Abstract
Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly nonlinear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the predefined model controller.

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Abstract

Abstract
Storing energy efficiently is one of the main factors of a more sustainable world. The battey management system in energy storage plays an extremely important role in ensuring these systems' efficiency, safety, and performance. This battery management system is capable of estimating the battery states, which are used to give better efficiency, a long life cycle, and safety. However, these states cannot be measured directly and must be estimated indirectly using battery models. Therefore, accurate battery models are essential for battery management systems implementation. One of these models is the nonlinear grey box model, which is easy to implement in embedded systems and has good accuracy when used with a good parameter identification method. Regarding the parameter identification methods, the nonlinear least square optimization is the most used method. However, to have accurate results, it is necessary to define the system's initial states, which is not an easy task. This paper presents a two-outputs nonlinear grey box battery model. The first output is the battery voltage, and the second output is the battery state of charge. The second output was added to improve the system's initial states identification and consequently improve the identified parameter accuracy. The model was estimated with the best experiment design, which was defined considering a comparison between seven different experiment designs regarding the fit to validation data, the parameter standard deviation, and the output variance. This paper also presents a method for defining a weight between the outputs, considering a greater weight in the output with greater model confidence. With this approach, it was possible to reach a value 1000 times smaller in the parameter standard deviation with a non-biased and little model prediction error when compared to the commonly used one-output nonlinear grey box model.

Abstract
Reducing vibration and noise in electrical machines for a given application is not a straightforward task, especially when the application imposes some restrictions. There are many techniques for reducing vibration based on design or control. Switched reluctance motors (SRMs) have a double-saliency structure, which results in a radial pulsation force. Consequently, they cause vibration and acoustic noise. This paper investigates the correlation between the radial force and the skew angle of the stator and/or rotor circuits. We computed the analysis from two-dimensional (2D) transient magnetic finite-element analysis (FEA) of three machine topologies, namely the 12/8 three-phase SRM, the 6/4 three-phase SRM and the 8/6 four-phase SRM. Compared to SRM, these topologies have the same basic dimensions (stator outer diameter, rotor outer diameter, and length) and operate in the same magnetic circuit saturation. The flux linkage and torque characteristics of the different motors are presented. The radial force distributed on the stator yoke under various skewing angles is studied extensively by FEA for the three machines. It is also demonstrated the effect of skewing angles in the reduction of radial force without any reduction in torque production. © 2023, Universidade do Porto - Faculdade de Engenharia. All rights reserved.