Thermal Modelling of an Induction Motor with Liquid Cooling Optimization for Different EV Drive Cycles
Bibliographic record
Abstract
Induction Motor (IM) is an alternative choice over permanent magnet motor in EV/HEV applications due to non–requirement of rare–earth magnets, greater thermal tolerance and low production costs. However, additional conductor losses in the rotor causes higher temperature in the IM which directly reduces the motor performance and in the long run it decreases motor life expectancy due to thermal stress in the rotor bars. Hence, it is critical to design and optimize the liquid cooling in induction motor to produce required torque and power for electric vehicles. Standard tests for UDDS (Urban Dynamometer Driving Schedule) and HWFET (Highway Fuel Economy Driving Schedule) drive cycles are used to determine performance of traction motors in terms of torque, power, efficiency and thermal condition. This paper proposes a novel simplified Lumped Parameter Thermal Network (LPTN) model which monitors the required power and torque generation for both UDDS and HWFET driving conditions and regulates liquid coolant flow rate to the motor to maintain the motor temperature within the safe limit. A 15–hp Aluminum Rotor Induction Motor (ARIM) prototype with liquid cooling has been used for the proposed thermal model development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".