Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter
State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/1/3 |
| _version_ | 1798039541472296960 |
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| author | Bizhong Xia Zizhou Lao Ruifeng Zhang Yong Tian Guanghao Chen Zhen Sun Wei Wang Wei Sun Yongzhi Lai Mingwang Wang Huawen Wang |
| author_facet | Bizhong Xia Zizhou Lao Ruifeng Zhang Yong Tian Guanghao Chen Zhen Sun Wei Wang Wei Sun Yongzhi Lai Mingwang Wang Huawen Wang |
| author_sort | Bizhong Xia |
| collection | DOAJ |
| description | State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms. |
| first_indexed | 2024-04-11T21:55:19Z |
| format | Article |
| id | doaj.art-e1bb38cae96141faacd2ae09b3aa844b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:55:19Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e1bb38cae96141faacd2ae09b3aa844b2022-12-22T04:01:08ZengMDPI AGEnergies1996-10732017-12-01111310.3390/en11010003en11010003Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman FilterBizhong Xia0Zizhou Lao1Ruifeng Zhang2Yong Tian3Guanghao Chen4Zhen Sun5Wei Wang6Wei Sun7Yongzhi Lai8Mingwang Wang9Huawen Wang10Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaSunwoda Electronic Co., Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co., Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co., Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co., Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co., Ltd., Shenzhen 518108, ChinaState of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.https://www.mdpi.com/1996-1073/11/1/3forgetting factor recursive least squaresnonlinear Kalman filterstate of charge estimationonline parameter identificationlithium-ion batteryvariable temperature |
| spellingShingle | Bizhong Xia Zizhou Lao Ruifeng Zhang Yong Tian Guanghao Chen Zhen Sun Wei Wang Wei Sun Yongzhi Lai Mingwang Wang Huawen Wang Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter Energies forgetting factor recursive least squares nonlinear Kalman filter state of charge estimation online parameter identification lithium-ion battery variable temperature |
| title | Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter |
| title_full | Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter |
| title_fullStr | Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter |
| title_full_unstemmed | Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter |
| title_short | Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter |
| title_sort | online parameter identification and state of charge estimation of lithium ion batteries based on forgetting factor recursive least squares and nonlinear kalman filter |
| topic | forgetting factor recursive least squares nonlinear Kalman filter state of charge estimation online parameter identification lithium-ion battery variable temperature |
| url | https://www.mdpi.com/1996-1073/11/1/3 |
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