Hybrid powertrains
Energy management, control strategies, and integration across electrical and mechanical systems.
Research notebook
Engineering systems through simulation, optimization, and experimentation.
My research interests sit between thermodynamics, control, computation, and energy systems. I’m learning through models, small investigations, and careful comparison with physical reality.
Research model
From observation to useful decisions.
A working framework for asking better questions. Each stage can send the work back for another iteration.
- 01
Physical systems
Observe real behavior
- 02
Modeling
Make assumptions explicit
- 03
Simulation
Test scenarios
- 04
Optimization
Compare strategies
- 05
Decision making
Interpret the tradeoffs
- 06
Impact
Apply what is learned
Iterate · Learn · Improve. Findings are checked against the model’s limits, then the questions are refined.
Core themes
What I’m exploring.
Hydrogen & combustion
Fuel behavior, engine adaptation, and the practical questions around alternative energy.
Energy optimization
Using models and data to make energy systems more efficient and responsive.
Computational engineering
Simulation and analysis as ways to understand complex physical systems.
Process
A repeatable way to investigate.
The steps are a guide rather than a claim that every project has reached validation.
- 01
Observe
- 02
Review
- 03
Model
- 04
Simulate
- 05
Analyze
- 06
Validate
- 07
Document
Current investigations
Questions under development.
These are investigations and planned studies, with their present status stated plainly.
Hybrid vehicle energy management
How can control strategies reduce fuel use while respecting battery constraints?
Read investigationHydrogen ICE simulation
What tradeoffs emerge when hydrogen is used in an internal combustion engine?
Read investigationSmart EV charging optimization
How might charging schedules account for grid demand and operational cost?
Read investigationMethods & tools
Methods before software.
Tools are chosen to serve the question, not the other way around.
Simulation
Represent physical behavior, expose assumptions, compare scenarios.
Exploring with Python, OpenModelica, Cantera
Optimization & control
Formulate objectives and constraints, then test candidate strategies.
Exploring with Python, Pyomo, CVXPY
Data analysis
Inspect results and uncertainty before drawing conclusions.
Exploring with NumPy, Pandas, SciPy
Machine learning
Explore data driven control where simpler models are insufficient.
Exploring with PyTorch, reinforcement learning
Reading & learning
On the desk.
References that inform the questions and methods behind the work.
- Book
Internal Combustion Engine Fundamentals
John B. Heywood
- Book
Reinforcement Learning: An Introduction
Richard S. Sutton & Andrew G. Barto
- Reference
Cantera documentation
Cantera project
Research roadmap
Where the work may lead.
These are directions and intentions, not completed milestones.
- Current
Strengthening foundations
Thermodynamics, control, and reproducible simulation practice.
- Exploring
Comparing strategies
Hybrid energy management and hydrogen combustion questions.
- Next
Graduate study
Preparing for a mechanical engineering degree focused on energy systems.
- Long term
Applied impact
Contributing to more efficient and sustainable mobility.