VDCWorkbench: A Vehicle Dynamics Control Test & Evaluation Library for Model and AI-based Control Approaches

Abstract

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 twodegree-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.