Electric drives are a fundamental component in nearly all complex mechatronic systems, consisting of motor, gearbox, encoder, brake and inverter. Their initial design is often based on simplified analytical formulas that provide a rough estimation of the required parameters. However, to ensure the reliability and performance of the overall system, a more detailed validation using physics-based simulations is essential. These simulations can capture dynamic interactions or vary system constraints.
Currently, the evaluation of such simulations is typically performed manually by domain experts. These experts interpret the results, identify potential weaknesses or areas for improvement, and derive recommendations for design. This process is not only time-consuming but also heavily dependent on individual expertise and experience, which can lead to inconsistencies and limited scalability.

Goals:

The goal of this thesis is to develop strategies for the automated evaluation of simulation results in the context of electric drive system design. One approach to consider is fuzzy logic to emulate the kind of imprecise, experience-based decision-making that human experts often rely on. This allows for a more flexible and human-like interpretation of simulation data.
Based on the automated evaluation, the system should then access a formal knowledge base, represented as an OWL ontology, to derive concrete design recommendations. These should guide an iterative improvement of the drive system design.
As part of the thesis, one selected approach will be implemented and tested using an already existing Modelica-based simulation environment, demonstrating the feasibility and effectiveness of the proposed method.

Helpful Prior Knowledge:

• Basic understanding about mechatronic and electric drive systems
• Programming skills
• Lectures Digital Twin Engineering or Cyber Physical Modeling
• Initial experience with Modelica simulations