Simultaneous Material and Shape Optimization of EV Battery Plates Using Surrogate Models Trained on Limited Samples
少数サンプル学習サロゲートモデルによるEVバッテリープレートの材料・形状同時最適化
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266109
- Paper/Info type
- Proceedings (Autumn)
No.114-26
- Pages
- 1-4(Total 4 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) 齋藤 博之, 2) 道下 孝之, 3) 河尻 耕太郎 |
|---|---|
| Author(E) | 1) Hiroyuki Saito, 2) Takayuki Michishita, 3) Kotaro Kawajiri |
| Affiliation(J) | 1) エイゾス, 2) エイゾス, 3) エイゾス |
| Affiliation(E) | 1) AIZOTH, 2) AIZOTH, 3) AIZOTH |
| Abstract(J) | 放熱性能を担うEVバッテリープレートの設計では，熱および強度特性を同時に満たす必要がある．本検討では，少数サンプル学習で構築した高精度な熱・構造物理場予測サロゲートモデルを活用し，リアルタイム予測および材料・形状同時最適化を可能にする手法を示す． Translation |
| Abstract(E) | In designing EV battery plates responsible for heat dissipation, both thermal and mechanical strength requirements must be satisfied simultaneously. This study presents a method that enables real-time prediction and simultaneous material and shape optimization by utilizing a high-accuracy surrogate model for thermo-structural physical field prediction, constructed through limited-sample learning. |
