Accurate modeling of electrical drives for online testing is a relevant problem, because of their nonlinear behavior. Efficient modeling for simulation, performance evaluation, and testing must consider accurate as well as simple models. This paper proposes the application of auto-tune methods to identify equivalent Hammerstein models, where the nonlinear process is approximated by a static nonlinear element followed by a linear dynamic second or third-order model. The effectiveness of the presented procedure is first verified by simulation results, showing that Hammerstein models overcome the limitations inherent to small-signal linearizations. A standard implementation of such technique considers a relay adjustment for attempts in a heuristic way. In this paper, two innovations are proposed: the relay adjustment is automatically shifted and the method is applied for complex electric drives. Experimental results are shown in the case of a drive constituted by a DC/AC inverter supplying a single-phase induction motor and of a step-down chopper.

The computational cost of topology optimization based on binary particle swarm optimization is greatly reduced by the use of Deep Neural Networks (DNN). A first Convolutional Neural Network (CNN) is trained with data coming from Finite Element Analysis (FEA) with the aim of correctly estimating the output quantity (a motor torque in the proposed case study). A second CNN is trained to give as output the boundary conditions (BC, expressed in terms of fields or potentials) to be used as BC of a reduced Finite Element Method (FEM) model, created in order to still be able to give the correct value of the output quantity. In the optimization phase, the torque properties are evaluated by the trained CNN, and only a reduced percentage of cases are re-evaluated by either the full FEM model or the reduced FEM model. The overall computational time of the optimization procedure is significantly reduced.

The pantograph-overhead contact wire system is investigated by using an infrared camera. As the pantograph has a vertical motion because of the non-uniform elasticity of the catenary, in order to detect the temperature along the strip from a sequence of infrared images, a segment-tracking algorithm, based on the Hough transformation, has been employed. An analysis of the stored images could help maintenance operations revealing, for example, overheating of the pantograph strip, bursts of arcing, or an irregular positioning of the contact line. Obtained results are relevant for monitoring the status of the quality transmission of the current and for a predictive maintenance of the pantograph and of the catenary system. Examples of analysis from experimental data are reported in the paper.

In this paper an innovative active pantograph for high-speed trains is proposed. The results presented are based on extensive simulation tests. The parameters used in the simulation are those of a real pantograph for high-speed trains: the pantograph model is modified by adding a wire actuation, in order to exert a constant contact force between the moving pantograph and the overhead contact wire. A wire-actuated control and contact force observers are proposed as effective solutions in the case of a possible implementation.

In this article, a novel axial flux double-stator switched reluctance motor (AFDSSRM) is presented and optimized for electric vehicle applications. AFDSSRM adopts the axial arrangement of double-stator and inner rotor structure with a full-pitch winding configuration. The flux generated by the two stators cancels each other at the unaligned position, and then, a low unaligned inductance barely affected by saturation is achieved, which is the primary advantage of the AFDSSRM. First, the topology and power equation of the motor are presented briefly. Due to a large number of dimensional parameters of the proposed structure, comprehensive sensitivity analysis is used to classify the design parameters into strong- and weak-sensitive classes, and a multilayer optimization approach is adopted for the variables of both classes. The response surface method combined with the multiobjective genetic algorithm is employed to optimize the strong-sensitive variables, while the Taguchi algorithm is applied to optimize the weak-sensitive variables. Moreover, the 3-D finite element model is established to analyze the electromagnetic characteristics of the motor. Finally, a prototype motor is manufactured, and the experimental results verify the effectiveness of the proposed structure and the optimization method.