Why This Topic Matters
An electro-thermal electric-vehicle powertrain model that links PMSM torque-speed operation, inverter and motor losses, vehicle demand and component temperature evolution.
For academic work, the model should connect every claimed improvement to a measurable output. A reliable workflow begins with a validated baseline, introduces one controlled modification at a time and uses repeatable scenarios for comparison.
Project Objective
Evaluate how electrical loading and drive-cycle demand translate into heat generation and temperature rise in a PMSM-driven EV powertrain, supporting thermal-limit, efficiency and cooling-system studies.
Recommended MATLAB Simulink Blocks
- Driver and longitudinal vehicle-dynamics model
- Battery/DC source and traction inverter
- PMSM traction motor with torque-speed and efficiency behavior
- Mechanical transmission and wheel-load model
- Copper, iron, switching and conduction loss calculations
- Lumped thermal network for winding, stator, rotor/magnet, inverter and coolant nodes
Step-by-Step Modelling Workflow
- Define vehicle mass, road load, gear ratio, motor ratings and drive-cycle speed reference.
- Calculate inverter and PMSM losses from current, speed, torque and switching conditions.
- Feed the loss terms into thermal capacitance and thermal-resistance networks.
- Apply ambient and coolant boundary conditions and simulate over repeated or severe drive cycles.
- Compare temperatures, losses and efficiency under baseline and improved cooling/control cases.
Simulation Cases to Include
- Urban stop–start drive cycle
- Highway or high-speed operation
- Hill-climb/high-torque demand
- Cooling-flow or ambient-temperature variation
- Motor-sizing and current-limit comparison
Graphs and Results to Discuss
- Vehicle speed and traction torque
- Battery power, inverter power and motor mechanical power
- Copper, iron, switching and conduction losses
- Winding, stator, magnet and inverter temperature
- Efficiency map trajectory and thermal-limit margin
Do not report a curve only as “improved.” State the event time, compare the reference and measured signals, calculate relevant indices and explain the physical reason for the change.
PhD Novelty and FYP Extension Ideas
- Liquid-cooling or oil-spray cooling model
- Temperature-dependent PMSM parameters and demagnetization risk
- Thermal-aware torque derating controller
- Battery–inverter–motor integrated thermal management
- Drive-cycle optimization for energy and temperature reduction
Where This Project Can Be Used
- EV thermal-management PhD and master’s research
- Automotive engineering FYP and capstone projects
- PMSM sizing and thermal-limit evaluation
- Electric powertrain efficiency studies
- OEM-oriented model-based design and calibration
Common Modelling Mistakes
- Using inconsistent base values, units or sign conventions across subsystems.
- Tuning all control loops simultaneously instead of validating the inner loops first.
- Comparing controllers under different initial conditions or disturbances.
- Ignoring actuator, converter, current, SOC, temperature or power limits.
- Presenting scope screenshots without quantitative result interpretation.
Related Project Demonstration
The dedicated project page includes the uploaded MATLAB Simulink video, project scope, expected outputs and related research links.