The rotating electrical machine industry is known for the optimal development of solutions through the experience of manufacturers, and its competitiveness is based on serial production that amortizes initial design efforts. The new concept industry 4.0 is a great opportunity to improve the competitiveness in a global market for small-medium size electric machinery companies. Therefore, it is mandatory these companies adopt new technologies offering customized products fulfilling the customers' requirements according to their investment capacity. To support the aforementioned issue, a new Knowledge-Based System (KBS) model for electrical machine design is introduced. This model socalled GSMWV2 stands for the steps of Gather, Standardized and Model the knowledge with the Wrapper process and the two Verein Deutscher Ingenieure (VDI) methodologies integration VDI-2221 and VDI-2206. It comprises the electrical machine development methodology and the knowledge integration process. This paper will conceptually describe this model and the integration of current industrial technologies such as Model-Based Systems Engineering (MBSE), Project Lifecycle Management (PLM), Project Data Management (PDM), multiphysics and analytic tools co-simulation with KBS tools. This architecture will be able to manage and automate tasks in the development process of electrical machines with emphasis on the design process.

The applications using electric motors have increased in the last decade. Some of these applications encounter the need for tailor-made motors that must meet demanding requirements. Therefore, the specification stage of an electric motor is a critical part of its development. If this stage is properly addressed, then future failures in the development process can be avoided. This paper presents a requirement engineering framework to support small-medium electric motors designers/manufacturers with the development of their product. The framework identifies the stakeholders and the tasks that they should undertake to finish a successful requirements specification stage. The framework is made from the designer/manufacturer’s perspective and it emphasizes the derivation of specialized requirements (lower-level). The result of the framework is well-defined requirements that form the design requirements specification of the motor that leads to the beginning of the design stage.

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process.

Finite Element Method (FEM) analysis tools are the most adopted in the design of brushless alternating current motors due to the advantage of considering multi-physics effects with dependencies of variables such as cross-coupling, saturation and others that are not possible to be modeled analytically with high precision. During the design process designers compute operation points such as maximum torque per ampere or flux weakening characteristics that cannot be targeted directly on the FEM tool. Therefore, designers make a sweep of simulations and post-processed the data in order to obtain the results, this is repetitive particularly in the conceptual phase of the design where features of the motor are still not defined. This paper presents nine algorithms as an alternative to compute with iterative methods operation points that cannot be targeted directly on a FEM tool. The algorithms must be coupled to the FEM tool and can compute complex points such as the characteristic current and modes of operations limits within acceptable range of error and times of execution for practical purposes. Validation of the algorithms using Jython is presented with results for the three types of brushless motors (non-salient, interior permanent magnet and reluctance motor).

This paper presents an electromagnetic design alternative using the so-called banana-shaped magnets, achieving magnet weight reduction of 21.82% for the same performance as the baseline motor in the case study. Typically, parallel magnetization is used in this topology because it provides the manufacturing advantage of not building a specific magnetizing device required to achieve radial magnetization. Therefore, as its main contribution, this paper proposes using tilted magnetization, demonstrating that the performance is better than the conventional way of applying parallel magnetization in banana-shaped motors but still using parallel magnetization. Because of the performance improvement for the same motor structure, magnet weight reduction is possible for a new design with the same performance as the baseline motor. In addition, this paper presents the workflow followed to obtain the less rare-earth design motor candidate using tilted magnetization.

A sensorless online temperature estimator is presented in this paper, which estimates the temperature using a novel signal injection strategy. This allows to eliminate the temperature sensors in the machine, as well as their faults, increasing the system reliability. A double dead-time DC signal is injected in the machine, adding a controlled offset in the control drive through the inverter. The proposed strategy eliminates the effect of the dead-time in the injected signal, which is an important drawback in DC injection strategies for resistance estimation. Furthermore, additional hardware is not needed. The strategy has been implemented in an inverter-fed railway traction induction machine. The proposed algorithm has been validated in a real test-bench.