## How Hills Affect Racecar Braking

Depending on whether you are driving uphill or downhill, **hills affect racecar braking.**

If you are racing at a hilly track then not only is the driving tricky 😎 but it can also make your **data analysis** difficult too.

This article explains *what* is going on and *how* you can account for hills in your racing car data analysis.

**Contents**hide

## Effects of Gravity

**Gravity makes a “contribution**” when you are braking on a hill.

You might intuitively know that you can brake *later* into an uphill corner. Similarly, when you are going downhill you might know you need to brake *earlier*.

**But how do you know IF you are maximising ALL of your racecar’s braking potential?**

**And, how can you work out how well you have you performed in order to go faster?**

## Braking Data Analysis

It is reasonable to assume that you will perform better in some braking zones than others.

What would be great is to **know how well you actually performed** in each braking zone.

With that information you know **what** you need to do and can prioritise **where** you can improve.

As you may know, there is a method for assessing braking performance using data analysis. A method you can use even if you do not have specific sensors on your braking system.

With this method you look at the **longitudinal acceleration** of your racing car, specifically under braking.

* Longitudinal acceleration* is the fore-aft “g-force” you feel when you hit the brakes (or throttle.)

These days, even the most basic datalogging systems can record your racing cars acceleration. It can look like this:

With a few pointers, you can use this recording of your racing cars longitudinal acceleration for things like:

- assessing
**braking points**, - looking at your braking
**consistency**, and - understanding
**how hard you are braking**.

As a result, longitudinal acceleration can be a really useful data channel.

But there is more …

## The Generalised Theory

You can also **generalise the longitudinal acceleration numbers** to give you *more-of-a-clue*–*about* **where** you are losing lap time on the brakes.

This general theory says:

*You should be able to achieve the SAME amount of maximum braking deceleration at EVERY corner on the track. *

This simple “rule” can be very informative once you start to look at your own data.

The idea is that by looking at the **PEAKS **of longitudinal acceleration around the lap, you can:

**quickly assess a racing drivers braking performance**, AND**put a plan together for improving next time out.**

Perfect.

## What Is “Better” Braking?

Before going further, by “improve” braking what I mean is find places where the racing driver can **brake later and harder**.

In case it is not clear **why** braking later is better, it is to do with your speed.

The aim of race car driving is to maximise your speed around the whole lap.

When you brake later, you end up spending *more* of the lap at a higher speed than if you had braked earlier than you need too.

**More time at a higher speed is faster.**

## Peak Acceleration Braking Analysis Example

Have another look at the example longitudinal data below. This time I have annotated it with how this braking theory works:

Negative longitudinal acceleration *typically* represents braking. I have therefore drawn a dotted line at the *peak* maximum longitudinal acceleration in braking.

The theory says that whenever the racing driver is braking **they could (and should) be hitting that maximum peak.** Otherwise, they are slower than they

*could*be.

You can hopefully see that the racing driver hits the peak at two points in the chart – ticked at **3** and **4**. Nice! 😎

You can also *hopefully* see that the rule is also telling us that there are *potentially* chances for the driver to improve. In-particular at points** 1, 2, 5, 6 **and **7**.

**This method therefore gives you specific, objective information that shows you precisely how (and where) you can improve your braking.**

Great news! 😎

**But, **being only a theory, there are lots of cases when this will *not* be the case.

Some of these (including** how hills affect racecar braking**) are noted below:

## Some Caveats To The Brake Analysis Theory

- If you run a racing car with a lot of
**downforce**, then your braking deceleration will be effected by how fast you are going. The faster you go the more braking potential you have because of the downforce (and drag) created by your racecar.

- Not all of the track will have the same
**track surface**everywhere. Therefore there could be different levels of grip at different corners. Without the same grip levels, this means your racing car cannot be expected to slowdown at the same rate everywhere. Knowing the track surface and condition around the lap is therefore useful to note if you do a track walk …

- This theory works best in areas of
**high “threshold” braking**, like braking for a hairpin after a long straight. For other areas, such as more flowing sections of track, you would use other approaches – perhaps that look at a combination of longitudinal and lateral forces.

