Figure 2: Optimal laptimes and setup with 10% less track grip

The pace difference between the FWD, RWD and AWD configuration is almost identical after a drop in grip, however the interesting result here is that AWDTV performs even better on a damp track relative to the others and has widened the gap by a further 1.4sec.

If we take a look at optimal weight distribution, we can see that weight distribution is by far the most sensitive parameter, as laptimes deteriorate rapidly when we move away from the ideal setup. At the high grip level, the FWD is quickest with 74% of the weight on the front axle (which explains why we don’t see many rear engine examples!), while Porsche owners will be delighted to see that the RWD is quickest with the weight as far back as 33%. BMW drivers with their 50:50 weight distribution could use this graph as an excuse to upgrade to all-wheel drive technology, as 53–55% is ideal for AWD performance.

The aerobalance optimum looks to be very extreme. FWD and RWD demand very far forwards and rearwards aerobalance respectively. I’ve never driven a car with 84% aerobalance around a high speed corner in the damp, but the thought of it sounds terrifying (maybe this explains why I make cars go quicker through engineering rather than driving!) If we impose a stability constraint, the aerobalance is still really far forward, probably because having to steer and drive the same set of wheels puts all the hard work at the front. If this doesn’t line up with our trackside experience (of 1MW FWD cars!), it’s really easy to adjust the setup. We can do this in the Canopy user interface by dragging the aerobalance rearwards to what we feel is a more acceptable balance and then moving the weight distribution and mechanical balance to their new optimum.

Mechanical balance has the smallest effect, which is good to know as it leaves the race engineer free to adjust this in order to tune the balance to driver preference without having a detrimental effect on ultimate performance.

In the case of the AWD where we have a fixed % front/rear torque split we find that 35% front/65% rear is a good place to start, with steep loss in laptime when we start to move away from this point. The ultimate solution is to have a dynamic front/rear and left/right split, as is the case with AWDTV. Perhaps the most significant result here is that by opting for torque vectoring, not only do we leave the other cars in the dust, but we are much less sensitive to weight distribution, aero balance and mechanical balance. This brings with it two major advantages: 1) it is much easier to design a car when we’re not restricted to keeping the design within a very narrow window 2) it’s probably much easier for the driver to consistently exploit the performance if the car performs well over a wider range of setups.

Now I can hear our RWD fans complaining that the equipment used to run AWDTV with a standard ICE is heavy. By adding weight to this car, we find that it takes 680kg to bring the performance down to the level of the RWD. This partly explains why the Porsche 918 is so quick around the Nordschleife while boasting more most modest headline figures when compared to the current crop of hypercars. With so many corners to get right, drivability has to be prioritised here.

The results seem clear; if you want to go fast and have a car that’s not too sensitive to setup, you need all wheel torque vectoring. In reality that’s easier said than done. Our simulations simply have 4 throttle and brake controls — one for each corner of the car; maybe a realistic aim if we’re making instruments for a one man band, but less practical when we’re trying to drive on the limit for 7minutes at an average speed of 215kph.