Comparison of IR, EKF, and NARX Estimators for Battery SOC and Voltage Prediction Across EV Pack Sizes
- 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
- 20266079
- 文献・情報種別
- 学術講演会予稿集（秋）
No.108-26
- 掲載ページ
- 1-2(Total 2 p)
- 発行年月
- 2026年 10月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2026年秋季大会
書誌事項
| 著者 | 1) Alessandro Massimo Bertucci, 2) Xinwei Li, 3) Ratnak Sok, 4) Keiki Tanabe, 5) Goro Iijima, 6) Jin Kusaka |
|---|---|
| 著者（英） | 1) Alessandro Massimo Bertucci, 2) Xinwei Li, 3) Ratnak Sok, 4) Keiki Tanabe, 5) Goro Iijima, 6) Jin Kusaka |
| 勤務先 | 1) Waseda University, 2) Waseda University, 3) Waseda University, 4) Mitsubishi Fuso Truck and Bus, 5) Mitsubishi Fuso Truck and Bus, 6) Waseda University |
| 勤務先（英） | 1) Waseda University, 2) Waseda University, 3) Waseda University, 4) Mitsubishi Fuso Truck and Bus, 5) Mitsubishi Fuso Truck and Bus, 6) Waseda University |
| 抄録（英） | This study compares Internal Resistance (IR), Extended Kalman Filter (EKF), and Nonlinear Autoregressive with Exogenous Inputs (NARX) estimators for battery state-of-charge and terminal-voltage prediction across S, M, and L electric-vehicle battery pack configurations. On regular-temperature JE05 measured logger data, IR and EKF achieved similar SOC accuracy but poor voltage reproduction, especially for the L configuration. NARX achieved higher joint SOC and voltage accuracy on the same data. Cold-chamber NARX results were separated by driving cycle, showing strong performance for 50 km/h and 80 km/h cases, while JE05 at -25 degC remained the most difficult voltage condition. 翻訳 |
