Battery Load Prediction Under Real-World Conditions Using Big Data and Generative AI
ビッグデータと生成AIを活用した市場環境における電池負荷予測技術
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266080
- Paper/Info type
- Proceedings (Autumn)
No.108-26
- Pages
- 1-2(Total 2 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) 波頭 佑哉 |
|---|---|
| Author(E) | 1) Yuya Hato |
| Affiliation(J) | 1) マツダ |
| Affiliation(E) | 1) Mazda |
| Abstract(J) | 電動車両のLiBでは，実際のユーザーの車両の使い方において，劣化や熱暴走に対する信頼性の確保が求められる．そこで市場のビッグデータと生成AIを活用し，ユーザーの使い方の特徴を再現した走行シナリオを生成する．そして，生成したシナリオと1Dモデルを用いて，市場で想定される電池の負荷強度を評価する手法を構築した． Translation |
| Abstract(E) | When lithium-ion batteries are used in electric vehicles, it is necessary to ensure reliability against degradation and thermal runaway, considering real-world customer usage patterns. Therefore, by applying generative AI to market big data, we developed a method to generate driving scenarios that replicate customer usage characteristics. We then developed a method to evaluate battery load intensity under market conditions by combining the generated scenarios with a one-dimensional model. |
