Simulink is great for expressing control logic visually. It is less great when I want to quickly ask a hundred small questions about a model from the command line. That is where Python earns its keep.
I am not trying to replace the model. I am trying to surround it with cheap checks: expected ranges, input sweeps, exported signal comparisons, calibration diffs, and plots that make bad behavior obvious before it turns into a longer debugging session.
What Python is good at here
Python makes it easy to script repetitive experiments. Generate a set of inputs, run a batch, collect the outputs, and compare the shape of the response against what you expected. The important part is not making an elaborate framework. It is making the next check so easy that you actually run it.
I like using simple CSV or MAT exports, pandas for inspection, and matplotlib when a plot tells the truth faster than a table. For many issues, a rough harness is enough to catch the mistake.
The kinds of checks that pay off
Boundary conditions are the obvious one. What happens at zero speed, low state of charge, max temperature, or a sensor value that is technically valid but suspicious? A model can look clean in the nominal case and still do something strange at an edge.
The other useful check is trend direction. If a calibration change is supposed to reduce a limit or increase a request, the script can verify that across the sweep. It is a small thing, but it catches a surprising number of backwards assumptions.
The goal is trust
The model is still the source of truth. Python is the skeptical friend beside it. It gives me a fast way to ask, "does this behave like I think it behaves?" before I ask a more expensive tool, a dyno, or another engineer's afternoon.