Development of an EV HVAC Power Consumption Prediction Model Based on Multivariate Analysis and Machine Learning of Probe Data
プローブデータの多要素解析と機械学習を用いたEV空調消費電力予測モデルの構築
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266003
- Paper/Info type
- Proceedings (Autumn)
No.93-26
- 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) 長谷川 滉一, 2) 土川 晴久, 3) 松井 弘毅, 4) 福井 陽喜 |
|---|---|
| Author(E) | 1) Koichi Hasegawa, 2) Haruhisa Tsuchikawa, 3) Hiroki Matsui, 4) Haruki Fukui |
| Affiliation(J) | 1) 日産自動車, 2) 日産自動車, 3) 日産自動車, 4) 日産自動車 |
| Affiliation(E) | 1) Nissan Motor, 2) Nissan Motor, 3) Nissan Motor, 4) Nissan Motor |
| Abstract(J) | 当社BEVのIntelligent Route Planner機能は車両エネルギー消費予測を基盤としている．このうち空調電力は環境・使用条件により変動し高精度化が課題である．本報では車両プローブデータから特徴量を抽出，機械学習予測モデルを構築，実走行データ検証により予測高精度化を確認した． Translation |
| Abstract(E) | Our BEV's Intelligent Route Planner function is built upon vehicle energy consumption prediction. Among its components, HVAC power consumption varies considerably with environmental and operating conditions, making high-accuracy prediction a key challenge. This study extracts feature variables from vehicle probe data, constructs a machine learning-based prediction model, and verifies improved prediction accuracy through validation against real-world driving data. |
