Industrial simulation has long been fragmented by proprietary tool ecosystems. Engineers working on a production line might need a dedicated tool for mechanical simulation, another for electrical behaviour, and yet another for thermal analysis, with no easy way to combine their outputs. The Functional Mock-up Interface (FMI) addresses this challenge by defining a standardised container format for exchanging dynamic simulation models between tools. Within the SPEAR project, FMI served as the backbone for energy model distribution and integration.
The FMI standard, maintained by the Modelica Association, defines a common interface that enables any compliant tool to export and import simulation models as Functional Mock-up Units (FMUs). Each FMU is essentially a self-contained package containing model equations, parameter definitions, and optionally its own solver. Over 270 simulation tools now support FMI, making it one of the most widely adopted interoperability standards in engineering simulation.
Why FMI Mattered for Energy Modelling
For the SPEAR consortium, adopting FMI was not merely a technical convenience but a strategic decision. The project involved partners using different modelling environments across five countries. German partners relied on specific virtual commissioning tools, Swedish researchers worked with physics engines, and Portuguese teams used custom demand-response frameworks. Without a common exchange format, combining these diverse contributions into a unified platform would have required extensive custom integration work for each tool pairing.
By packaging energy models as FMUs, the consortium ensured that a model created by one partner could be imported and executed by any other partner's tools without modification. A behavioural model of a welding robot's energy consumption, developed using one simulation environment, could be seamlessly combined with a conveyor system model from a completely different tool. This interoperability was essential for building complete production line simulations from individual component models.
Component-Level Precision at Scale
The FMU approach enabled what might be called "component-level precision at production-line scale." Each component, whether a servo drive, a pneumatic actuator, or a heating element, was modelled individually with high fidelity. These component-level FMUs were then composed into larger system models that represented entire production stations or lines. The FMI standard's support for co-simulation meant that each component could maintain its own solver and time-stepping logic while still participating in a coordinated overall simulation.
This bottom-up approach yielded significantly more accurate energy forecasts than top-down estimation methods. Instead of applying generic energy profiles to broad categories of equipment, the platform could predict the exact energy draw of specific machines under specific operating conditions. The difference in accuracy was substantial enough to enable optimisations that simpler models would have missed entirely.
Beyond the Project
The consortium's experience with FMI also contributed back to the broader standardisation ecosystem. By pushing the boundaries of what the standard could handle in terms of energy-specific modelling and real-time co-simulation, the project identified areas where future versions of FMI could be enhanced. Several partners continued their involvement in FMI-related standardisation activities after the project concluded, helping to shape the evolution of the standard based on practical industrial experience.
For organisations considering similar energy optimisation initiatives, the SPEAR experience offers a clear lesson: investing in open standards pays dividends. The initial effort of packaging models in FMI-compliant formats was repaid many times over through reduced integration costs, broader reusability of models, and the ability to leverage a growing ecosystem of compatible tools. As the manufacturing sector accelerates its digital transformation, standards like FMI will continue to serve as critical enablers of cross-company and cross-tool collaboration.