Data Analysis Intro

One of the responsibilities of the race engineer is finding the optimal setup for each session out on the track. I utilize data from engine RPM, speed, throttle position, lateral acceleration, and position of each damper to determine what changes need to be made. This task usually takes a few hours of studying the data, looking at past data, and playing with numbers. No one has a few hours to spare at the track, between fixing any broken parts and prepping the car for the next session, I am lucky to have half a hour.

To solve this problem, I am turning to my new found friend called Matlab!
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Tonight I thought I would focus on the first project I coded up almost a year ago. Steering vs. Lateral acceleration plots are extremely useful to quickly determine what the car is doing. Understeer, oversteer, poor damping, and poor driving technique can all be seen (of course I have an eye on my driver, I mean, my setup is always perfect...not)

Steering is on the bottom, the scale is irrelevant since it is just the voltage read from the sensor. Lateral acceleration is on the side, measured in G forces. Each color represents a different corner. This graph shows there is not necessarily a corrilation between more steering angle and more lateral acceleration. Slip angle tire and vehicle characteristics dictate this curve.

You many now be saying "Wow Matt, way to be a giant nerd and reinvent the wheel, most data analysis software has this built in, show us the goods!"

Well this was my first foray into the world of Matlab and without any instruction from school I was quite proud of myself. Going back to the whole "time is money" concept, I needed to automate everything and make it easy to read. In addition, as soon as I got to the track I found this graph view was useless since I had no theoretical reference to see what the car should be doing. So I quickly fit a line to the data as a reference and split the graph up corner by corner, ending up with this:

The blue x's are what the car would be doing in a perfect world, with the red being what the car is doing in that corner. Corner camber, and conditions needed to be taken into consideration when looking at the data. As an example take a look at corner 12. At High Plains Raceway, this is a constant radius, flat corner. Consequently the data should sit right on the theoretical. It is very close, but upon closer examination at the end of the corner the driver is throwing in more steering and the lateral acceleration is way down (the lowest red points of the curve for all of us color and resolution challenged, ie me). This indicates some good understeer, and the next step is to find out why, which is exactly where we are going to leave off for the night, and pick up later!