Electric Racecar (Formula SAE)
When I returned to school after a period of time running my startup Embark, I joined the university's Formula Electric racecar team (FSAE). I quickly became heavily involved and became the engineering / product lead of the team for the newly starting season.
I worked diligently to shift the teams culture to one that will yield better and more reliable results as well as increasing the teams sustainability year to year. I accomplished this though implementing better practices, better tools, and more informed leadership.
I lead the development of the 2018 vehicle's architecture and design along with leading the control algorithms team and accumulator (battery pack) team due to the lack of available subteam leads. This lead me developing many simulation tools to facilitate data driven design on all areas of the team.
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After 8 months on the team, I decided to step back from it to focus on other priorities of mine. I now still provide consulting for the team where needed.
Changing Team Culture
When running my own startup, I developed a deeper understanding about how to build and run an organization of people in an efficient and productive way. I noticed many aspects of the team's culture that I believed were in need of change in an effort to improve the caliber of the team.
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The first major change was to develop a system to retain information from year to year. I built a wiki using Atlassian's Confluence and pushed to build a culture around continuous documentation. With this resource now naturally growing and building, future years will have a significant advantage when diving into the design as a new member.
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To promote the use of confluence, I developed a structure of project outlines and deadlines that live on confluence. Team leads are able to document the requirements of a project which can be easily passed to students who are requesting a project for their school term. Work is documented, tracked and reviewed all in this one central location.
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I also worked to promote better communication through a changed culture around meetings, student involvement / mentorship, and conflict resolution.
2018 Vehicle Design
NOTE: This section will be updated at the end of the 2018 season.
Lap Time Simulator
When beginning to develop the architecture of our 2018 racecar, it became clear that significant analyses around the performance impacts of different designs would be necessary to achieve our goals for the season. Much of this work involves evaluating the impact of track performance when varying different joint parameters such as motor power to weight, battery capacity to mass, aerodynamics cost to performance, etc. Other characteristics such as battery capacity were also evaluated with the simulator. Each simulation iteration takes about 2 seconds on common laptops of the time allowing for batch simulations to be set up sweeping a variety of parameters to find optimal configurations and build tradeoff mappings.
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While existing simulation tools exist to simulate a car driving a track given vehicle parameters, the physics is heavily simplified and does not allow for the broad range of parameters that we wanted to evaluate. For example, weight transfer is a huge aspect of racing as it determines when each tire will begin to break traction. Available simulators assume no weight transfer which does not allow for evaluating how the centre of mass affects traction when accelerating. Additionally, with an electric drivetrain using independent motors per tire, we are able to perform torque vectoring where each tires available traction is calculated and used to maximize torque delivery around corners. Our custom simulation allowed us to evaluate the performance tradeoffs of implementing torque vectoring helping us allocate our resources more appropriately.
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The simulator was developed in Matlab and can be found on bitbucket. A point mass with a friction ellipse governs the maximum forces that can be applied to the model. A track of constant radius arc segments and straight segments is inputed and used to define the maximum speeds around each segment based on their curvature. Then, the simulated vehicle is accelerated and decelerated through each track segment while considering the aerodynamic drag and downforce changing the vehicles characteristics given its current state. The vehicle's state follows the maximum theoretical acceleration and velocity profiles to maximize tire performance at all times.
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On the right are two plots representing the data from the lap simulator. The top plot represents the velocity over the track. The bottom plot represents the acceleration.
Tire Data Analysis
Tires are the most important aspect of race car design. They define the maximum amount of force that can be subjected to influence a car on the road, and so optimizing the vehicles characteristics to work with your chosen tire is critical.
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Paying for a firm to conduct tire testing would have been a significant amount of money, similarly, doing the testing ourselves would be too human resource intensive. As a resource for the competition, a group of former FSAE members yearly conduct tests on a few tires and publish their results for a small operating fee. As the data is simply raw logs of data, a tool was needed to parse the data and fit useful coefficients to the tire. Most teams use a $1000 tool that I believed was unnecessary as I could simply make it myself.
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My Matlab script searches through and parses the data into individually run tests. A number of different tire parameters are extracted from these tests and presented in output figures that are given after running the script.
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A standard tire formula is used as a model to fit the slip angle to lateral acceleration plots for varying camber and normal force
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Coefficient of lateral friction as a function of different parameters is determined
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Coefficient of longitudinal friction as a function of different parameters is determined
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Other data like returning force are formatted to be easily extracted
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All of the code can be found on this bitbucket page.
Related Project Links
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