POWER FLOW ANALYSIS AND APPLICATION SIMULATION OF ELECTROMAGNETIC CONTINUOUSLY VARIABLE TRANSMISSION

With the structure of two air gaps and two rotors, the electromagnetic continuously variable transmission（EMCVT） is a novel power-split continuously variable transmission（CVT）. There are two kinds of power flowing through the EMCVT, one is mechanical power and the other is electric power. In the mean time, there are three power ports in the EMCVT, one is the outer rotor named mechanical power port and the other two are the inner rotor and the stator named electric power ports. The mechanical power port is connected to the driving wheels through the final gear and the electric ports are connected to the batteries through the transducers. The two kinds of power are coupled on the outer rotor of the EMCVT. The EMCVT can be equipped on the conventional vehicle being regarded as the CVT and it also can be equipped on the hybrid electric vehicle（HEV） as the multi-energy sources assembly. The power flows of these two kinds of applications are analysed. The back electromotive force（EMF） equations are illatively studied and so the dynamic mathematic model is theorized. In order to certify the feasibility of the above theories, three simulations are carried out in allusion to the above two kinds of mentioned applications of the EMCVT and a five speed automatic transmission（AT） vehicle. The simulation results illustrate that the efficiency of the EMCVT vehicles is higher than that of the AT vehicle owed to the optimized operation area of the engine. Hence the fuel consumption of the EMCVT vehicles is knock-down.

A neural network-based commutation optimization strategy and drive system design for brushless DC motor

An optimized commutation method based on backpropagation(BP)neural network is proposed to resolve the low stability and high-power consumption caused by inaccurate commutation point prediction in conventional commutation strategy during acceleration and deceleration.This article also builds a complete brushless DC motor drive system based on the GD32F103 micro control unit(MCU),with an Artix-7 XC7A35T field programmable gate array(FPGA)to meet the performance requirements of neural network calculation for real-time motor commutation control.Experimental results show that the proposed optimization strategy can effectively improve the system stability during system acceleration and deceleration,and reduce the current spikes generated during speed chan-ges.The system power consumption is reduced by about 11.7%on average.

Analysis of Permanent Magnet-assisted Synchronous Reluctance Motor Based on Equivalent Reluctance Network Model

In this paper,the equivalent reluctance network model(ERNM)is used to calculate the magnetic circuit of a permanent magnet-assisted synchronous reluctance motor(PMASynRM)and calculate no-load air-gap magnetic field and electromagnetic torque.Iteration method is used to solve the relative permeability of iron core.A novel reluctance network model based on actual distribution of the magnetic flux inside the motor is established.The magnetomotive force(MMF)generated by armature winding affects the relative permeability of iron core,which is considered in the calculation of ERNM to improve the accuracy when the motor is under load.ERNM can be used to measure air-gap flux density,no-load back electromotive force(EMF),the average value of motor torque,the armature winding voltage under load,and power factor.The method of calculating the motor performance is proposed.The results of calculation are consistent with finite element method(FEM)and the computational complexity is much less than that of the FEM.The results of ERNM has been verified,which will provide a simple method for motor design and analysis.

Short-circuit fault-tolerant control for five-phase fault-tolerant permanent magnet motors with trapezoidal back-EMF

When a short-circuit fault occurs in a phase,the faulty phase needs to be removed artificially from the system because of the loss of the capability to generate torque.In this case,both the short-circuit current and phase-loss fault would generate additional torque ripples.In this study,a novel fault-tolerant control strategy is introduced to achieve low torque ripple operation of five-phase fault-tolerant permanent magnet synchronous motors with trapezoidal back electromotive force(FTPMSM-TEMF)in the event of a short-circuit fault.The key concept of this method is to compensate for the torque ripples caused by the short-circuit current and the adverse effect of the phase-loss.Based on the torque expression under fault conditions,the torque ripple caused by the short-circuit current can be offset by injecting a certain pulsating component into the torque expression in the phase-loss condition.This would result in smooth operation under fault conditions.Moreover,to track the fault-tolerant alternating currents,the model of the deadbeat current predictive control is extended and restructured for the fault condition.The effectiveness and feasibility of the proposed fault-tolerant strategy are verified by experimental results.