Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs
With the continuous reduction of energy and aggravation of environmental damage, the wind and solar complementary power generation system has received wide attention, among which the lithium battery pack is one of the most concerned components of the whole system. Accurate and effective estimation o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_02040.pdf |
| _version_ | 1797756262132219904 |
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| author | Zhang Ruoyuan Zhang hailong |
| author_facet | Zhang Ruoyuan Zhang hailong |
| author_sort | Zhang Ruoyuan |
| collection | DOAJ |
| description | With the continuous reduction of energy and aggravation of environmental damage, the wind and solar complementary power generation system has received wide attention, among which the lithium battery pack is one of the most concerned components of the whole system. Accurate and effective estimation of the state of charge (SOC) of lithium battery pack not only can ensure the rational use of resources and reduce costs, but also ensure the safe and reliable operation of the system. Since the normal operation of Li-ion battery pack has strong nonlinearity, a general nonlinear equivalent circuit model is selected, the charge/discharge multiplier and ambient temperature are fully considered, the Fourier function is used to effectively fit the model parameters, and the extended Kalman filter algorithm (EKF) is used to dynamically estimate the SOC of Li-ion battery pack in combination with the traditional ampere-time integration method, and the simulation is verified by MATLAB software. The results show that the extended Kalman filter algorithm selected in the paper can effectively track the SOC of the lithium battery pack and control the tracking error below 1%. |
| first_indexed | 2024-03-12T17:57:55Z |
| format | Article |
| id | doaj.art-dd45fe6ad254419cb55161fa0528e250 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T17:57:55Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-dd45fe6ad254419cb55161fa0528e2502023-08-02T13:18:51ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014060204010.1051/e3sconf/202340602040e3sconf_icemee2023_02040Extended Kalman Filter-Based SOC Estimation for Lithium Battery PacksZhang Ruoyuan0Zhang hailong1Anhui Water Conservancy Technical College, 18 Hema Road, Feidong County, Hefei CityAnhui Water Conservancy Technical College, 18 Hema Road, Feidong County, Hefei CityWith the continuous reduction of energy and aggravation of environmental damage, the wind and solar complementary power generation system has received wide attention, among which the lithium battery pack is one of the most concerned components of the whole system. Accurate and effective estimation of the state of charge (SOC) of lithium battery pack not only can ensure the rational use of resources and reduce costs, but also ensure the safe and reliable operation of the system. Since the normal operation of Li-ion battery pack has strong nonlinearity, a general nonlinear equivalent circuit model is selected, the charge/discharge multiplier and ambient temperature are fully considered, the Fourier function is used to effectively fit the model parameters, and the extended Kalman filter algorithm (EKF) is used to dynamically estimate the SOC of Li-ion battery pack in combination with the traditional ampere-time integration method, and the simulation is verified by MATLAB software. The results show that the extended Kalman filter algorithm selected in the paper can effectively track the SOC of the lithium battery pack and control the tracking error below 1%.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_02040.pdf |
| spellingShingle | Zhang Ruoyuan Zhang hailong Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs E3S Web of Conferences |
| title | Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs |
| title_full | Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs |
| title_fullStr | Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs |
| title_full_unstemmed | Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs |
| title_short | Extended Kalman Filter-Based SOC Estimation for Lithium Battery Packs |
| title_sort | extended kalman filter based soc estimation for lithium battery packs |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_02040.pdf |
| work_keys_str_mv | AT zhangruoyuan extendedkalmanfilterbasedsocestimationforlithiumbatterypacks AT zhanghailong extendedkalmanfilterbasedsocestimationforlithiumbatterypacks |