General Answer to Questions about Modelica Implementation Details

[JBRDLR]

Dear Participants,

Thank you for your high interest in the modeling of the MVC 2023 Modelica model. One of the main hurdles that a control engineer faces when developing an energy management algorithm (EMA) is the lack of high-fidelity model of the vehicle and components. Motivated by this issue, the organizers of the VTS Challenge 2023 decided to only provide a high-level description of the physical models employed in the simulation. We are not planning to disclose the modeling details of all components, e.g. equations and parameters. If you are using a model-based EMA, you are encouraged to apply system identification (or adaptive) techniques to “reverse engineer” the model of the vehicle. If you are using data-driven approaches (e.g. model-free reinforcement learning), then you should be able to treat the vehicle as a “black box”, without knowledge of the physical model.
Good luck!
Best
@imeb, @tobolar, @rpintodecastro , @JBRDLR