Design and Optimization of the Heatsink of a Level 1 Electric Vehicle Charger
Bibliographic record
Abstract
The onboard circuits of EV chargers comprise heat-producing electronic devices such as MOSFETs and diodes for switching and power conversion operations. A heatsink must dissipate this generated heat to extend the devices’ life and prevent component thermal stress or failure. This study primarily investigates the optimal heatsink geometry and pin configuration, which offers the most efficient temperature versus cost performance. MATLAB/Simulink (R2024a) was used to model a Level 1 charger using eight MOSFETs and four diodes. Various heatsink geometries were modeled using the ANSYS (2024 R1) Workbench and Fluent software to optimize the sink’s thermal performance. The analyses were performed under transient conditions using natural and forced cooling scenarios. The 2 mm wide plate fin heatsink with 44 fins yielded the best result. Further enhancement of the best-performing naturally cooled model improved the switches and diodes temperatures by 14% and 4%, respectively. The performance of the heatsink was further improved by applying a cooling fan to achieve an up to 25% diode and 40% MOSFET thermal dissipation efficiency. The results of this study show that the most efficient cooling performance and cost are realized when the optimum combination of fin spacing, proximity from the cooling fan, and fin geometry is selected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".