Accurate modeling and simulation are essential for the effective design, testing, and evaluation of electric machine systems. However, existing models often face interoperability challenges due to differences in programming languages (e.g., C, MATLAB, Python) and the separation of components such as inverters and controllers across diverse environments. These challenges are amplified by the growing use of advanced simulation platforms like Hardware-in-the-Loop (HIL) and Controller-HIL, which require repeated adaptations for compatibility. This paper presents a HELICS-based co-simulation framework that enables seamless coordination among heterogeneous tools and models, providing a unified platform for integrating and testing electric drive models regardless of their origin. The approach is demonstrated through the co-simulation of an inverter-fed permanent magnet synchronous machine (PMSM) drive under speed control, showcasing reduced development time, flexible reuse of existing models, and efficient integration into both software and HIL environments offering a scalable, modular solution for collaborative and repeatable electric drive system testing and development.
The concept of Digital Twin (DT) consists of a physical asset, a digital asset, and their bidirectional data exchange, differing the DT from concepts with lower level of integration. Availability of the bidirectional interconnection not only enables monitoring of system states bu…
Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored. Electricity price forecasting (EPF) presents a challenging testbed due to complex temporal dependenci…
This study proposes a precipitation control framework integrating a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC). At each control instant in MPC, a finite-difference sensitivity matrix is constructed from the NWP model and used as a local…
This paper contributes to vehicle dynamics modeling by introducing a physics-informed neural state-space model tailored for the parking regime of a production battery-electric sedan, identified entirely from field-test maneuvers. At parking speeds the model captures what the kine…
Mobile robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers' subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for real-time robot navigation in dynam…
The driveability of a new heavy-truck driveline is traditionally assessed using physical prototypes. Enabling early evaluation of the driving experience in a human-in-the-loop driving simulator using a virtual prototype has the potential to significantly improve development effic…