Robust hybrid estimator for the state of charge of a lithium-ion battery
The use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batt...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1069364/full |
| _version_ | 1797948170712383488 |
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| author | Ayokunle Awelewa Koto Omiloli Isaac Samuel Ayobami Olajube Olawale Popoola |
| author_facet | Ayokunle Awelewa Koto Omiloli Isaac Samuel Ayobami Olajube Olawale Popoola |
| author_sort | Ayokunle Awelewa |
| collection | DOAJ |
| description | The use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batteries in the presence of disturbance signals arising from different battery types remains a challenge. Hence, this paper presents a hybrid estimator that combines the extended Kalman filter (EKF) and sliding mode observer (SMO) via a switching function and tracking closed loop to achieve the qualities of noise cancellation and disturbance rejection. Hybridization was carried out in such a way that the inactive observer tracks the output of the used observer, simultaneously feeding back a zero-sum signal to the input gain of the used observer. The results obtained show that noise filtering is preserved at a convergence time of .01 s. Also, the state of charge estimation interval improves greatly from a range of [1, .93] and [.94, .84] obtained from the extended Kalman filter and sliding mode observer, respectively, to a range of [1, 0], in spite of the added disturbance signals from a lithium–nickel (INR 18650) battery type. |
| first_indexed | 2024-04-10T21:39:06Z |
| format | Article |
| id | doaj.art-49b803502e404ca698102dd34033cf3c |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T21:39:06Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-49b803502e404ca698102dd34033cf3c2023-01-19T06:49:30ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10693641069364Robust hybrid estimator for the state of charge of a lithium-ion batteryAyokunle Awelewa0Koto Omiloli1Isaac Samuel2Ayobami Olajube3Olawale Popoola4Department of Electrical and Information Engineering, Covenant University, Ota, NigeriaDepartment of Electrical and Information Engineering, Covenant University, Ota, NigeriaDepartment of Electrical and Information Engineering, Covenant University, Ota, NigeriaDepartment of Electrical and Computer Engineering, Florida State University, Tallahassee, FL, United StatesDepartment of Electrical Engineering, Centre for Energy and Electric Power, Tshwane University of Technology, Pretoria, South AfricaThe use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batteries in the presence of disturbance signals arising from different battery types remains a challenge. Hence, this paper presents a hybrid estimator that combines the extended Kalman filter (EKF) and sliding mode observer (SMO) via a switching function and tracking closed loop to achieve the qualities of noise cancellation and disturbance rejection. Hybridization was carried out in such a way that the inactive observer tracks the output of the used observer, simultaneously feeding back a zero-sum signal to the input gain of the used observer. The results obtained show that noise filtering is preserved at a convergence time of .01 s. Also, the state of charge estimation interval improves greatly from a range of [1, .93] and [.94, .84] obtained from the extended Kalman filter and sliding mode observer, respectively, to a range of [1, 0], in spite of the added disturbance signals from a lithium–nickel (INR 18650) battery type.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1069364/fullextended Kalman filterlithium-ion batterySliding mode observerstate of chargehybridstate |
| spellingShingle | Ayokunle Awelewa Koto Omiloli Isaac Samuel Ayobami Olajube Olawale Popoola Robust hybrid estimator for the state of charge of a lithium-ion battery Frontiers in Energy Research extended Kalman filter lithium-ion battery Sliding mode observer state of charge hybrid state |
| title | Robust hybrid estimator for the state of charge of a lithium-ion battery |
| title_full | Robust hybrid estimator for the state of charge of a lithium-ion battery |
| title_fullStr | Robust hybrid estimator for the state of charge of a lithium-ion battery |
| title_full_unstemmed | Robust hybrid estimator for the state of charge of a lithium-ion battery |
| title_short | Robust hybrid estimator for the state of charge of a lithium-ion battery |
| title_sort | robust hybrid estimator for the state of charge of a lithium ion battery |
| topic | extended Kalman filter lithium-ion battery Sliding mode observer state of charge hybrid state |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1069364/full |
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