Project Description: AI-Assisted Automation of Modeling and Simulation Workflows for Power Electronic and Motor Drive Systems Modern electrical engineering systems particularly in power electronics and motor control rely heavily on high-fidelity modeling and simulation to design, validate, and optimize performance. Tools such as MATLAB and Simulink are widely used to construct plant models, simulate dynamic behavior, and evaluate control strategies. However, despite the availability of these tools, the process of analyzing simulation results remains largely manual, time-consuming, and dependent on expert interpretation. Engineers must repeatedly tune parameters, run simulations, extract relevant signals, compute performance metrics, and interpret system behavior, often across hundreds of experimental scenarios. This workflow creates bottlenecks in productivity and limits scalability, especially when dealing with complex electromechanical systems. This project addresses these challenges by developing an AI-assisted framework for automating the analysis of modeling and simulation workflows for power electronic converters and Permanent Magnet Synchronous Motor (PMSM) drive systems. The core objective is to integrate structured simulation data with an offline large language model (LLM) to create a system capable of interpreting plant model behavior, identifying relationships between system parameters and performance, and assisting engineers in decision-making processes. traditional simulation-based workflows, engineers must manually analyze system responses to changes in plant parameters and control inputs. in a PMSM motor drive system, parameters such as stator resistance, inductance, rotor inertia, and back-EMF constant directly influence system dynamics. Similarly, in power electronic converters such as buck or boost converters, parameters such as switching frequency, inductance, and capacitance determine output voltage ripple, efficiency and transient response.

Our team collaborated with Computer Science & Engineering 46 on this project.