Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter
In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/15/1733 |
| _version_ | 1797525336842305536 |
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| author | Hao Wang Yanping Zheng Yang Yu |
| author_facet | Hao Wang Yanping Zheng Yang Yu |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC. |
| first_indexed | 2024-03-10T09:12:22Z |
| format | Article |
| id | doaj.art-b510f83fa0314305973072389a76f663 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T09:12:22Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b510f83fa0314305973072389a76f6632023-11-22T05:55:51ZengMDPI AGMathematics2227-73902021-07-01915173310.3390/math9151733Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman FilterHao Wang0Yanping Zheng1Yang Yu2College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaIn order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.https://www.mdpi.com/2227-7390/9/15/1733adaptive forgetting factorsimulated annealing optimizationonline identificationunscented Kalman filter |
| spellingShingle | Hao Wang Yanping Zheng Yang Yu Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter Mathematics adaptive forgetting factor simulated annealing optimization online identification unscented Kalman filter |
| title | Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter |
| title_full | Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter |
| title_fullStr | Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter |
| title_full_unstemmed | Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter |
| title_short | Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter |
| title_sort | lithium ion battery soc estimation based on adaptive forgetting factor least squares online identification and unscented kalman filter |
| topic | adaptive forgetting factor simulated annealing optimization online identification unscented Kalman filter |
| url | https://www.mdpi.com/2227-7390/9/15/1733 |
| work_keys_str_mv | AT haowang lithiumionbatterysocestimationbasedonadaptiveforgettingfactorleastsquaresonlineidentificationandunscentedkalmanfilter AT yanpingzheng lithiumionbatterysocestimationbasedonadaptiveforgettingfactorleastsquaresonlineidentificationandunscentedkalmanfilter AT yangyu lithiumionbatterysocestimationbasedonadaptiveforgettingfactorleastsquaresonlineidentificationandunscentedkalmanfilter |