Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/14/5558 |
| _version_ | 1797589429064302592 |
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| author | Hend M. Fahmy Rania A. Swief Hany M. Hasanien Mohammed Alharbi José Luis Maldonado Francisco Jurado |
| author_facet | Hend M. Fahmy Rania A. Swief Hany M. Hasanien Mohammed Alharbi José Luis Maldonado Francisco Jurado |
| author_sort | Hend M. Fahmy |
| collection | DOAJ |
| description | This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods. |
| first_indexed | 2024-03-11T01:06:33Z |
| format | Article |
| id | doaj.art-36fa6df6d8ca4d34ad99aab22e174943 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T01:06:33Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-36fa6df6d8ca4d34ad99aab22e1749432023-11-18T19:12:11ZengMDPI AGEnergies1996-10732023-07-011614555810.3390/en16145558Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman FilterHend M. Fahmy0Rania A. Swief1Hany M. Hasanien2Mohammed Alharbi3José Luis Maldonado4Francisco Jurado5Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, EgyptElectrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, EgyptElectrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, EgyptElectrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi ArabiaDepartment of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, SpainDepartment of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, SpainThis paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.https://www.mdpi.com/1996-1073/16/14/5558Li-ion batteriesbattery management system (BMS)state of charge (SoC)battery modelparameter identificationKalman filters |
| spellingShingle | Hend M. Fahmy Rania A. Swief Hany M. Hasanien Mohammed Alharbi José Luis Maldonado Francisco Jurado Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter Energies Li-ion batteries battery management system (BMS) state of charge (SoC) battery model parameter identification Kalman filters |
| title | Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter |
| title_full | Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter |
| title_fullStr | Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter |
| title_full_unstemmed | Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter |
| title_short | Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter |
| title_sort | hybrid state of charge estimation of lithium ion battery using the coulomb counting method and an adaptive unscented kalman filter |
| topic | Li-ion batteries battery management system (BMS) state of charge (SoC) battery model parameter identification Kalman filters |
| url | https://www.mdpi.com/1996-1073/16/14/5558 |
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