State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter
Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2021/8869415 |
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| author | Quanchun Yan Kangkang Yuan Wen Gu Chenlong Li Guoqiang Sun Yanan Liu |
| author_facet | Quanchun Yan Kangkang Yuan Wen Gu Chenlong Li Guoqiang Sun Yanan Liu |
| author_sort | Quanchun Yan |
| collection | DOAJ |
| description | Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC. |
| format | Article |
| id | doaj-art-54ec4bbddf4e4496b7b52f78cc7fd027 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-54ec4bbddf4e4496b7b52f78cc7fd0272025-02-03T05:57:51ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2021-01-01202110.1155/2021/88694158869415State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman FilterQuanchun Yan0Kangkang Yuan1Wen Gu2Chenlong Li3Guoqiang Sun4Yanan Liu5The College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100, ChinaThe College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100, ChinaJiangsu Frontier Electric Power Technology Co., Ltd, Nanjing, Jiangsu 211102, ChinaJiangsu Frontier Electric Power Technology Co., Ltd, Nanjing, Jiangsu 211102, ChinaThe College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100, ChinaJiangsu Frontier Electric Power Technology Co., Ltd, Nanjing, Jiangsu 211102, ChinaAccurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC.http://dx.doi.org/10.1155/2021/8869415 |
| spellingShingle | Quanchun Yan Kangkang Yuan Wen Gu Chenlong Li Guoqiang Sun Yanan Liu State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter International Journal of Photoenergy |
| title | State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter |
| title_full | State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter |
| title_fullStr | State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter |
| title_full_unstemmed | State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter |
| title_short | State of Charge Estimation for Lithium-Ion Battery via MILS Algorithm Based on Ensemble Kalman Filter |
| title_sort | state of charge estimation for lithium ion battery via mils algorithm based on ensemble kalman filter |
| url | http://dx.doi.org/10.1155/2021/8869415 |
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