Aircraft electrification is currently the best alternative to address the rising demand for more air transportation and deal with anticipated economic and environmental impacts. Although the all-electric-aircraft (AEA) concept is not yet a feasible solution, the more-electric aircraft (MEA) is gaining significant attention. Electrical systems either partially or entirely replace the large and inefficient hydraulic, pneumatic, and mechanical conventional aircraft actuating systems. The upgrade could also encompass the propulsion system, as in hybrid- and turbo-electric aircraft. This upgrade reduces the aircraft weight, reduces the usage of pollutant fluids, increases fuel efficiency, reduces carbon emissions, and increases aircraft controllability and reliability. This article reviews various application areas of electric machines in electrified aircraft, such as actuation, taxiing, propulsion, and generation. Moreover, it reviews the main types of currently/to be utilized electric machines and the critically required specifications. Finally, a comparison between the different considered machines and potential future research is discussed.

Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.

This work presents a comprehensive review of the developments in using Machine Learning (ML)-based algorithms for the modeling and design optimization of switched reluctance motors (SRMs). We reviewed Machine Learning-based numerical and analytical approaches used in modeling SRMs. We showed the difference between the supervised, unsupervised and reinforcement learning algorithms. More focus is placed on supervised learning algorithms as they are the most used algorithms in this area. The supervised learning algorithms studied in this work include the feedforward neural networks, recurrent neural networks, support vector machines, extreme learning machines, and Bayesian networks. This work also discusses several essential aspects of the considered machine learning algorithms, such as core concept, structure, and computational time. It also surveys sample data acquisition methods and data size. Finally, comparisons between the different considered ML-based algorithms are conducted in terms of electric motor type, dataset inputs and outputs, and algorithm’s structure and accuracy to provide a summary overview of the ML-based algorithms for SRMs modeling and design.

The switched reluctance machine is considered as the simplest machine in terms of the machine structure. The lack of permanent magnets on the rotating part enables the machine to operate at high speeds and reduces the thermal requirements of the machine. However, the double salient structure of the SRMs creates challenges such as high acoustic noise, vibrations and high machine torque ripples. Topology optimization is used in the literature to enhance the dynamic performance of the switched reluctance machines. This paper introduces a new ON/OFF optimization method based on the line search method to overcome the limitations of the conventional annealing-based ON/OFF optimization. The proposed method shows a faster convergence to optimal solutions than the conventional annealing-based ON/OFF method. The paper also compares the performance of the generative optimization and the topology optimization of a 6/14 switched reluctance machine with the proposed method and the conventional method. The two methods are applied to two different design domains of the machine for topology and generative optimization and the results are compared to the results of the annealing-based ON/OFF method. The results show the effectiveness of the newly proposed method.

Silicon carbide (SiC) multi-chip power modules (MCPMs) have emerged as critical components in high-power and high-reliability power electronics due to their superior integration capability and performance potential. However, the inherent structural complexity and intense thermal coupling effects pose significant challenges in thermal modeling and monitoring. This paper provides a comprehensive review of recent advances in thermal modeling and monitoring methods tailored specifically for SiC MCPMs. Thermal modeling approaches are thoroughly analyzed, highlighting their respective advantages and limitations in terms of computational efficiency, accuracy, and applicability. Additionally, various direct and indirect thermal monitoring techniques are discussed, examining their practical implementations, strengths, and constraints. To address existing challenges in thermal modeling and monitoring of SiC MCPMs, future research directions are identified. Challenges and opportunities in thermal modeling and monitoring have been specifically analyzed and discussed for SiC MCPMs with advanced packaging technologies. This review not only summarizes the state-of-the-art methodologies but also identifies potential research pathways to enhance the accuracy and robustness of thermal modeling and monitoring for SiC MCPMs. Beyond thermal modeling and monitoring, this paper also serves as a valuable reference for advancing SiC MCPM research in thermal management, packaging design, and lifetime assessment.

This article introduces a new technique for reducing the time of calculating radial and tangential force density waves of switched reluctance machines (SRMs). The method is based on the finite element (FE) simulation of a fraction of an electrical cycle. The new approach shows that a significant time reduction is achieved as compared to the time required for stator radial force density and the rotor tangential force calculation based on the one mechanical cycle simulation method. As the switched reluctance motors introduce new challenges in aspects such as acoustic noise, vibrations, and torque ripples, the method introduced in this paper will help reduce the time of the optimization process of switched reluctance machines in the design stage to improve the machine performance. The proposed method is applied to radial flux SRMs. Three different topologies were used to show the effectiveness of this technique in different force components with minimal error as compared to the benchmark method based on the FE simulation of one mechanical cycle.