No description available.

No description available.

Switched Reluctance Motors (SRMs) are gaining recognition due to their robust design, cost-effectiveness, fault tolerance, and reliable high-speed performance, positioning them as promising alternatives to traditional electric motors. However, SRMs face high torque ripples, vibration, acoustic noise, and nonlinear modeling complexities. Through careful geometry design optimization, these drawbacks can be mitigated. Design optimization for SRMs is a multi-objective and nonlinear problem that requires an accurate finite element analysis (FEA) model to relate designable parameters to output objectives. The geometric design process follows a multi-stage and iterative approach, leading to prohibitive computational time until the optimal design is reached. Machine learning algorithms (MLAs) have recently acquired attention in electric machine design. This study introduces an extensive analysis of various MLAs applied to SRM modeling and design. Additionally, it presents a robust framework for a comprehensive evaluation of these MLAs, facilitating the selection of the optimal machine learning topology for SRM design. Existing research on the geometry optimization of SRMs using MLAs has focused only on the machine’s static characteristics. This thesis introduces an advanced optimization method utilizing an MLA to act as a surrogate model for both static and dynamic characteristics of the SRM. The dynamic model incorporates conduction angles optimization to enhance the torque profile. The proposed MLA is applied to map out the SRM geometrical parameters, stator and rotor pole arc angles and their dynamic performance metrics, such as average torque and torque ripples. The optimal design improves the average torque and significantly reduces the torque ripples. Radial forces constitute a critical objective that should be considered alongside average torque, efficiency, and torque ripple in the design optimization of SRMs. Accurate modeling of radial forces is a prerequisite for optimizing motor geometry to mitigate their adverse effects on vibrations and acoustic noise. This work presents an MLA-based surrogate model for the most influential radial force harmonic components, facilitating the integration of radial force reduction into a multi-objective optimization framework. The proposed optimization framework employs two MLA-based surrogate models: the first correlates SRM pole arc angles with average torque and torque ripples, while the second models the most significant radial force harmonics. A genetic algorithm leverages these surrogate models to predict new geometrical parameters that enhance the SRM's torque profile and reduce radial forces. The optimization framework significantly reduced torque ripples and radial forces while slightly increasing average torque. The optimal design candidates were verified using FEA and MATLAB simulations, confirming the effectiveness of the proposed method, which offers significant computational time savings compared to traditional FEA techniques. / Thesis / Doctor of Philosophy (PhD)

Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: This thesis deals with the design and optimisation of medium power traction switched reluctance machines with tapered and straight stator poles. Only the prototype of the tapered stator pole machine is constructed and evaluated in this study. A non-commercial finite element package is used in the design and optimisation of the machines. The finite element method is applied directly in the optimisation procedure to optimise the design of the machines in multi-dimensions. The lumped circuit analysis method is used only for the purpose of verifying some of the finite element calculated. It is not used in the optimisation procedure. The performance characteristics of the tapered and straight stator pole machines are compared and discussed and the tapered stator pole machine is found to have better torque performance. Also the calculated and measured static torque versus rotor position characteristics of the tapered stator pole machine are compared and discussed. / AFRIKAANSE OPSOMMING: Die tesis beskryf die ontwerp en optimering van medium drywing trekkrag geskakelde reluktansie masjiene met tapse en reguit stator pole. Slegs 'n prototipe van die tapse stator pool masjien is gebou en geëvalueer. Die masjiene is ontwerp en geoptimeer met behulp van 'n nie-kommersiële eindige element metode pakket. Die eindige element metode is direk in die optimerings algoritme gebruik vir die optimering van die masjiene in multi-dimensies. Die gekonsentreede parameter stroombaananalise is slegs gebruik om sommige van die eindige element berekenings te verifeer. Die vermoës van die tapse en reguit stator pool masjiene is vergelyk en bespreek. Die resultate toon dat die tapse stator pool masjien se draaimoment vermoë beter is as die van die reguit stator pool masjien. Die berekende en gemete statiese draaimoment teenoor rotorposisie van die tapse stator pool masjien is ook vergelyk en bespreek.

No description available.

No description available.

High-accuracy electromagnetic design and analysis of electric machines is enhanced by the use of various numerical methods. These methods solve Maxwell’s equations to determine the distribution of the electric and magnetic fields throughout the considered machine structure. Due to the complicated architectures of the machines and the nonlinearity of the utilized magnetic materials, it is a very challenging task to obtain an analytical solution and, in most cases, only a numerical solution is possible. The finite element method (FEM) is one of the standard numerical methods for electromagnetic field analysis. The considered machine domain is divided into finite elements to which the field equations are applied. FEM solvers are utilized to develop optimization procedures to assist in achieving a design that meets the required specifications without violating the design constraints. The design process of electric machines involves adjusting the machine parameters. This is usually done through experience, intuition, and heuristic approaches using FEM software which gives results for various parameter changes. There is no guarantee that the achieved design is the optimal one. An alternative approach to the design of electric machines exploits robust gradient-based optimization algorithms that are guaranteed to converge to a locally-optimal model. The gradient-based approaches utilize the sensitivities of the performance characteristics with respect to the design parameters. These sensitivities are classically calculated using finite difference approximations which require repeated simulations with perturbed parameter values. The cost of evaluating these sensitivities can be significant for a slow finite element simulation or when the number of parameters is large. The adjoint variable method (AVM) offers an alternative approach for efficiently estimating response sensitivities. Using at most one extra not-iterative simulation, the sensitivities of the response to all parameters are estimated. Here, a MATLAB tool has been developed to automate the design process of switched reluctance motors (SRMs). The tool extracts the mesh data of an initial motor model from a commercial FEM software, JMAG. It then solves for magnetic vector potential throughout the considered SRM domain using FEM taking into consideration the nonlinearity of the magnetic material and the motor dynamic performance. The tool calculates various electromagnetic quantities such as electromagnetic torque, torque ripple, phase flux linkage, x and y components of flux density, air-region stored magnetic energy, phase voltage, and phase dynamic currents. The tool uses a structural mapping technique to parametrize various design parameters of SRMs. These parameters are back iron thickness, teeth height, pole arc angle, and pole taper angle of both stator and rotor. Moreover, it calculates the sensitivities of various electromagnetic quantities with respect to all these geometric design parameters in addition to the number of turn per phase using the AVM method. The tool applies interior point optimization algorithm to simultaneously optimize the motor geometry, number of turns per phase, and the drive-circuit control parameters (reference current, and turn-on and turn-off angles) to increase the motor average dynamic torque. It also applies the ON/OFF topology optimization algorithm to optimize the geometries of the stator teeth for proper distribution of the magnetic material to reduce the RMS torque ripple. A 6/14 SRM has been automatically designed using the developed MATLAB tool to achieve the same performance specifications as 6110E Evergreen surface-mounted PM brushless DC motor which is commercially available for an HVAC system. / Thesis / Doctor of Philosophy (PhD)

