A Multi-Scale Time Method for the State of Charge and Parameter Estimation of Lithium-Ion Batteries Using MIUKF-EKF
Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the...
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Published in | Frontiers in energy research Vol. 10 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Frontiers Media S.A
10.08.2022
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Abstract | Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the Kalman filter are widely used. However, it is difficult to estimate SOC online and get high accuracy results. This article proposes a method for parameter identification and SOC estimation for lithium-ion batteries. Because the lithium-ion battery has slow-varying parameters (such as internal resistance, and polarization resistance), and the SOC has fast-varying characteristics, so a multi-scale multi-innovation unscented Kalman filter and extended Kalman filter (MIUKF-EKF) are used to perform online measurement of battery parameters and SOC estimation in this method. The battery parameters are estimated with a macro-scale, and the SOC is estimated with a micro-scale. This method can improve the estimation accuracy of the SOC in real-time. Results of experiments indicate that the algorithm has higher accuracy in online parameter identification and SOC estimation than in the dual extended Kalman filter (DEKF) algorithm. |
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AbstractList | Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the Kalman filter are widely used. However, it is difficult to estimate SOC online and get high accuracy results. This article proposes a method for parameter identification and SOC estimation for lithium-ion batteries. Because the lithium-ion battery has slow-varying parameters (such as internal resistance, and polarization resistance), and the SOC has fast-varying characteristics, so a multi-scale multi-innovation unscented Kalman filter and extended Kalman filter (MIUKF-EKF) are used to perform online measurement of battery parameters and SOC estimation in this method. The battery parameters are estimated with a macro-scale, and the SOC is estimated with a micro-scale. This method can improve the estimation accuracy of the SOC in real-time. Results of experiments indicate that the algorithm has higher accuracy in online parameter identification and SOC estimation than in the dual extended Kalman filter (DEKF) algorithm. |
Author | Chen, Zexing Liao, Wu Ji, Shiyu Sun, Yi |
Author_xml | – sequence: 1 givenname: Shiyu surname: Ji fullname: Ji, Shiyu – sequence: 2 givenname: Yi surname: Sun fullname: Sun, Yi – sequence: 3 givenname: Zexing surname: Chen fullname: Chen, Zexing – sequence: 4 givenname: Wu surname: Liao fullname: Liao, Wu |
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CitedBy_id | crossref_primary_10_1149_1945_7111_acd148 crossref_primary_10_1016_j_est_2024_111222 crossref_primary_10_3390_app131910910 crossref_primary_10_3390_en16166013 crossref_primary_10_1002_cta_3788 crossref_primary_10_1007_s11581_024_05678_z crossref_primary_10_23919_PCMP_2023_000232 crossref_primary_10_1002_ese3_1674 crossref_primary_10_1016_j_renene_2023_119277 |
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Title | A Multi-Scale Time Method for the State of Charge and Parameter Estimation of Lithium-Ion Batteries Using MIUKF-EKF |
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