Thermal Performance Evaluation and Design Optimization of a Battery Disconnect Unit for High-Power Electric Vehicle Applications
Bibliographic record
Abstract
The battery disconnect unit (BDU) is one of the most essential safety systems and battery control sub-assemblies within conventional electric vehicles (EVs) to date. The BDU plays a critical role in electro-mechanical control and protection by using contactors and fuses to manage the flow and distribution of power. The selection of busbar size and means of convection is critical given the restrictive mechanical constraints imposed by OEM battery tray designs-including requirements for vibration, electrical isolation, manufacturing tolerances, and creepages and clearances. This study aims to provide a thermally focused evaluation of various busbar thicknesses in addition to also determine whether natural convection is sufficient to manage the heat rise, or if forced convection is required using a combined joule heating modelling approach in MATLAB Simulink and computational fluid dynamics (CFD) with SolidWorks. A mixed drive cycle and constant current charging conditions were applied to determine key hotspots and assess component temperature rise. The methodology incorporates mechanical constraints, high voltage schematics, and justification of electrical component selection to provide a practical and thermally reliable custom BDU. Results showed that 6 mm thick C110 copper busbar limited the temperature rise under a DC fast charging (DCFC) case of 29.09° C whereas thinner busbars exceeded 30° C. Forced convection with a convection coefficient of$35\ \mathrm{W} /\left(\mathrm{m}^{2} \cdot \mathrm{K}\right)$was superior to natural convection with a ROA for the DCFC case being 4.35° C as opposed to the natural convection case of 13.27 °C, both approaches maintained temperatures within acceptable limits. Ultimately, natural convection was selected due to its thermal adequacy and drastically lower mechanical complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".