hybrid power system and the battery power system have been done in the cycles of UDDS and NEDC respectively. The results show that the braking energy recovery rates of the hybrid power system are 71.4% and 81.3%, and that of the battery power system are 43.2% and 68.5%.In view of optimizing for design parameters, considered of performances on hybrid electrical vehicle of power-train parameters and control parameters, the optimizations on both parameters are concurrently performed. A new optimization scheme in which the genetic algorithm is used combined with simulated annealing algorithm is proposed. The optimizations for single-objective function and multi-objective function have been done respectively. Optimization results show that the fuel consumption of the single-objective optimization reduces 9.6% and emissions also decline compared with pre-optimization, and that of the multi-objective optimization reduces 7.1% and emissions of CO, HC and NO_x fell 23.4%, 5.6% and 17.4% compared with pre-optimization.