Adaptive Particle Swarm Optimization Algorithm for Optimal Matching of Automobile Transmission System Parameters
In order to improve the power of the car and reduce the fuel consumption of the car,a multi-objective optimization matching method of transmission system parameters is proposed.Based on the mechanical transmission system,the fuel consump-tion of 100 kilometers and the acceleration time of(0～100)km/h are respectively optimized sub-objectives to construct the vehicle dynamics model and the economic model by setting different dynamic constraint indicators,the weighting coefficient is introduced Method and penalty function,the parameter optimization model of the vehicle transmission system under multiple working condi-tions is established.In order to improve the matching degree of drive train parameters,an improved adaptive particle swarm opti-mization algorithm based on dynamic learning factor and adaptive adjustment of inertia weight strategy is proposed to obtain the optimal set of vehicle drive train parameters.The simulation results show that the improved algorithm converges faster and is more"active",and it avoids the"premature convergence"of the algorithm.Compared with the traditional adaptive algorithm,the fuel consumption per 100 kilometers under the six-cycle working condition It is reduced by 1.5％,(0～100)km/h acceleration time is shortened by 2.3％,and the top speed is also increased by 0.53％.These results fully verify the reliability and effectiveness of the im-proved adaptive particle swarm algorithm.
万方数据
维普
