A Closer Look at a Multiphysics Fuel Cell Electric Bus Model
Much has been made recently about the importance of using virtual models in delivering eMobility. Benefits are often talked about in general and holistic terms; reduction of prototypes, digital twins, model-based design and so forth. As one can easily imagine, the accuracy of a model directly influences its usefulness. After all, you need to trust what the model is telling you in place of real world testing. But how does a model achieve that realism? What is the importance of detail and fidelity? What benefits does complexity bring?
To answer that question, we need to understand simulation as a concept. Imagine a single use case and corresponding results dataset; we can quickly, and accurately formulate a model of some description which replicates that specific result. Such a model does not have to be a granular reproduction of all specific elements of the system, rather exhibit the same behavioural results compared to the use case. But we would not have confidence in applying the model to another use case. Without validation and adjustment of the model parameters that is, to satisfy the constraint of replicating enough real-world data sets to encompass all the use cases the model would likely encounter.
e-mobility technology international – Vol 13 – Autumn 2022
If we are building a model to simply replicate the results of real-world testing, then such an approach is satisfactory. We can define our model to behave in a way that we know is valid, therefore can consider our model to be valid as the outcome matches the validation condition. An important and useful concept. One could consider this model to be descriptive, and consistent with the definition of the word simulation.
However, there’s an immediate problem. We need to have results to validate our model against. A bit of a roadblock if we are trying to produce a model that is predictive – i.e. can be exercised without total validation results with confidence. Somewhat counterintuitively, what we need to do is produce a model which is more akin in sprit to an emulation – each element recreated in as much physical detail as possible. By building an overall model from a sum of valid parts, then we can create a predictive model which enables a non-existent physical system to be tested. Complex phenomena can then be observed, as all the combined interactions between components which comprise the system dynamics.
Such an approach can be very useful in the field of eMobility.
Read the full article here: The Devil In In The Detail
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