A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering
As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and repl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9751147/ |
| _version_ | 1818479540801896448 |
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| author | Younghwi Ko Kangcheol Cho Minseong Kim Woojin Choi |
| author_facet | Younghwi Ko Kangcheol Cho Minseong Kim Woojin Choi |
| author_sort | Younghwi Ko |
| collection | DOAJ |
| description | As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error. |
| first_indexed | 2024-12-10T11:12:01Z |
| format | Article |
| id | doaj.art-510e19337cee4e4da813e842fa93d135 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:12:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-510e19337cee4e4da813e842fa93d1352022-12-22T01:51:23ZengIEEEIEEE Access2169-35362022-01-0110387933880110.1109/ACCESS.2022.31656399751147A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman FilteringYounghwi Ko0Kangcheol Cho1Minseong Kim2Woojin Choi3https://orcid.org/0000-0003-3200-9635Department of Electrical Engineering, Soongsil University, Seoul, South KoreaDepartment of Electrical Engineering, Soongsil University, Seoul, South KoreaDepartment of Electrical Engineering, Soongsil University, Seoul, South KoreaDepartment of Electrical Engineering, Soongsil University, Seoul, South KoreaAs the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error.https://ieeexplore.ieee.org/document/9751147/Li-ion batteryenhanced coulomb countingKalman filtercapacity estimationSOC estimation |
| spellingShingle | Younghwi Ko Kangcheol Cho Minseong Kim Woojin Choi A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering IEEE Access Li-ion battery enhanced coulomb counting Kalman filter capacity estimation SOC estimation |
| title | A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering |
| title_full | A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering |
| title_fullStr | A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering |
| title_full_unstemmed | A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering |
| title_short | A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering |
| title_sort | novel capacity estimation method for the lithium batteries using the enhanced coulomb counting method with kalman filtering |
| topic | Li-ion battery enhanced coulomb counting Kalman filter capacity estimation SOC estimation |
| url | https://ieeexplore.ieee.org/document/9751147/ |
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