Motivated by real use cases from Bosch for optimization-based functions and controllers in the context of energy optimal operation of vehicles and buildings, we investigate the usage of FMI for optimization purposes via the open-source tool CasADi. We implemented a framework in Python to automatically compare optimization results and computation times of optimal control problems in CasADi. We are able to compare the results generated by different implementations: a) including the system dynamics as FMUs and b) a native implementation where the system dynamics is realized by Python code for CasADi. We present results from two use cases: the trajectory following of a single track vehicle model and the optimal control of a building’s chiller system. Detailed analysis of the split of the execution time of one optimization run gives valuable insight which kind of FMI function calls or derivatives are competitive and which one have bottlenecks compared to the native solution in CasADi without FMUs.

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.