These systems combine electrical, thermal, and mechanical domains, and their optimization requires methods that consider not only individual components but also the overall system behavior.
Our work addresses several interconnected challenges. For electrical machines, the emphasis lies on improving efficiency, robustness, and dynamic performance under variable operating conditions. On the system level, optimization techniques integrate these components into complex networks where multiple objectives—such as efficiency, emissions, lifetime, and resilience—must be balanced.
Model-based and data-driven approaches are combined to capture the interactions between domains. Simulation and co-simulation tools link machine models with network dynamics, allowing detailed analysis of transient behavior, load scenarios, and fault conditions. We explicetly expand this up to the individual multiphysics of electrical machines. Optimization strategies include multi-objective formulations, predictive control, and adaptive scheduling to ensure safe and efficient operation.