This will be a lengthy post on race strategy. I will concentrate on Challenger-class cars, for which the main decision is at what speed to drive (there are also some tactical and psychological issues associated with overtaking, but I won’t get into them here).
The kind of analysis I’m doing assumes that teams have a good predictive model of how their car will perform under various conditions (collecting the data required to build such a model is yet another reason to do lots of testing). I’m using a very simplistic model of a hypothetical car – I only took half a day to build it (in R, of course); a real solar car strategy guy would take months and produce something much more detailed. I’m assuming that aerodynamic drag is the main consumer of energy. Remember that the drag force is:
where Cd is the drag coefficient, A is the frontal area of the car, ρ is the density of air (about 1.2), and v is the speed of the car. The key thing here is that faster speeds burn up more energy.
I’m further assuming a solar model loosely based on the region of the American Solar Challenge, a perfectly flat road (no, that’s not realistic), a race start time of 8:00 AM, and a finish time of 5:00 PM with no rest breaks (no, that’s not realistic either). My hypothetical car has 4 square metres of solar panel with 25% efficiency, and a 5 kWh battery pack which is 80% full at the start of the day. In my first simulation, my car runs at constant speed until 5:00 PM or the battery dies, whichever comes first (solar cars are not intended to run with totally empty batteries). This chart shows what happens:
Initially, the graph is a straight line. The faster you drive, the further you go. But this only lasts up to 73.7 km/h, which takes you a distance of 663 km. Faster than that, and your battery dies before the end of the day, so that you go less far in total. In reality, of course, the driver would slow down before the battery died completely, but I’m not modelling that.
My second chart looks at varying the speed of the car. We start the day at one speed (horizontal axis), gradually speeding up or slowing down to reach a second speed at solar noon (vertical axis), and then slowly shifting back so as to end the day at the start speed:
In the chart, the distance travelled is shown by colour and number. It can be seen that, at least for my hypothetical car, the optimum is fairly forgiving: speeds between about 60 and 85 km/h are OK, as long as they average out to about 73 km/h, which means 653 km travelled. You will also notice that there is no incentive to break the speed limit – the rules have been carefully constructed to ensure that this is true.
In my third chart, I’m assuming a patch of cloud on the road ahead, between 200 km and 400 km from the start. We start the day at one speed (horizontal axis), switch to a second speed once inside the cloud (vertical axis), and then switch to a third speed when leaving the cloud (the third speed is chosen to be whatever works best with the remaining battery charge).
Two vertical stripes are noticeable on the left and right sides of the chart. At 20 km/h, the race day ends before you reach the cloud, so that the choice of “cloud speed” doesn’t actually matter. And at 100 km/h, your battery dies before you reach the cloud, so that the choice of “cloud speed” doesn’t matter either. The optimum is to run at a constant 70 km/h initially, and then to speed up to 75 km/h inside the cloud (so as to get back into sunshine that little bit sooner). If you run at an optimal 63 km/h after leaving the cloud, this lets you cover 619 km.
The final scenario changes the one above to have a moving cloud. This cloud is circular and once again 200 km in diameter, with a centre 300 km away from the start position. The edge of the cloud is initially 210 km from the road, but moving at a rapid 50 km/h towards and across it:
The optimum here is to outrun the cloud: to run at 85 km/h till the cloud is past, and then to drop back to whatever speed gives you a 73 km/h average. Not surprisingly, this gives essentially the same outcome as my first varying-speed example. A solution that is almost as good is to run at 70 km/h initially, and speed up to 80 km/h once inside the cloud. This gives you about an hour and 20 minutes inside the cloud. Slowing down to an optimal 70 km/h after leaving the cloud then lets you cover 642 km.
In real life, outrunning clouds and speeding up slightly through them can both be feasible options – if you have the right prediction software, and if know exactly what the weather is doing (which is the hard part). This is why the strategy guys in the chase car are always so busy!
Nuon’s WSC 2011 chase car (image credit)