Control And Realization Of Optimal Efficiency Point Tracking For MC-WPT System Based On Fuzzy Algorithm

Magnetic Coupled Wireless Power Transfer technology(MC-WPT)uses magnetic field as the medium to transmit electrical energy.It has received extensive attention and rapid development in recent years for its characteristics of safety?convenience?large transfer power and strong environmental adaptability,especially get attention and application in the electric vehicle and other industries.In the electric vehicle wireless charging system,not only the output voltage or current needs to be stabilized,but the transmission efficiency is also required.The system structure and offset will lead to low efficiency,which will result in energy waste.Therefore,efficiency optimization is of great significance for the design and promotion of electric vehicle wireless charging system.At present,most of the researches on efficiency optimization are discussed from the aspects of coupling mechanism and system topology.The research on control is relatively lacking,and the appropriate control strategy can make the system improve the system performance while keeping its own circuit simple.Therefore,this paper proposes an adaptive control method to achieve the optimal efficiency of the system quickly and stably for the efficiency optimization problem.This paper combines the characteristics of static electric vehicle wireless charging system,and aims to find a control scheme to improve system efficiency,so as to simplify the circuit structure and optimize efficiency under different mutual inductance and load values,and carry out research work.Firstly,this paper compares and analyzes the transmission characteristics,harmonic influence and the existence of optimal efficiency points of several typical resonant compensation topologies in electric vehicle wireless charging systems.Then take the SS control topology as an example to discuss the system control scheme,and summarize the advantages and disadvantages of the commonly used power conversion control,and propose the system efficiency optimization control scheme that the primary side is controlled by the phase shift control to track the minimum input power,and the secondary side stabilize the output voltage or current by adjusting the controllable rectifier duty cycle ?.However,the system has complex coupling characteristics,and traditional control algorithms are not applicable to the system of this paper.Therefore,this paper based on the fuzzy algorithm without precise mathematical modeling to track and control the optimal efficiency point of the system.Under the condition that the output voltage is constant,by analyzing the relationship between the phase shift angle of the full-bridge inverter and the input power when the load and mutual inductance are different,the input and output variables of the fuzzy controller and the membership function of each variable are designed.A control rule table is developed to achieve variable step size tracking for optimal efficiency.In addition,in the electric vehicles static wireless charging system,the mutual inductance of the coupling mechanism is highly variable due to the uncertainty of human operation.In order to achieve better control effect,this paper carried on the optimization design to the fuzzy algorithm,so that the fuzzy algorithm can quickly and stably find the optimal efficiency point when the mutual inductance value is different Then,for the different stages of charging the electric vehicle's power battery,the implementation process of the system control scheme is explained,and the control flow of the primary and secondary side controllers is elaborated.The implementation of the fuzzy algorithm is explained based on the ARM platform.Finally,the MATLAB/Simulink platform is used to build the system simulation model.The optimal efficiency point tracking control simulation is carried out for the system with different charging stages and mutual inductance values.The feasibility of the theoretical method is verified.At the same time,the experimental platform is built and the effectiveness of the control scheme is verified by analyzing the experimental results.