Project Summary
The loops Loops Optimization sub-team project is focused on building models to analyze whether to take loops during the raceand forecast loops effectiveness and efficiency to our distance-maximizing performance. The loops team is building models for before and during the race.
Project Summary
Each driver will have different driving patterns when put behind the wheel of a solar car, coming in the form of different levels of battery efficiency, velocities, brake patterns, etc. In order for our models to more accurately predict battery efficiency, the driver must be taken into account and some adjustments may need to be made to the model depending on the person behind the wheel.
Goals
Determine the types of data to profile our drivers (may be able to use inspiration from other teams driver profiles)
Graph the data points to show trendlines for each driver
Gain an understanding of areas that our drivers can improve or what our best driver is doing and how our other drivers can improve
Inputs
Driver data points for velocity, time, battery efficiency levels, etc
Outputs
Graphs will likely be velocity vs efficiency or something similar, something that will be able to display a driver’s efficiency based on time and speed
Notes
This requires testing data to be obtained with the car and multiple drivers. Given our history, this probably won’t really be happening and may end up being used live at race during FSGP evaluating drivers over the course of race laps and analyzing the data on the flyneeded to make decisions about which loops to take or not to take
Build a model that determines which loop to take prior to the race starting
Build a model that determines which loop to take based on real-time race conditions as well as how to take the loop (speed, other factors).