An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications
In practical applications, especially in applications where the current scale is very high, the accuracy of measuring sensors is very important to estimate the battery state of charge. In addition, due to the increase in the number of battery cells in these applications, the cost of providing accura...
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Bilijipub publisher
2023-12-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_186523_2491fdff7843759019cf5a5e69e33c4f.pdf |
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| author | Mehrdad Yousefi Faal |
| author_facet | Mehrdad Yousefi Faal |
| author_sort | Mehrdad Yousefi Faal |
| collection | DOAJ |
| description | In practical applications, especially in applications where the current scale is very high, the accuracy of measuring sensors is very important to estimate the battery state of charge. In addition, due to the increase in the number of battery cells in these applications, the cost of providing accurate sensors is very high. On the other hand, low-cost sensors have some error in the form of bias or fault on their output, which causes an estimation error in the battery state of charge. This paper addresses this problem and presents an augmented unscented Kalman filter in which the faults that occur on the measurement sensor are considered and estimated as an additive variable. On the other hand, the effect of these faults on the estimation of other state variables of the battery model, including the state of charge, is removed. In this way, an accurate estimate of the battery state of charge is obtained even in the presence of measurement sensor faults. In order to check the performance of the proposed method, a series of experiments have been conducted using practical data and the results have been compared with an unscented Kalman filter. The results show that the proposed method has a suitable and very good performance and can provide better accuracy than the basic method as much as 3% for estimating the state of charge. |
| format | Article |
| id | doaj-art-bda176b97b2a4889a86dc11a8efd7846 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Bilijipub publisher |
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| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-bda176b97b2a4889a86dc11a8efd78462025-02-12T08:47:31ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-12-0100204344310.22034/aeis.2023.416069.1133186523An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary ApplicationsMehrdad Yousefi Faal0Department of Mechanical Engineering, University of Tabriz, Tabriz, 5166616471, IranIn practical applications, especially in applications where the current scale is very high, the accuracy of measuring sensors is very important to estimate the battery state of charge. In addition, due to the increase in the number of battery cells in these applications, the cost of providing accurate sensors is very high. On the other hand, low-cost sensors have some error in the form of bias or fault on their output, which causes an estimation error in the battery state of charge. This paper addresses this problem and presents an augmented unscented Kalman filter in which the faults that occur on the measurement sensor are considered and estimated as an additive variable. On the other hand, the effect of these faults on the estimation of other state variables of the battery model, including the state of charge, is removed. In this way, an accurate estimate of the battery state of charge is obtained even in the presence of measurement sensor faults. In order to check the performance of the proposed method, a series of experiments have been conducted using practical data and the results have been compared with an unscented Kalman filter. The results show that the proposed method has a suitable and very good performance and can provide better accuracy than the basic method as much as 3% for estimating the state of charge.https://aeis.bilijipub.com/article_186523_2491fdff7843759019cf5a5e69e33c4f.pdfstate of chargeestimationlithium-ion batteriesunscented kalman filterfault |
| spellingShingle | Mehrdad Yousefi Faal An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications Advances in Engineering and Intelligence Systems state of charge estimation lithium-ion batteries unscented kalman filter fault |
| title | An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications |
| title_full | An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications |
| title_fullStr | An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications |
| title_full_unstemmed | An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications |
| title_short | An Augmented Estimation of the State of Charge and Measurement Fault for Lithium-ion Batteries for Off-Grid Stationary Applications |
| title_sort | augmented estimation of the state of charge and measurement fault for lithium ion batteries for off grid stationary applications |
| topic | state of charge estimation lithium-ion batteries unscented kalman filter fault |
| url | https://aeis.bilijipub.com/article_186523_2491fdff7843759019cf5a5e69e33c4f.pdf |
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