The traditional approaches in material research and hardware design are insufficient to address the evolving Operation and Maintenance (O&M) demands in contemporary power electronics. Overengineering and data acquisition practices lead to unsustainable costs and reduced profit margins. Digital Twins (DTs), defined as real-time simulation models of physical systems, emerge as promising solutions to meet stringent O&M requirements. In power electronics, DTs offer significant potential in thermal management, crucial for control performance, safety, and system lifespan. This paper aims to analyze the development of computationally efficient and high-fidelity DTs tailored for power electronics applications, emphasizing their predictive reliability. To achieve this goal, the proposed physics-based approach is enhanced by integrating Data-Driven Artificial Intelligence (AI)based techniques. The predictive reliability of the DTs produced through this workflow is then experimentally validated against a power electronic converter designed for induction heating applications. Additionally, the feasibility of real-time execution is demonstrated, affirming the practical applicability of the developed DTs.

The integration of electric motors into various industrial and automotive applications emphasizes the critical necessity for reliable performance and operational efficiency. The advent of advanced digital technologies offers opportunities for predictive maintenance strategies. Digital Twins (DTs), mathematical models simulating a system's physical behavior in real-time, present a transformative approach to enhance real-time monitoring of critical quantities, which is imperative to improve operational efficiency and minimize downtime. In this paper, we explore the feasibility and efficacy of deploying real-time physics-based DTs for condition monitoring in electric motor applications. Particularly, we focus on employing on-the-edge DTs, implemented on low-power onboard microprocessors, ensuring continuous communication with the physical asset for reliable real-time monitoring. The study applies DT technology to a high-voltage high-density Electric Vehicle (EV) motor, assessing its predictive capabilities in a real-world scenario. Results showcase the potential of DTs in revolutionizing condition monitoring, thereby meeting the evolving operational and maintenance requirements of contemporary electric motor systems.

This paper proposes an efficient QP solver for electric motors.