State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm
An improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/13/11/220 |
| _version_ | 1797463773671325696 |
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| author | Lihong Xiang Li Cai Nina Dai Le Gao Guoping Lei Junting Li Ming Deng |
| author_facet | Lihong Xiang Li Cai Nina Dai Le Gao Guoping Lei Junting Li Ming Deng |
| author_sort | Lihong Xiang |
| collection | DOAJ |
| description | An improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC curve is obtained using a 7-segment linear fitting method before the algorithms estimate the SOC. In addition, by combining this improved method with a third-order RC equivalent circuit model in the dynamic stress test (DST) case the convergence time is reduced by 0.15 s compared to the second-order RC equivalent circuit model. Following that, four different types of comparison experiments are carried out by comparing the improved algorithm to EKF and other SHEKF algorithms.The estimation accuracy under DST conditions of 0 °C, 25 °C and 45 °C is approximately 0.5%, 2.2% and 1.3% improvement compared to the EKF algorithm. |
| first_indexed | 2024-03-09T17:55:30Z |
| format | Article |
| id | doaj.art-aa2ee50d796241b6881eb1f77928199d |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-09T17:55:30Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-aa2ee50d796241b6881eb1f77928199d2023-11-24T10:23:14ZengMDPI AGWorld Electric Vehicle Journal2032-66532022-11-01131122010.3390/wevj13110220State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter AlgorithmLihong Xiang0Li Cai1Nina Dai2Le Gao3Guoping Lei4Junting Li5Ming Deng6Department of Electrical Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaDepartment of Electrical Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaDepartment of Electrical Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaSchool of Information and Electronics, Neijiang Vocational and Technical College, Neijiang 641100, ChinaDepartment of Electrical Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaCollege of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaResearch and Development Technology Center of Changan Industrial Group Co., Chongqing 404100, ChinaAn improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC curve is obtained using a 7-segment linear fitting method before the algorithms estimate the SOC. In addition, by combining this improved method with a third-order RC equivalent circuit model in the dynamic stress test (DST) case the convergence time is reduced by 0.15 s compared to the second-order RC equivalent circuit model. Following that, four different types of comparison experiments are carried out by comparing the improved algorithm to EKF and other SHEKF algorithms.The estimation accuracy under DST conditions of 0 °C, 25 °C and 45 °C is approximately 0.5%, 2.2% and 1.3% improvement compared to the EKF algorithm.https://www.mdpi.com/2032-6653/13/11/220state of chargemeasurement noise and system noisethird-order RC equivalent circuit modelSage-Husa algorithmexponential weighting |
| spellingShingle | Lihong Xiang Li Cai Nina Dai Le Gao Guoping Lei Junting Li Ming Deng State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm World Electric Vehicle Journal state of charge measurement noise and system noise third-order RC equivalent circuit model Sage-Husa algorithm exponential weighting |
| title | State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm |
| title_full | State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm |
| title_fullStr | State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm |
| title_full_unstemmed | State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm |
| title_short | State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm |
| title_sort | state of charge estimation of lithium ion batteries based on an improved sage husa extended kalman filter algorithm |
| topic | state of charge measurement noise and system noise third-order RC equivalent circuit model Sage-Husa algorithm exponential weighting |
| url | https://www.mdpi.com/2032-6653/13/11/220 |
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