**Hills.**When you go up a hill, gravity will be helping you to slow down. When you go downhill it will be working against you. This is why you can brake*later*going uphill than you would be able to on a flat track. Equally, why you need to brake*earlier*going downhill compared to taking the same corner on a flat track.

These caveats can be significant enough that you cannot reliably use the theory and that is a real shame as it is such an easy one to look at.

**What can you do?**

The effect of the hills on braking performance is one that comes up quite regularly in discussion.

I therefore had a look at what you *might* be able to do with your data to compensate. The outcome of my investigation is below.

## Compensating For How Hills Affect Racecar Braking **Analysis**

**Analysis**

The goal was to modify the longitudinal acceleration figures so that, no matter how hilly the race track, **you could still use the peak longitudinal acceleration theory for your braking assessment**.

As with anything engineering (it seems!) once you look into it a bit, it rapidly becomes more complex than you initially anticipated. Adding to the above caveats are the (potential) effects of weight transfer, condition of the brakes (i.e. temperatures), tyre temperatures/pressures and even the rate of brake pedal application.

I decided to keep it simple and just see what came out.

My thought was try something **knowing and acknowledging **there are limitations but with the aim of seeing *if* I could still help you do *something* **useful with the data so many of you have**.

## Firstly What Is Actually Going On?

I will keep this simple too:

**Gravity sneaks into your racecar’s acceleration measurements on hills.**

Gravity is 9.81m/s (typically, depending on where on the earth you are … Engineers = zzzzz 🙂 )

Gravity acts vertically and more commonly, it is referred to in “g’s.”

When driving your racing car **on a flat race track this means that gravity does not really get involved in your driving**. Gravity simply acts vertically to keep you on the tarmac.

The thing is, to keep us all on the ground we experience “1g” all the time.

**1g is quite strong**. It is the equivalent to the *maximum* longitudinal acceleration under braking for nearly *any* road car and many *many* racing cars.

What that means is that **gravity is more than strong enough to affect racecar braking on hills.**

The effect of gravity, on your racing cars longitudinal acceleration, *increases* with the angle of the hill you are driving on.

**The steeper the hill the more gravity’s effect. **The flatter the hill the less its effect.

As a result, the acceleration measurements of your racing car datalogger will be affected by gravity when you are on a hill.

On hilly tracks this then makes using the general theory (about looking for peaks of opportunity in the longitudinal acceleration braking trace) less robust.

However, *if* you know the slope of the hill, then **what could you do**?

It kind of becomes a Maths challenge to simply work out the acceleration your race car is “un-usefully” measuring due to gravity **and correct for it.**

So that is what I set out to do.

## Maths Bit

Below is a sine curve:

My approach to the maths challenge above is to take the Sine of the slope angle (i.e. your race track’s hills) and multiply that by Gravity.

That *should* give you the “un-useful” contribution gravity is making to your measurements and enable you to correct for it … hopefully!

**I have used the sine wave (SIN) to determine the proportion of gravity** that acts on your racing car based on **the angle** of your race tracks’ hill:

Proportion of Gravity Caused by Hills = Gravity x SIN (Slope Angle)

The diagram below aims to show this a bit more clearly (aims too 🙂 ):

The proportion of “un-useful-to-our-measurements” gravity is on the vertical axis. The hill angle is on the x-axis. There are some example calculations at 90 and 20 degrees slope angle.

Clearly 90 degree hills are a bit extreme, but you can see that in a range of plus or minus 20 degrees you get well over half a “g” effect.

Many *many* tracks have slope angles of 10 or more degrees. These slopes are also often into the braking zones. Hopefully you can see how this could now become significant in your braking assessments.

