Data · dataset · 2026
Numerical investigation on no-load loss reduction in electric vehicle gearbox by surface wettability modification
Listed in ScienceDB
High no-load losses during high-speed cruising impair the range performance of electric vehicles.
Description
To address the issue, a drag reduction (DR) strategy based on an oleophobic coating is applied to an engineering grade electric vehicle gearbox (EVG), and a component to vehicle energy-saving evaluation chain is established. A decomposition framework is then established to isolate churning loss from total no-load loss and quantify the coating effect.
A combined experimental and numerical approach is then adopted. A splash-lubricated EVG model is built using the Moving Particle Semi-Implicit (MPS) method. The model resolves how the flow field, pressure field, and oil coverage rate evolve with rotational speed.
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The model is validated step by step using a rotating disk rig, a transparent case gearbox rig, and a high speed EVG rig. Surface characterization shows that the coating forms a nanoscale rough surface on the gear end faces. The measured oil contact angle increases from 7.8° to 52.7°, and the oleophobicity is retained after abrasion.
The experiments show that the coating reduces the churning loss by approximately 17.3% to 52.3% over the 500 to 10000 r/min range. The MPS simulation shows that the peak oil pressure in the meshing zone is lowered by 41.3%. Finally, the measured power saving ΔP0 is mapped onto the WLTC using the proposed model.
This mapping yields a cumulative energy saving of about 122 Wh per cycle. The driving range extension is concentrated in the medium and high speed range.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.0102d ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Automotive engineering 73% · Simulation 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.0102d | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4002 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |