Tech download: IndyCar fuel saving strategies

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Tech download: IndyCar fuel saving strategies

Insights & Analysis

Tech download: IndyCar fuel saving strategies


ED’s note: Welcome to the first installment of a new series of IndyCar technical deep-dives that we’ll be running through the winter. Over the coming weeks we’ll be shining the spotlight onto all sorts of topics related to making an IndyCar go fast, many of which are rarely discussed in detail outside of engineering debriefs.

To help explain them we’re pleased to have Stan Sandoval, who was dealing with these very concepts as an IndyCar engineer until quite recently, and who was first introduced to readers last year when he wrote a column about how a shot-in-the-dark letter to our late colleague Robin Miller helped set the ball rolling on his own career in motorsport. If you missed it the first time around, you can find it here.


Fuel saving is discussed during practically every IndyCar broadcast, and it becomes the primarily focus of in-race strategy several times a season. On the surface, it may seem counterintuitive that there is a strategic benefit to not pushing 100% at all times, but the truth is that fuel saving is a powerful strategic tool that teams turn to in many different situations.

The strategic goals of every team are the same in every race: achieve the best finishing position possible. Clearly, teams are choosing to fuel save because it helps them finish higher up the order, but what situations call for fuel saving, and why is it sometimes better than pushing flat-out? While fuel saving is prevalent in IndyCar, the motivations and techniques for doing so are often hidden from fans in the name of preserving a competitive advantage.

Why do teams save fuel?

1. To remove a stop

Probably the most obvious reason to fuel save is to make less pit stops. This is the old fable of the tortoise and the hare: is it faster to sprint the whole race and stop more often, or jog the whole race and stop less? There are lot of factors that go into deciding whether eliminating a pit stop by fuel saving is the ideal course of action for a race.

A very basic example can be used to compare strategies, and then complexity can be layered onto this model later on. Say the race is 100 laps: there is only one tire compound, there is no deg, and there are only two cars. It won’t be the most exciting race ever on TV (you could argue it wouldn’t be the most boring, either), but it does allow the variables to be controlled so that there is a direct compare between a flat-out strategy and a fuel saving approach.

In this race, pushing to the absolute maximum uses 0.74 gallons each lap and achieves a 60s lap time. The time lost making a pit stop is 35 seconds, and the fuel tank is a standard 18.5 gallons. Looking at the options, the obvious choice is to push the whole race, do 25 laps in each stint, and stop three times (the hare). The other option is to fuel save in order to only stop twice (the tortoise). This means going 33 to 34 laps per stint, which will only allow 0.54 to 0.56 gallons per lap to be used for the whole race. This consumption becomes the ‘fuel target’ for doing a two-stopper.

So there are two options on the table, but now a decision needs to be made as to which one is faster. This is where engineers can compare the strategies by effectively racing them against each other mathematically. By adding together the time it takes to do all of the laps and all of the pit stops for a given strategy, engineers can predict which car would finish in front. In this example, the three-stopper does 100 laps at 60 seconds and makes three 35-second pit stops, for a total race time of 6,105 seconds.

The two stopper also does 100 laps, but at a different lap time (since it has to hit the fuel target, they’re going to go slower), and only makes two 35-second pit stops. During a practice session in the buildup to this race, the driver is able to achieve a lap time of 60.2 seconds while hitting the fuel target of 0.54 gallons per lap. To compare the two strategies, engineer will often visualize how the race will play out. Below is a typical strategy graph showing lap number vs. total race time. Note that while total race time would normally just count up and up as the race goes on, engineers will usually plot total race time relative to some average to make the graph more readable.

In these graphs, the vertical spacing between the two strategies tells the gap on track on that lap. (Think of it as the difference in total race time between two strategies when they’ve completed the same lap). The upward slopes of the plot shows lap time (steeper is faster), because it’s change in the total race time per lap. The sudden downward drops in time are pit stops: 35 additional seconds lost in a single lap in order to change tires and add fuel. This basic model has constant lap time all throughout the stint, which isn’t particularly realistic.

Each lap that the three-stopper completes is the same 60s lap time and each lap for the two-stopper is the 60.2 second lap time that the driver was able to do in practice. The difference in pace between the two cars is represented visually by the difference in slope. It’s pretty evident right from the get-go that the three-stopper has better pace and begins to pull away. From this graph, it says that even though the three -topper is faster than the two-stopper throughout the whole race, the three-stopper is not able to make up the time lost by making an additional pit stop. In this example, the tortoise wins by 15 seconds.

From this rudimentary model, engineers can begin to add complexity to consider other variables and factors to make the model more representative of reality. If the model needs to consider tire wear, that effect can be included by making each lap 0.05 seconds slower than the previous lap until they pit for new tires. Because the two-stopper and the three-stopper have different stint lengths, the deg will affect the two strategies differently, just as it does in reality. The two-stopper, with its longer stints, is going to suffer more deg.

Rerunning the race with tire deg gives a similar graph, but with a key difference: both cars slow down as the stint goes on. This is because as the tires wear out, the lap time gets slower, as shown by the slopes getting less steep as the stint goes on (and then resetting when new tires are put on). With the model now considering tire deg, the result is different: because the two-stopper has to deal with more deg as a result of its longer stints, the three-stopper is eventually able to catch the two-stopper and make the pass with 10 laps to go. When considering tire deg, the hare wins.

As the model continues to consider other factors, it can eventually incorporate all facets of strategy: tire compounds, traffic, cautions, and whatever else an engineer thinks is relevant when numerically modelling all of the considerations that go into a race strategy. These models can become hugely complex since so much can happen in a race. The situations can be endlessly complicated and no model is perfect. Regardless of the model complexity though, the overall conclusion is the same: if fuel saving in order to eliminate a pit stop appears to be the best way to finish in front, then teams will employ a fuel saving strategy. Sometimes, they do this right from the drop of the green flag.

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