To work out *the-equivalent-to-driving-on-a-flat-race-track-longitudinal-acceleration*, or, “Longitudinal Acceleration Corrected” then you need to **add** the measured longitudinal acceleration to the proportion of Gravity caused by hills number, like this:

Long Acc Corrected = Long Acc + G Correction

And see if it was worth it …

## Worked Example (in AIM Race Studio Analysis)

Above is the longitudinal acceleration data you saw from earlier in the article. You may recognise that this data is shown in the AIM Race Studio 2 Analysis software. This is popular with many of you and it also has some nice benefits for doing this kind of study. Specifically, it has standard data channels of * GPS_Slope* (the hill angle) as well as

**(your longitudinal acceleration):**

*GPS_LonAcc*AIM Race Studio *also* enables you to write custom “Maths Channels.”

If you are not familiar with these then they are a bit like how you can write an equation in Excel. In this case, it will allow you to take your Longitudinal Acceleration data and your Slope data, and create a NEW data channel based on the Maths section above.

The AIM documentation is a bit … lite … however they have produced this pdf on how to create a Maths Channel if you are not familiar with what this all about.

This is the equation you need to use. I called it: **LonAcc_Corr**

## Pulling this together. **Was It Worth It?**

To test this I was fortunately to have an example data set of a fairly hilly track (thanks Matt):

I created the new Maths channel and then compared the two.

You can see the result of the two data channels next to each other here. Note I have zoomed in on just the braking peaks for clarity:

The Top chart is “Hill Compensated.” The Bottom is the original longitudinal acceleration.

Not a massive difference on first inspection but have a *closer* look. Below I have annotated the two charts to help you:

There is quite a lot going on here. But in simple terms it **looks like the hill compensated longitudinal acceleration is DEFINITELY worth you looking at.**

I will explain …

### Worked Example – What is going on?

In the original data on the bottom chart (“*No Hill Compensation*“) the peaks are quite inconsistent. Except for the two I identified before (at points 3 and 4), there looks like there is a lot of opportunity to brake later into a number of other corners.

The “*Hill Compensated*” data **tells a slight different story**. Now that you can see the data without the gravity effect messing with the measurement, it *looks* like the racing driver is much more consistent around the lap.

The maximum peak seems lower* and therefore the racing driver hits the peak at points 3, 4, 5 and 7. This is much better! It leaves only points 1, 2, and 6 as major opportunities for improvement. Particularly points 2 and 6 look a bit worse in the Hill Compensated longitudinal data than the original data, suggesting they need the most attention.

Where this is really useful is in a situation where you were looking at points 5 and 7. In the raw data these look like there is opportunity for improvement. However the Hill corrected data suggests otherwise, so much so that you’d be wasting your time and effort to get improvements in this area.

Useful hey!

** I have taken a bit of “analytics licence” at point 4, because I think there was something else going on here outside the scope of the article but alluded too here if you are curious* …

## Wrapping Up – Hills Affect Racecar Braking

This article results in a short, simple one line equation. As you can see there is a lot going on behind the scenes – even with the simple version!

This is typical of data analysis, including lots of ways this could be “wrong”, but hopefully you can also see **how it could still be useful.**

I thought if you run an AIM datalogger and you are struggling with your braking analysis, especially at a hilly track, you might want to give this a go.

The aim always with analysis is to help steer your understanding **so you can prioritise the biggest areas for most improvement first.** This suggestion aims to do just that for your braking.

For something so simple to implement it might help your race drivers braking performance a lot.

As ever, I am keen to hear form readers who give this a go!

Just sign-up to the newsletter on the form below as it would be great to hear from you. Oh, and as a subscriber you also get** instant access to a growing Vault of invaluable motorsports resources**, such as spreadsheets, course, handouts and (much) more … so if motorsports is your *thing* its worth it anyway 😎

Best wishes as ever!

## Further Reading

Are your maximising ALL your grip? Find out how to know for sure –> https://www.yourdatadriven.com/maximising-your-racing-cars-grip/

Where are you gaining or losing time on the lap? Delta-t will tell you –> https://www.yourdatadriven.com/introduction-to-motorsport-data-analysis-delta-t/

Need a super simple explanation of Understeer and Oversteer? This should help those debrief conversations –> https://www.yourdatadriven.com/racing-talk-understeer-vs-oversteer-basic-explanation/