Andrea Deflorio

A novel online PMSM parameter identification method for electric and hybrid electric vehicles based on cluster technique

In order to save costs and volume, the torque is calculated based on a machine model instead of measuring it with a torque sensor. Thus, an accurate knowledge of the machines electrical parameters is required for a high performance field-oriented control and torque accuracy. In this work, an innovative method is presented and developed for the online identification and correction of the electrical parameters of electrical machines based on cluster technique. This technique allows for the estimation of all electrical parameters with low computational cost. It uses the information of stator currents, stator voltages, and rotor angular speed. The algorithmic performance has been tested using simulation data of a 75-kW permanent magnet synchronous motor for electric vehicles. The results confirm the high accuracy of the identification method. This approach is applicable to electric and hybrid electric vehicles.

Sensitivity analysis for induction machine manufacturing tolerances: Modeling of electrical parameters deviation

Production processes of induction machines suffer from manufacturing tolerances because of assembly imperfections. This causes undesired deviations in the machines performance and behavior. In particular, the equivalent circuit parameters of the machine are affected. Therefore, the validity of the electrical parameters of a reference machine is doubtful for sample machines. In this work, a sensitivity analysis by design of experiments is carried out over the whole torque-speed characteristic map. This analysis is done in order to investigate the influence of design variables (e.g. air gap) on the electrical parameters. The aim is to show the importance of paying an attention to the deviations of parameters resulting from tolerances, to identify how the parameter deviation trend is, and which geometrical parameters have the most significant impact on the machine parameters. The study is done using finite element method for an induction machine for electric vehicles by changing the design variables in the tolerance band. Analysis results show that a mathematical model, that describes the deviations of electrical parameters, can be extracted.

Sensitivity analysis for induction machine manufacturing tolerances in EV and HEV

Production processes of induction machines suffer from manufacturing tolerances because of assembly imperfections. This causes undesired deviations in the machines performance and behavior. In particular, the equivalent circuit parameters of the machine are affected. Therefore, the validity of the electrical parameters of a reference machine is doubtful for sample machines. In this work, a sensitivity analysis by design of experiments is carried out over the whole torque-speed characteristic map. This analysis is done in order to investigate the influence of design variables (e.g. airgap) on the electrical parameters. The aim is to show the importance of paying an attention to the deviations of parameters resulting from tolerances, to identify how the parameter deviation trend is, and which geometrical parameters have the most significant impact on the machine parameters. The study is done using finite element method for an induction machine for electric vehicles by changing the design variables in the tolerance band. Analysis results show that a mathematical model, that describes the deviations of electrical parameters, can be extracted.