In this thesis, after a comprehensive literature review on different conventional and predictive torque control strategies for switched reluctance motor (SRM) drives, two online methods and one offline multi-objective optimization-based method are proposed to extend the operating speed range of finite control set model predictive torque control (FCS-MPTC) for SRM by adaptively controlling the commutation angles in the entire speed range. Furthermore, a method is proposed to minimize the steady state torque tracking error of FCS-MPTC for SRM drives. The incapability of the conventional FCS-MPTC in controlling the commutation angles, which is considered as one of the main drawbacks of the conventional FCS-MPTC, limits its application for high-speed torque control of SRM drives. The phase turn-off angle is always selected to be close to the aligned position with the conventional FCS-MPTC regardless of the operating speed. However, commutation angle advancement is required for high-speed torque control of SRM drives to limit the negative phase torque resulting from the current tail after the turn-off angle in the generating region. Excessive negative torque with the conventional FCS-MPTC at higher speeds can result in a degraded performance with high rms current, low average torque, high torque ripple, and reduced efficiency. The phase turn-off angle can be adaptively controlled as speed changes with the first online commutation angle control strategy proposed in this thesis. This method is based on predicting the free-wheeling phase current in an extended time interval which is much bigger than the prediction horizon of FCS-MPTC. The second online turn-off angle control method is also proposed by improving the optimality condition defined for determining the optimal turn-off angle. The optimality condition is determined by calculating the work done by the conducting phase after the phase is turned off. The weighting factor of the objective function of FCS-MPTC is kept constant with both proposed online methods. An offline multi-objective optimization-based strategy is proposed to determine the globally optimal turn-off angle and the weighting factor in the entire operating torque and speed ranges. The effectiveness of both proposed online methods and the offline commutation angle control strategy is verified using simulations and experimental results. The results are also compared to the conventional FCS-MPTC and the indirect average torque control with optimized conduction angles which is considered as one of the main conventional torque control strategies for SRM drives. In order to minimize the torque tracking error as a result of either parameter uncertainties or tracking multiple objectives with a single objective function with weighting factors, a method is proposed which is based on updating the reference torque at each sample time by calculating the average torque tracking error in the previous sample times. The validity of the proposed method is verified using simulations. / Thesis / Doctor of Philosophy (PhD)

Cette thèse de doctorat s'inscrit dans un projet pour le développement du véhicule électrique piloté par la société Renault. Il répond aux prévisions d’exploitation de véhicules électriques pour des déplacements interurbains et urbains afin d’améliorer les aspects environnementaux. L'objectif de nos travaux a été de développer un outil capable de prédire le bruit d'origine électromagnétique produit par des machines à rotor passifs : machine à réluctance variable, sur une large plage de vitesse. Pour cela, le développement d’un modèle vibro-acoustique reposant sur les équations aux dérivées partielles permet d’obtenir une bonne estimation des vibrations et du bruit de la machine pour une force donnée. Cette modélisation analytique couplée à un outil éléments finis, permettant ainsi d’estimer précisément les pressions radiales d’origine magnétique, fournit sous forme de sonagramme le bruit de la machine sur une large plage de vitesse. Cette approche dite hybride « numérique et analytique » offre l’avantage d’un très bon compromis temps de calcul – précision afin de concevoir des machines peu bruyantes. Enfin une analyse des effets de la saturation de ces machines ainsi qu’une analyse harmonique par produit de convolution sont fournis. / This thesis is related to the development of an electric car by Renault. This vehicle respects the constraints in order to improve environmental aspects. The aim of this work is to develop a tool capable of predicting electromagnetic noise generated by motors with passive rotor: switched reluctance machine, for a large range of speed.For that, a vibro-acoustic model based on an analytical approach was developed. It gives a good estimation of the vibrations and noise of the machine for a given force. This analytical model is coupled with Finite Element models which allows accurate estimation of radial Maxwell pressure and gives the sonogram of the radiated noise regarding a large range of speed.The advantage of this “hybrid” approach (Finite Element and analytical) is the very good compromise accuracy/computational time in order to design less noisy motors. Finally, an analysis of the saturation effect and harmonic analysis using convolution were performed.