Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter
Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is pro...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9524 |
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| author | Likun Xing Xianyuan Wu Liuyi Ling Lu Lu Liang Qi |
| author_facet | Likun Xing Xianyuan Wu Liuyi Ling Lu Lu Liang Qi |
| author_sort | Likun Xing |
| collection | DOAJ |
| description | Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance <i>R</i><sub>0</sub> of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when <i>R</i><sub>0</sub> is inaccurate. |
| first_indexed | 2024-03-09T22:06:27Z |
| format | Article |
| id | doaj.art-4d9ad959e35f48558ce4cd63cba1cce7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:27Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-4d9ad959e35f48558ce4cd63cba1cce72023-11-23T19:41:00ZengMDPI AGApplied Sciences2076-34172022-09-011219952410.3390/app12199524Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman FilterLikun Xing0Xianyuan Wu1Liuyi Ling2Lu Lu3Liang Qi4School of Electrical and Information Technology, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Technology, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Technology, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Technology, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Technology, Anhui University of Science and Technology, Huainan 232001, ChinaAccurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance <i>R</i><sub>0</sub> of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when <i>R</i><sub>0</sub> is inaccurate.https://www.mdpi.com/2076-3417/12/19/9524state-of-chargemulti-innovation unscented Kalman filterfractional order square root cubature Kalman filteradaptive genetic algorithmthe fractional order model |
| spellingShingle | Likun Xing Xianyuan Wu Liuyi Ling Lu Lu Liang Qi Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter Applied Sciences state-of-charge multi-innovation unscented Kalman filter fractional order square root cubature Kalman filter adaptive genetic algorithm the fractional order model |
| title | Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter |
| title_full | Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter |
| title_fullStr | Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter |
| title_full_unstemmed | Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter |
| title_short | Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter |
| title_sort | lithium battery soc estimation based on multi innovation unscented and fractional order square root cubature kalman filter |
| topic | state-of-charge multi-innovation unscented Kalman filter fractional order square root cubature Kalman filter adaptive genetic algorithm the fractional order model |
| url | https://www.mdpi.com/2076-3417/12/19/9524 |
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