Abstract
This work proposes a new control framework for power converters with a dual half bridge (DHB) configuration. The new framework exploits the multi-port structure of the DHB to simultaneously: i) regulate the current in the primary side of the DIM and ii) equalize the voltage in the two secondary ports of the DHB. To implement these functions, we combine input-output linearization methods with pragmatic voltage balance algorithms. We then apply this framework to a hybrid energy storage system composed of a battery pack and two supercapacitor modules. Numerical simulation results demonstrate the effectiveness of the proposed approach in regulating the power between the energy storage units and balancing the supercapacitors' voltages.

Abstract
Switched reluctance machines are simple, robust, fault-tolerant and do not use permanent magnets, which makes them a strung candidate fur vehicular propulsion. Despites the advantages they still stiffer from high torque pulsation and acoustic noise, which can be reduced by the controller. In this paper the concern is in having an advanced current control, so it is used the model predictive control (MPC). This requires an accurate model to estimate the future behavior of current and the back-electromotive force (emf) signal is essential. As this signal cannot be directly calculated or measured it is proposed a new algorithm to calculate its estimation in real time. The algorithm is easy to implement and the numerical results show the accuracy of the method, which permits a very low current estimation error in the MPC framework.

Abstract
The design of the switched reluctance machine involves several variables that impact the performance of the machine in different ways. The topology, polar arcs, internal and external diameter, number of turns and core length are some of the parameters that must be determined at the design stage. The effect of geometric parameters on performance is different for each switched reluctance machine topology, so this study is dedicated to establishing a sensitivity analysis for two popular topologies, a three-phase 6/4 and a four-phase 8/6. The performance results are estimated through finite element simulations and the dimensional effects analysis is performed using statistical software. The results show the dimensions with the greatest effect on the average torque value and total losses on the machine and can be useful to support the choices made during the design and optimization of this type of machine.

Abstract
A considerable amount of research within the last few decades has been focusing on controllers for switched reluctance motor drives and how they affect the torque ripple. Despite all its potentials, there are still major concerns and obstacles to overcome concerning the dependency of the magnetic characteristic of the switched reluctance motor. This work targets these concerns by proposing an initial study of the fundamentals of a drive scheme using a finite set model predictive control for a switched reluctance motor through an asymmetric bridge converter. The implementation of this scheme is the main contribution of this paper. The method uses the dynamic model of the motor to estimate the future behavior of the current for each converter state. A cost function then evaluates which switching state minimizes the current error and applies it to the motor. Some simulation results illustrate the technique. Simulation results show the good performance of the method with fast and accurate transient response. © IFIP International Federation for Information Processing 2020.

Abstract
Purpose The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance. Design/methodology/approach In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements. Findings The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model. Originality/value The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

Abstract