Design of Quantum Genetic Algorithm Based Electric vehicle Traction on Reluctance Synchronous Motor Speed Control Accounting for System identifiablility and VSI Non-Linearity

It was recently presented that the Reluctance Synchronous Motor (RSM) is well suited for variable gear electric as well as for hybrid electric vehicles. This paper deeply investigates the capabilities of a Reluctance Synchronous Motor and proposes a multi parameter estimation scheme for Reluctance Synchronous Motor under variable-speed control. The proposed control evaluation model is fully efficient and has taken into account the estimation and compensation of voltage-source-inverter nonlinearity. The RSM will work under variable speed control, and the proposed estimation model is calculated using the recorded two sets of machine data corresponding to two sets of different rotor speeds. The proposed estimation model can be solved by using a conventional Quantum Genetic Algorithm. The drawback of adopting permanent magnets is the possible demagnetization of the magnets themselves. This can greatly limit the maximum overload capability of the motor, which is a salient requirement of a traction motor. The salient requirement of a traction motor can greatly limit the maximum overload capability of the motor. The Reluctance Synchronous Motor improves its capabilities by avoiding the use of rare-earth permanent magnets.