Plan is develop this in the following steps:

• Point mass implemented in Python, in 3D with a basic “tyre” model to capture induced rolling resistance from slip angle - mostly for me to have some maths practice and understand the basic equations
• Point mass implemented in Modelica - replicating the Python implementation to validate Modelica FMU for the vehicle model, which will make extensions to the vehicle model significantly easier
• Bicycle model implemented in Modelica
• 4-tyre model implemented in Modelica
• more complex stuff all in Modelica, ideally I can reuse the same vehicle model for both QS and dynamic modules, just with different wrappers

At the edge of the GGV envelope if:

• Max throttle input
• Max brake input
• Any tyre has a gradient ratio of 0
  • Note that similarly this can be gradient ratio at a defined limit, or stability metric at a defined limit

Therefore, at the edge of the GGV envelope when the minimum of:

• Throttle max threshold - throttle input
• Brake max threshold - brake input
• FL tyre gradient ratio (both long and lat)
• FR tyre gradient ratio (both long and lat)
• RL tyre gradient ratio (both long and lat)
• RR tyre gradient ratio (both long and lat)

equals 0 → so a root finding algorithm can be used which should be faster than a general minimisation algorithm (unless there’s a possibility for weird edge cases)

PointMass