DSL Masters / NRF Sprint
Research discussion feedback note
This note is a thinking guide from your research discussion. It is not a proposal draft, not a decision, and not something to paste into an AI tool. Use it to think before you rewrite.
How To Use This
Read it once, close the page, and answer the questions in rough bullets. The goal is to make your own reasoning clearer before the next version of your NRF application.
What you are trying to do
You are trying to use a real electric drivetrain platform to understand how control strategies behave when the hardware, sensors, cost, and safety limits are constrained. The useful research direction is the gap between clean simulation and measured hardware behaviour, especially when the sensing path is deliberately simpler or cheaper than a high-end test rig.
What is strong
The strongest part of the idea is the physical platform and your willingness to work with real measurements. That gives the project a concrete evidence path: model a drivetrain, run bounded tests, compare expected behaviour with measured behaviour, and identify which trade-offs matter.
Main issue
The main knot is separating a cool build from a reusable research contribution. Building or improving the platform is engineering. The research begins when you decide what control choice, sensing limit, or measurement problem you will test and what another engineer could learn from the result.
Three thinking questions
- Which one control or sensing question is the centre: sensorless control, field weakening, regenerative braking, or model-versus-hardware mismatch?
- What safe bench or drivetrain test can produce useful data without turning the project into a vehicle build?
- What baseline will you compare against, and what trade-off will matter most: efficiency, torque ripple, speed tracking, energy recovery, implementation complexity, or sensing cost?
What to do next
Bring rough bullets that name the hardware route, the one control question, the measurement plan, the baseline, the safety boundary, and the reusable lesson the work could produce.