Electric Machine Data and Efficiency Map Generator
Automated Model, FEM Data and Efficiency Map Generation
A research and software system under development that automates parameterised electric-machine model creation, FEMM analyses and processing of results into operating characteristics and efficiency maps.
The project supports research on data-efficient and physics-constrained generative models for electric machines. The target workflow covers geometry description, computational-model generation, electromagnetic calculation, losses and efficiency maps, driving-cycle evaluation and export of data for machine-learning models.
The system is under active development. Published materials present the architecture and selected demonstration results, not a finished commercial product.
Geometry and TopologiesA canonical FEMM-independent 2D geometry representation, stator and rotor parameterisation, winding configurations and model-validity checks. |
Physics CalculationsAutomation of electromagnetic analyses, d–q quantities, torque, saturation, losses and efficiency maps across a defined operating range. |
ML DataExport of structured geometries, graphs, parameters and results for training physics-constrained generative models. |
One Workflow from Geometry to ML Data
The key feature is consistency between machine parameters, the FEM model, physical results and the representation used by machine-learning algorithms.
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Contact IMEE to discuss the requirements, available data, expected outcome and a suitable scope of work.
DISCUSS YOUR PROJECTEngineering design, research and development, prototyping and optimisation of technical systems. Research test benches, electric machines and drives, and solutions for specialised applications.