State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter
The covariance matrix of measurement noise is fixed in the Kalman filter algorithm. However, in the process of battery operation, the measurement noise is affected by different charging and discharging conditions and the external environment. Consequently, obtaining the noise statistical characteris...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/5/3/91 |
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| author | Liping Chen Yu Chen António M. Lopes Huifang Kong Ranchao Wu |
| author_facet | Liping Chen Yu Chen António M. Lopes Huifang Kong Ranchao Wu |
| author_sort | Liping Chen |
| collection | DOAJ |
| description | The covariance matrix of measurement noise is fixed in the Kalman filter algorithm. However, in the process of battery operation, the measurement noise is affected by different charging and discharging conditions and the external environment. Consequently, obtaining the noise statistical characteristics is difficult, which affects the accuracy of the Kalman filter algorithm. In order to improve the estimation accuracy of the state of charge (SOC) of lithium-ion batteries under actual working conditions, a fuzzy fractional-order unscented Kalman filter (FFUKF) is proposed. The algorithm combines fuzzy inference with fractional-order unscented Kalman filter (FUKF) to infer the measurement noise in real time and take advantage of fractional calculus in describing the dynamic behavior of the lithium batteries. The accuracy of the SOC estimation under different working conditions at three different temperatures is verified. The results show that the accuracy of the proposed algorithm is superior to those of the FUKF and extended Kalman filter (EKF) algorithms. |
| first_indexed | 2024-03-10T07:39:29Z |
| format | Article |
| id | doaj.art-46bb5d01f14f4107bf37ba6e0e12bcce |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-10T07:39:29Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-46bb5d01f14f4107bf37ba6e0e12bcce2023-11-22T13:09:25ZengMDPI AGFractal and Fractional2504-31102021-08-01539110.3390/fractalfract5030091State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman FilterLiping Chen0Yu Chen1António M. Lopes2Huifang Kong3Ranchao Wu4School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaLAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, PortugalSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaSchool of Mathematics, Anhui University, Hefei 230039, ChinaThe covariance matrix of measurement noise is fixed in the Kalman filter algorithm. However, in the process of battery operation, the measurement noise is affected by different charging and discharging conditions and the external environment. Consequently, obtaining the noise statistical characteristics is difficult, which affects the accuracy of the Kalman filter algorithm. In order to improve the estimation accuracy of the state of charge (SOC) of lithium-ion batteries under actual working conditions, a fuzzy fractional-order unscented Kalman filter (FFUKF) is proposed. The algorithm combines fuzzy inference with fractional-order unscented Kalman filter (FUKF) to infer the measurement noise in real time and take advantage of fractional calculus in describing the dynamic behavior of the lithium batteries. The accuracy of the SOC estimation under different working conditions at three different temperatures is verified. The results show that the accuracy of the proposed algorithm is superior to those of the FUKF and extended Kalman filter (EKF) algorithms.https://www.mdpi.com/2504-3110/5/3/91Kalman filterstate of chargefuzzy inferencelithium-ion batteries |
| spellingShingle | Liping Chen Yu Chen António M. Lopes Huifang Kong Ranchao Wu State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter Fractal and Fractional Kalman filter state of charge fuzzy inference lithium-ion batteries |
| title | State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter |
| title_full | State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter |
| title_fullStr | State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter |
| title_full_unstemmed | State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter |
| title_short | State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter |
| title_sort | state of charge estimation of lithium ion batteries based on fuzzy fractional order unscented kalman filter |
| topic | Kalman filter state of charge fuzzy inference lithium-ion batteries |
| url | https://www.mdpi.com/2504-3110/5/3/91 |
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