Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery
Model-based observers appeal to both research and industry utilization due to the high accuracy and robustness. To further improve the robustness to dynamic work conditions and battery ageing, the online model identification is integrated to the state estimation, giving rise to the co-estimation met...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/139329 |
| _version_ | 1826126713120096256 |
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| author | Wei, Zhongbao Zhao, Jiyun Zou, Changfu Lim, Tuti Mariana Tseng, King Jet |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Wei, Zhongbao Zhao, Jiyun Zou, Changfu Lim, Tuti Mariana Tseng, King Jet |
| author_sort | Wei, Zhongbao |
| collection | NTU |
| description | Model-based observers appeal to both research and industry utilization due to the high accuracy and robustness. To further improve the robustness to dynamic work conditions and battery ageing, the online model identification is integrated to the state estimation, giving rise to the co-estimation methods. This paper systematically compares three types of co-estimation methods for the online state of charge of lithium-ion battery. This first method is dual extended Kalman filter which uses two parallel filters for co-estimation. The second method is a typical data-model fusion method which uses recursive least squares for model identification and extended Kalman filter for state estimation. Meanwhile, a noise compensating method based on recursive total least squares and Rayleigh quotient minimization is exploited for online model identification, which is further designed in conjunction with the extended Kalman filter to estimate the state of charge. Simulation and experimental studies are carried out to compare the performances of three methods in terms of the accuracy, convergence property, and noise immunity. The computing cost and tuning effort are further discussed to give insights to the application prospective of different methods. |
| first_indexed | 2024-10-01T06:56:51Z |
| format | Journal Article |
| id | ntu-10356/139329 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:56:51Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1393292020-05-19T02:03:06Z Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery Wei, Zhongbao Zhao, Jiyun Zou, Changfu Lim, Tuti Mariana Tseng, King Jet School of Civil and Environmental Engineering Engineering::Environmental engineering Model Identification State of Charge Model-based observers appeal to both research and industry utilization due to the high accuracy and robustness. To further improve the robustness to dynamic work conditions and battery ageing, the online model identification is integrated to the state estimation, giving rise to the co-estimation methods. This paper systematically compares three types of co-estimation methods for the online state of charge of lithium-ion battery. This first method is dual extended Kalman filter which uses two parallel filters for co-estimation. The second method is a typical data-model fusion method which uses recursive least squares for model identification and extended Kalman filter for state estimation. Meanwhile, a noise compensating method based on recursive total least squares and Rayleigh quotient minimization is exploited for online model identification, which is further designed in conjunction with the extended Kalman filter to estimate the state of charge. Simulation and experimental studies are carried out to compare the performances of three methods in terms of the accuracy, convergence property, and noise immunity. The computing cost and tuning effort are further discussed to give insights to the application prospective of different methods. 2020-05-19T02:03:06Z 2020-05-19T02:03:06Z 2018 Journal Article Wei, Z., Zhao, J., Zou, C., Lim, T. M., & Tseng, K. J. (2018). Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery. Journal of Power Sources, 402, 189-197. doi:10.1016/j.jpowsour.2018.09.034 0378-7753 https://hdl.handle.net/10356/139329 10.1016/j.jpowsour.2018.09.034 2-s2.0-85053489611 402 189 197 en Journal of Power Sources © 2018 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Environmental engineering Model Identification State of Charge Wei, Zhongbao Zhao, Jiyun Zou, Changfu Lim, Tuti Mariana Tseng, King Jet Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title | Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title_full | Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title_fullStr | Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title_full_unstemmed | Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title_short | Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery |
| title_sort | comparative study of methods for integrated model identification and state of charge estimation of lithium ion battery |
| topic | Engineering::Environmental engineering Model Identification State of Charge |
| url | https://hdl.handle.net/10356/139329 |
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