This article focuses on increasing the computational efficiency of 3D multi-static magnetic finite element analysis (FEA) for electrical machines (EMs), which have a magnetic field evolving in 3D space. Although 3D FEA is crucial for analyzing these machines and their operational behavior, it is computationally expensive. A novel approach is proposed in order to solve the magnetic field equations by exclusively using the magnetic scalar potential. For this purpose, virtual variable permanent magnets (vPMs) are introduced to model the impact of the machine’s coils. The effect on which this approach is based is derived from and explained by Maxwell’s equations. To validate the new approach, an axial flux machine (AFM) is simulated using both 2D and 3D FEA with the magnetic vector potential and current-carrying coils as a reference. The results demonstrate a high level of agreement between the new approach and the reference simulations as well as an acceleration of the computation by a factor of 15 or even more. Additionally, the research provides valuable insights into meshing techniques and torque calculation for EMs in FEA.

Various approaches already exist for identifying the Jiles-Atherton (JA) hysteresis model, typically using optimization algorithms that minimize the deviation of hysteresis curves from a reference trajectory to approximate suitable parameters. However, depending on the implementation of the JA model and the optimization algorithm, this approach can be computationally intensive. Therefore, a data-driven approach based on the Gaussian process regression (GPR) is investigated. Using numerical simulations, B(H) curves of toroidal core samples are initially generated. Hysteresis and eddy current effects are considered through the use of electromagnetically coupled Maxwell equations and the JA model. By varying JA parameters, core geometry, and electrical excitation, a dataset of B(H) loops for different materials and different test configurations is created. The resulting dataset serves as input for GPR models which predict JA parameters and electrical conductivity based on hysteresis curves and boundary conditions. The GPR models achieves strong regression performance with $R^{2}$ values of up to 0.999. Overall, this approach can reliably estimate the JA parameters and the conductivity of the material based on the hysteresis curve as well as the geometrical and electrical boundary conditions. The estimation of the parameters occurs with minimal temporal delay and is universally applicable.

The increasing volatility of the markets and the growing demand for customized products are challenges for future production to ensure maximum flexibility and adaptability. This paper introduces software-defined value stream process systems (SVPSs), a novel approach that extends the concept of software-defined manufacturing into autonomous, reconfigurable production systems. SVPSs establish a cyber–physical chain that links product features to requirements, enabling their fulfillment through modular machine and process hardware. A modular construction kit of individually combinable hardware and associated software modules is presented. These modules are coordinated via a digital process chain that enables holistic simulations, optimizations, and planning based on a Digital Twin. This system is based on software-defined manufacturing but extends it into autonomous reconfigurable machines. By enabling virtual planning and commissioning of entire production lines, the SVPS concept provides an efficient and adaptable solution to meet the demands of volatile markets.

For the efficient simulation and optimization of electrical machines, the soft magnetic behavior of core materials is typically represented by the magnetization curve rather than complex hysteresis models, and hysteresis losses are calculated and accounted for in post-processing instead. Currently, either interpolation methods or phenomenological magnetization models are used to describe the magnetization curve. However, the existing methods exhibit several disadvantages concerning identification, comparability, implementation, and the resulting behavior of the nonlinear solver. Based on these identified disadvantages, a set of criteria for magnetization models are developed. An analysis of the state of the art reveals that the existing methods do not meet these criteria. Therefore, three novel models are proposed to advance the state of the art: a simple algebraic model, a special arctangent model, and a special hyperbolic tangent model. To evaluate these models, the Langevin function, which describes anhysteretic behavior based on a physical approach in models, such as the Jiles-Atherton (JA) model, is used. The new methods are fitted to the anhysteretic Langevin functions of different materials and show excellent agreement in regression as well as improved computational efficiency compared to the Langevin function. The best models reach R-squared values exceeding 0.99 for the four reference materials. In particular, the algebraic and arctangent models exhibit excellent regression performance, and beyond, the algebraic model achieves outstanding computational efficiency, rendering them viable alternatives to the existing models for magnetization curves in technical simulations.

The increasing volatility of the markets and the growing demand for customized products are the challenges for the production of the future to ensure maximum flexibility and adaptability. This paper presents the development of a cyber-physical chain linking product features to requirements and their fulfillment in machine and process hardware to form a modular, reconfigurable production system. The conception and realization of universal production systems is investigated. A modular construction kit of individually combinable hardware and associated software modules is presented. These modules are coordinated via a digital process chain that enables holistic simulations, optimizations, and planning based on digital twins. This system is based on software-defined manufacturing but extends it into autonomous reconfigurable machines. The virtual planning and commissioning of entire production lines aims to achieve efficient and adaptable production in volatile markets.