The ability to systematically compare and evaluate diverse control strategies is essential for the development of effective control algorithms in autonomous driving. This contribution presents the VDCWorkbench Modelica Library, a unified platform designed to support the development, testing, validation and verification of vehicle dynamics controllers and energy management strategies. The presented library is an extension of the IEEE VTS Motor Vehicle Challenge 2023 models and offers multi-physical component modeling, including a hybrid energy storage system (fuel cell & hydrogen tank and battery with aging model), as well as vehicle dynamics control for autonomous driving research projects. Two path-following approaches are featured: an open-loop lateral controller with a static inversion of a single-track model, and a closed-loop state-dependent geometric path-following controller with static control allocation. The library may also serve as the foundation for development of vehicle control methods, such as two-degree-of-freedom control approaches concepts. One example is given for this combination of a feedforward controller with residual reinforcement learning, where a learned agent improves the performance of the open loop controller. The entire library will be released as open source on GitHub in September 2025.

Mitigating periodic oscillations (e.g. in rotating systems) is a common control engineering problem. Fast Fourier Transform (FFT)-based methods are well-suited for respective analysis. While FFT algorithms inherently assume signal periodicity, rotating systems often exhibit true periodic behavior (e.g., shaft rotation frequencies). Using angle-sampled data rather than time-sampled data allows direct analysis of oscillations relative to rotational cycles, which is particularly useful for tracking unbalance or periodic external excitation in rotating assemblies. Modelica provides several built-in resources to address these challenges. First of all, inverse models have the potential to derive an ideal control signal in time domain. For periodic disturbances, this ideal control is likely to be approximated well by a periodic, i.e. Fourier-transformable signal. Modelica is an appropriate model environment to store and retrieve tabled FFT data depending on operating conditions such as rotational speed. In a real-time application, synthesizing control signals from precomputed Fourier tables offers a practical alternative to executing potentially complex inverse models online, reducing computational effort and system complexity. The paper demonstrates this approach using the example of mitigating oscillations induced by an internal combustion engine in a hybrid automotive power train.