Thermal Management of Nonuniform Heat Fluxes in an Electric-Vehicle Fast-Charger: Experimental and Numerical Analysis
Bibliographic record
Abstract
This article presents a novel approach to address nonuniform heat dissipation in high-power electrical systems, focusing specifically on an electric-vehicle (EV) fast-charger system. These systems often incorporate diverse power semiconductor devices with distinct electrical loads and thermal characteristics, leading to nonuniform heat fluxes. Due to manufacturing constraints, commercial off-the-shelf (COTS) heat sinks are unable to effectively handle these heat load distributions. To address this issue, this work utilizes a wire-arc thermal spray additive manufacturing technique to fabricate a topologically optimized heat sink for the thermal management of an EV fast-charger system. The optimized heat sink exhibits substantial volume reduction (81%) and mass reduction (71%) compared to a modified COTS heat sink. Experimental results demonstrate an average 27% reduction (0.02 °C/W) in overall thermal resistance and a 25% reduction (2.8 °C) in maximum heat sink surface temperature difference. Real-world implementation of the fast-charger system revealed a 78% reduction (7.6 °C) in interdevice temperature difference and a notable 14% reduction (13.1 °C) in maximum heat sink temperature within the most effective region. Numerical analysis substantiates these findings by emphasizing the significance of adapting the local Nusselt number based on the locally applied heat load. This work showcases the practicality of the proposed approach in designing and fabricating application-specific heat sink solutions for challenging thermal profiles prevalent in high-power fast-charger systems.
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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) | 0.000 | 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) | 0.001 | 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 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".