Recursive Estimation of Effective Resistance for Rechargeable Batteries with μ-Markov Model
μ-マルコフモデルを用いた二次電池の実効抵抗の逐次推定
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- Publication code
- 20214926
- Paper/Info type
- JSAE Transaction
Vol.52 No.6
- Pages
- 1323-1328(Total 6 p)
- Date of publication
- Nov 2021
- Publisher
- JSAE
- Language
- Japanese
Detailed Information
| Category(J) | 研究論文 Translation |
|---|---|
| Category(E) | ResearchPaper |
| Author(J) | 1) 川口 貴弘, 2) 大山 隆景, 3) 丸田 一郎, 4) 長村 謙介, 5) 片芝 惇平, 6) 足立 修一 |
| Author(E) | 1) Takahiro Kawaguchi, 2) Takahiro Ohyama, 3) Ichiro Maruta, 4) Kensuke Osamura, 5) Jumpei Katashiba, 6) Shuichi Adachi |
| Affiliation(J) | 1) 群馬大学, 2) 慶應義塾大学, 3) 京都大学, 4) マレリ, 5) マレリ, 6) 慶應義塾大学 |
| Abstract(J) | 二次電池から得られる充放電可能電力を評価するために実行抵抗という特徴量を定義し，それを電流や電圧のデータから推定する方法を提案する。実行抵抗を計算するのに適したシステム同定モデルの構造について考察する。用いるデータのオフセットに対応し，逐次推定法を適用して実用性を向上する。 Translation |
| Abstract(E) | This paper proposes a recursive estimation method of the effective resistance, which is a feature used to estimate the state of power of a battery. In the proposed method, a model of the internal impedance is constructed with a system identification technique, and the effective resistance is calculated from the model. This paper considers what structure of the model is appropriate for the calculation of the effective resistance and proposes using a model named μ-Markov model. This structure helps to reduce the number of estimated parameters and computational burden. The model is extended to be applied for data that have an offset. The parameters of the model are estimated by the recursive leastsquares with a forgetting factor. The effectiveness of the proposed method is verified through a numerical simulation and an experiment. |
