Integrated topology and packaging optimization for conceptual-level electric vehicle chassis design via the component-existence method
Bibliographic record
Abstract
Conventional vehicle architectures are undergoing significant transformation as automakers embrace electrification. With increased emphasis on lightweight structures design and efficient packaging of new electric powertrains, numerical tools are now essential to help solve these complex material and component distribution problems. To address these challenges, methods for integrated topology and packaging optimization (iTOPO) have been developed to couple these problem statements and form dynamic component-structure interactions. In this work, a component-existence approach is used for conducting iTOPO of self-contained electric vehicle chassis structures to demonstrate the benefits and scalability of this emerging methodology. Examples focus on incorporating simplified components for battery modules and electric motors within the underlying vehicle structure, integrating up to 43 components simultaneously in a 3D design domain. Here, discussion highlights the development of unique integrated layouts, methodology tunability, and practical insights of the formed component-structure interactions. iTOPO results are also compared to equivalent topology-only problems and show less than a 10% difference in compliance despite the addition of various complex integration requirements (e.g. multiple geometries, packaging symmetry).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".