Al Alloys and Casting Processes for Induction Motor Applications in Battery-Powered Electric Vehicles: A Review
Bibliographic record
Abstract
With the rapid expansion of battery-powered electric vehicles (BEVs) in the automotive industry, research interest in lightweight Al alloys as well as their casting processes and applications has increased considerably. The substitution of castable aluminum alloys with superior strengths and electrical conductivity for copper reduces the weight and size of electric induction motors, and improves the energy efficiency and driving range of the BEVs. The present article was intended to give a general introduction into the common cast Al aluminum alloys and their relevant processes, as well as to motivate the development of high strength and conductive Al alloys for the practical realization of Al applications in the motors of the BEVs. A number of cast alloy systems containing Cu, Si, Ni, Mg, Fe, and Ti were evaluated, in comparison to nanostructured wrought Al alloys. The conventional casting processes suitable for Al alloys, high pressure die casting, squeeze casting, and sand casting were described. Strengthening mechanisms including solid solution strengthening, precipitation strengthening, dislocation accumulation strengthening, and grain boundary strengthening were presented. The phenomenon of electrical conduction for Al alloys was outlined. The mechanical properties and electrical properties of the recently developed Al alloys for casting and deformation processes were comprehensively listed and critically reviewed in association with microstructural characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".