Thermal Analysis of Electric Machines for Combined Stirling Engine-Generator Performance

The performance of an electric machine depends on its ability to resist rising internal temperature and ambient temperature. In particular when it is a combination with a heat engine, it is essential to know the thermal characteristics of the electric machine in connection with its operating environment to decide which type of machine for a better result. This work will make a comparative thermal study of three types of generators namely: the permanent magnet generator (PMSG), the squirrel cage asynchronous generator (SCIG) and the switched reluctance generator (SRG), all driven by Stirling engine. The method involves solving the heat propagation equation to determine the thermal resistance network for each machine. The resolution of the network combined with the finite element method will allow a comparison of the temperature rise and its effect on the performance of each machine. The simulation results show that the temperature of the PMSG windings stabilizes at 430 K while that of the others stabilizes at 373 K and 346 K respectively. However, when comparing the performances for the specifications of this work (i.e., produce minimum electric power of 2kW at low speed generated by the Stirling engine), PMSG is the one that fulfil all the requirements. For the use of this machine for the generator set, it will be necessary to use magnets of types GNS-39EH whose operating temperature is approximately 473K (200 ° C) with magnetic induction of 1.22 T. Keywords: choice of machines, thermal network, Finite Element Method, machine’s performances, Stirling engine.

Thermal Analysis of Electric Machines for Combined Stirling Engine-Generator Performance

The performance of an electric machine depends on its ability to resist rising internal temperature and ambient temperature. In particular when it is a combination with a heat engine, it is essential to know the thermal characteristics of the electric machine in connection with its operating environment to decide which type of machine for a better result. This work will make a comparative thermal study of three types of generators namely: the permanent magnet generator (PMSG), the squirrel cage asynchronous generator (SCIG) and the switched reluctance generator (SRG), all driven by Stirling engine. The method involves solving the heat propagation equation to determine the thermal resistance network for each machine. The resolution of the network combined with the finite element method will allow a comparison of the temperature rise and its effect on the performance of each machine. The simulation results show that the temperature of the PMSG windings stabilizes at 430 K while that of the others stabilizes at 373 K and 346 K respectively. However, when comparing the performances for the specifications of this work (i.e., produce minimum electric power of 2kW at low speed generated by the Stirling engine), PMSG is the one that fulfil all the requirements. For the use of this machine for the generator set, it will be necessary to use magnets of types GNS-39EH whose operating temperature is approximately 473K (200 ° C) with magnetic induction of 1.22 T. Keywords: choice of machines, thermal network, Finite Element Method, machine’s performances, Stirling engine.

Losses in Efficiency Maps of Electric Vehicles: An Overview

In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed.

Artificial Intelligence in Electric Machine Drives: Advances and Trends

This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

Analysis of features of magnetic field of a synchronous electric machine with a multi-phase fractional slot winding in a polyharmonic mode of operation

An analytical model has been developed for calculating magnetic field in a multiphase synchronous electric machine with fractional toothed windings. For this, a harmonic analysis of the distribution functions of the magnetic field of excitation and the magnetic field of the armature reaction was carried out, taking into account the presence of higher harmonic components in the function of the magnetomotive force of permanent magnets, variable magnetic conductivity of the air gap, polyharmonic mode of operation of a multiphase electric machine and a non-sinusoidal law of variation of spatial winding functions. As a result of the analysis, the substantiation is given that in the investigated electric machine a nine-phase winding can extract with the greatest efficiency the harmonic components of the first and third order of a rotating magnetic field to create flux linkage and induce an electromotive force (as well as create a magnetomotive force with prevailing spatial harmonics of the first and third order). In the investigated electric machine, the amplitudes of the working harmonics of the induction of the modulated magnetic field of the armature reaction can be increased due to the modulation of the inoperative harmonics of the magnetomotive force of the armature response by the stator teeth to the first and third order. To check the developed provisions, a magnetostatic vector model of the magnetic field of the investigated electric machine was created. The simulation results confirmed the high efficiency of the developed analytical model for calculating the magnetic field in a synchronous electric machine with fractional toothed windings. The use of such a model will make it possible to reveal most reliably the influence of the geometricparameters of the magnetic circuit and the multiphase winding circuit on the nature of the change in the functions of the magnetic field in the air gap with the lowest time costs in the process of optimizing an electric machine.

Artificial Intelligence in Electric Machine Drives: Advances and Trends

This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.