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This paper introduces the FMI 3.0 Layered Standard for Network Communication (FMI-LS-BUS), an extension of the Functional Mock-up Interface 3.0 (FMI 3.0) standard designed to address interoperability challenges in simulating distributed, networked systems, particularly in automotive applications. By leveraging FMI 3.0 features such as clocks, clocked variables, and hierarchical terminals, the standard defines two complementary abstraction layers: Physical Signal Abstraction (High-Cut): Representing physical signal values as clocked variables. Network Abstraction (Low-Cut): Emulates hardware-level bus protocols (e.g., CAN, Ethernet) using FMI 3.0’s clocked binary variables. Aligning with the V-model development process, we demonstrate how these layers address distinct challenges in different design phases: High-Cut supports require- ments engineering and functional testing by simplifying signal exchange during Virtual Electronic Control Unit (vECU) integration. Low-Cut enables later phases of the design validation by replicating network timing and protocol specific properties, such as error handling. The standard’s applicability currently focuses on automotive use cases (e.g., CAN, CAN FD, CAN XL, Ethernet, FlexRay, LIN) but can be extended to industrial au- tomation and IoT, facilitated by its domain-agnostic structure.

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.