Research On Dual Motor Drive System And Its Pareto Control Strategy

In the global energy shortage today,non-renewable fossil fuels less and less,the development of new energy to promote a revolution in all walks of life,including our transportation industry,new energy research and development of electric vehicles have also been vigorously supported by the world,How to maximize the power of the case,so that the maximum battery life,we are pressing to solve the problem.For electric motors with dual motors,in the process of single and double motor switching,the actuator of the brake can respond quickly and accurately to the motor to switch the signal,and whether the motor is stable in different working modes before and after switching,the study of this series of multi-objective problems is even more important for the motor-driven continuous output characteristics.The specifics of this article are as follows:(1)In the dual motor drive system,the matching of the motor on the 1st and the 2nd motor,the selection of the energy source,and the matching of the transmission ratio(2)According to the theory of automobile,the model of double motor drive system is established and analyzed,the coupling method of torque coupling and torque coupling is analyzed,to find the best way for dual motor drive coupling---speed coupling.(3)In the design to meet the dual motor-driven Pareto control strategy,our first thought is the use of intelligent algorithms,proposed a particle swarm algorithm,prior to this,we have a more comprehensive theory of the particle group algorithm to tear down the process,verify the superiority of the particle swarm algorithm.(4)For dual motor switching process,we generally require the smoothness of the double motor during the switching process and the fast response,here the general particle swarm algorithm is difficult to meet,so the design of the improved particle swarm optimization algorithm to meet the switching requirements.(5)Through improved particle swarm optimization,we are in MATLAB simulation,get more optimized results,the improved particle swarm optimization algorithm can solve the multi-objective problem.