Accelerating Battery Thermal Analysis by Integrating CFD Simulation and Machine Learning Techniques
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込)：￥1,100 会員価格(税込)：￥880
- 文献番号
- 20249121
- 文献・情報種別
- SETC
No.2024-32-0121
- 掲載ページ
- 1-10(Total 10 p)
- 発行年月
- 2024年 11月
- 出版社
- その他・不明
- 言語
- 英語
- イベント
- SETC2024
書誌事項
| 著者（英） | 1) Gurudevan Devarajan, 2) Ganesh Vaidyanathan, 3) Ajinkya Bhave, 4) Lichao Ji, 5) Jiao Wang, 6) Wei Zhou, 7) Jiguang He, 8) Pengfei Shi |
|---|---|
| 勤務先（英） | 1) Siemens Industry Software (India) Private Limited, 2) Siemens Industry Software (India) Private Limited, 3) Siemens Industry Software (India) Private Limited, 4) Siemens Industry Software (Beijing) Co., Ltd., 5) Siemens Industry Software (Beijing) Co., Ltd., 6) BMW Brilliance Automotive Ltd., 7) BMW Brilliance Automotive Ltd., 8) BMW Brilliance Automotive Ltd. |
| 抄録（英） | The growing demand for sustainable transportation solutions and renewable energy storage systems has heightened the necessity for precise and effective prediction of battery thermal performance. However, achieving both precision and efficiency poses a challenge, necessitating exploration into diverse methodologies. The conventional use of Computational Fluid Dynamics (CFD) offers a comprehensive insight into thermal dynamics but prioritizes precision over efficiency. To enhance the efficiency of this traditional approach, numerous reduced-order modeling techniques have emerged, and the concept of Machine Learning (ML) presents a distinct avenue for enhancing simulation capabilities, particularly in the context of mobility solutions. This paper presents a novel approach to accelerate battery thermal analysis by integrating CFD and ML. The CFD simulations provide an intricate understanding of the thermal dynamics within batteries, encompassing fluid flow and temperature distributions. Building upon this physical understanding, ML models are trained using the CFD data to capture complex relationships and patterns within the thermal behavior to develop a framework capable of efficient prediction of thermal responses under diverse operating conditions. To validate the effectiveness of the proposed methodology, a case study is presented in the paper, comparing the results of the ML approach with CFD results. The findings demonstrate that the proposed methodology significantly reduces computational time while maintaining a high level of accuracy in prediction of battery thermal behavior. This innovative approach represents a promising step towards expediting the design and optimization of battery systems, contributing to faster development cycle of sustainable energy technologies. 翻訳 |
