Comparison of IR, EKF, and NARX Estimators for Battery SOC and Voltage Prediction Across EV Pack Sizes
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266079
- Paper/Info type
- Proceedings (Autumn)
No.108-26
- Pages
- 1-2(Total 2 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- English
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) Alessandro Massimo Bertucci, 2) Xinwei Li, 3) Ratnak Sok, 4) Keiki Tanabe, 5) Goro Iijima, 6) Jin Kusaka |
|---|---|
| Author(E) | 1) Alessandro Massimo Bertucci, 2) Xinwei Li, 3) Ratnak Sok, 4) Keiki Tanabe, 5) Goro Iijima, 6) Jin Kusaka |
| Affiliation(J) | 1) Waseda University, 2) Waseda University, 3) Waseda University, 4) Mitsubishi Fuso Truck and Bus, 5) Mitsubishi Fuso Truck and Bus, 6) Waseda University |
| Affiliation(E) | 1) Waseda University, 2) Waseda University, 3) Waseda University, 4) Mitsubishi Fuso Truck and Bus, 5) Mitsubishi Fuso Truck and Bus, 6) Waseda University |
| Abstract(E) | 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. |
