tomwebster98 / race-strategy-modelling

Hey 👋, I'm Tom!

Energy Management Controls & Strategy Engineer

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Energy Management Controls Engineer for Mahindra Racing Formula E Team, MSc Advanced Motorsport Mechatronics student at Cranfield University, and first-class MPhys Physics with Astrophysics graduate from the University of Sussex. Experienced in control systems and software engineering, model-based system architecture, material mechanics testing and experimental design; in addition to STEM outreach and communications. Currently working towards a chartership in engineering (CEng) with the IET.

Passionate about Motorsport race strategy, vehicle dynamics and controls. Current projects focus on F1 race strategy data analysis, race strategy optimisation, Formula E Monte Carlo race strategy simulation development (MSc Thesis).

What I'm Doing:

• 📚   Completing an MSc in Advanced Motorsport Mechatronics at Cranfield University (09/2023 - 09/2024).
• 🛠   I mainly work with MATLAB, Simulink, and Python, specifically for data processing, visualisation, and analysis, or control system design.
• 🌱   I’m learning new techniques in race strategy analysis and optimisation, completing my MSc Thesis with a focus on Monte Carlo simulation development for Gen3 Formula E.
• 👨🏻‍💻   My personal projects are available on GitHub.
• 📫   Reach out on LinkedIn!

💡 Languages 💡:

• MATLAB/Simulink
• Python
• C++
• XML

🔨 Tools 🔨:

• Simulink
• McLaren Applied: ATLAS 9/10 for Race Data Analysis
• McLaren Applied: System Monitor for ECU (TAG320 and VCU500) Software Integration and Testing
• AVL Boost
• Capella (Model-Based Systems Engineering Tool)
• Python4Capella
• Git / GitHub / GitLab
• Linux
• VSCode

📈 Methodologies 📈:

• Model-Based Systems Engineering
• Test-Driven Development
• Hardware-in-the-Loop Testing
• Driver-in-the-Loop Testing
• Object-Oriented Programming
• Monte Carlo Simulation

🏎️ Current Projects 🏎️:

• Race Strategy Simulation and Optimisation in Third Generation Formula E - MSc Thesis, Cranfield University
• F1 Strategy Analysis - Available here.
• F1 Strategy Optimisation - Available here.
• Motorsport Engineering Study Notes - Available here.

race-strategy-modelling's Issues

Las Vegas Strategy Model Improvements

• Adjust Graphs Axes to be consistent for easier comparisons.
• Implement Pit Stop lap optimisation from corrected optimisation problem practice.

Optimisation Problem

Model the tyre strategy of an F1 race using an optimisation problem:

Simulate or model the pace/lapt-times for different tyre compounds, and use this information to optimise the pit stop window, and determine if it should be a 1 or 2 stop race.

Simulink and Simscape Modelling

Develop simulink and simscape models for mechanical systems.

Practice with Stateflow

Develop a state machine for automotive applications such as activating engine modes or gears in an automatic drivetrain.

Add full LV GP strategy model justification.

• Write up justifications for tyre parameter estimations.
• Include additional detail on potential SC and VSC scenarios impacting the chosen strategy. (SC window)
• Highlight alternative strategy options for unexpected scenarios, such as red flags or starting grid penalties.

Add analysis for LVGP non-linear tyre model.

• Add C5 - C3 strategy option.
• Add C5 - C4 - C4 strategy option.
• Complete non-linear section of Las Vegas strategy README. Determine the optimal strategy in this tyre model and account for flexibility with SC and VSC.

Tidy up MATLAB auto commits for race strategy optimiser project.