Research Of Ant Colony Optimized Control Strategy For Hybrid Electric Vehicle

Oil is a non-renewable energy sources. Some data indicate that the current proven oil reserves, according to the current rate of extraction, use just enough of the world 50-60 years. Since 2003, rising oil prices, the world has entered a period of high oil prices, oil supply and demand contradiction between the increasingly acute energy supply security has become a major problem faced by many major industrial countries. In comparison, the hybrid car is all new energy vehicles in the technology is relatively mature, relatively broad prospects for the development of new energy vehicles, its ease energy shortages, reduce environmental pollution is of great significance. While hybrid vehicles have now begun a preliminary mass production, but its related core technology is still need to improve, especially the control strategy to be further improved and enhanced.Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with the electric motor adding to the traditional vehicle power system. For the vehicle real driving cycle seems to be uncertain, the dynamic driving cycle will have an impact on control strategy’s energy-saving effect. In order to better adapt dynamic the driving cycle, control strategy should have the ability to recognize the real-time driving cycle, and adaptively adjust to the off-line optimal control parameters according to the recognize result. In this paper, four types of representative driving cycle are constructed based on the actual vehicle operating data and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is used to search the optimal control parameters’charge and discharge equivalent factors’for each type of representative driving cycles.At last, the construction of four typical standard conditions as model input, and the control parameters charge and discharge equivalent parameters are adjusted by the ant colony optimization algorithm, while the algorithm’s termination condition is satisfied the objective fuel consumption which is calculated by the model will be minimized under the corresponding driving cycle, The optimal control parameters and driving cycle recognition part are added into the hybrid electric bus Simulink model to test the control strategy’s performance. The test cycle used in the process of simulation is the Dalian cycle, the simulation results verify the effectiveness of the proposed control strategy and fuzzy recognition accuracy of the algorithm.