Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation
The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/2/122 |
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| author | Peipei Xu Junqiu Li Chao Sun Guodong Yang Fengchun Sun |
| author_facet | Peipei Xu Junqiu Li Chao Sun Guodong Yang Fengchun Sun |
| author_sort | Peipei Xu |
| collection | DOAJ |
| description | The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%. |
| first_indexed | 2024-03-09T05:36:42Z |
| format | Article |
| id | doaj.art-69313388c4f5454eadd556346135455a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:36:42Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-69313388c4f5454eadd556346135455a2023-12-03T12:28:21ZengMDPI AGElectronics2079-92922021-01-0110212210.3390/electronics10020122Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity DegradationPeipei Xu0Junqiu Li1Chao Sun2Guodong Yang3Fengchun Sun4National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaThe accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.https://www.mdpi.com/2079-9292/10/2/122state of charge (SOC)equivalent circuit model (ECM)capacity degradation modelforgetting factor recursive least squares (FFRLS) |
| spellingShingle | Peipei Xu Junqiu Li Chao Sun Guodong Yang Fengchun Sun Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation Electronics state of charge (SOC) equivalent circuit model (ECM) capacity degradation model forgetting factor recursive least squares (FFRLS) |
| title | Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation |
| title_full | Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation |
| title_fullStr | Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation |
| title_full_unstemmed | Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation |
| title_short | Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation |
| title_sort | adaptive state of charge estimation for lithium ion batteries by considering capacity degradation |
| topic | state of charge (SOC) equivalent circuit model (ECM) capacity degradation model forgetting factor recursive least squares (FFRLS) |
| url | https://www.mdpi.com/2079-9292/10/2/122 |
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