Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor
It is necessary to identify the parameters for an equivalent circuit model of Li-ion batteries and understand their degradation conditions in electric vehicles and consumer electronic devices. As a parameter identification method, the recursive least-squares method with a forgetting factor (FRLS) ha...
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2025-01-01
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| author | Takeshi Inoue Daiki Komatsu |
| author_facet | Takeshi Inoue Daiki Komatsu |
| author_sort | Takeshi Inoue |
| collection | DOAJ |
| description | It is necessary to identify the parameters for an equivalent circuit model of Li-ion batteries and understand their degradation conditions in electric vehicles and consumer electronic devices. As a parameter identification method, the recursive least-squares method with a forgetting factor (FRLS) has been used in several applications of battery management systems (BMS). However, the inverse covariance matrix of FRLS is singular and a covariance matrix does not exist depending on the current pattern (in one example, the current is maintained at zero). In this case, the parameter identification using FRLS is divergent. One approach to solving this challenge is to realize FRLS for the Moore-Penrose inverse (MIFRLS). However, when tested with MIFRLS, certain parameters became almost zero when the current was maintained at zero, and the challenge still remained. In this study, we proposed a new method to solve this challenge and demonstrated improvements in identification. First, we verified that a certain parameter becomes almost zero when the current was maintained at zero. We found that this is caused by noise and the divergence term in the covariance matrix owing to the rank reduction. To solve this challenge, we propose a new method called “an eigenvalue addition/deletion MIFRLS” for BMS which removes eigenvectors with large eigenvalues from the covariance matrix to avoid the divergence term. Lastly, we describe the proposed method to consistently identify parameters, as confirmed by fake numerical tests with known true values, and the average computation time was only 1.47 times longer than that of FRLS. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-7ff7a368abf64de09bdb578dabd8f02c2025-08-20T02:11:25ZengIEEEIEEE Access2169-35362025-01-0113735697358510.1109/ACCESS.2025.355860210964739Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting FactorTakeshi Inoue0https://orcid.org/0009-0009-7883-8819Daiki Komatsu1Hitachi Ltd., Research and Development Group, Ibaraki, JapanHitachi Ltd., Research and Development Group, Ibaraki, JapanIt is necessary to identify the parameters for an equivalent circuit model of Li-ion batteries and understand their degradation conditions in electric vehicles and consumer electronic devices. As a parameter identification method, the recursive least-squares method with a forgetting factor (FRLS) has been used in several applications of battery management systems (BMS). However, the inverse covariance matrix of FRLS is singular and a covariance matrix does not exist depending on the current pattern (in one example, the current is maintained at zero). In this case, the parameter identification using FRLS is divergent. One approach to solving this challenge is to realize FRLS for the Moore-Penrose inverse (MIFRLS). However, when tested with MIFRLS, certain parameters became almost zero when the current was maintained at zero, and the challenge still remained. In this study, we proposed a new method to solve this challenge and demonstrated improvements in identification. First, we verified that a certain parameter becomes almost zero when the current was maintained at zero. We found that this is caused by noise and the divergence term in the covariance matrix owing to the rank reduction. To solve this challenge, we propose a new method called “an eigenvalue addition/deletion MIFRLS” for BMS which removes eigenvectors with large eigenvalues from the covariance matrix to avoid the divergence term. Lastly, we describe the proposed method to consistently identify parameters, as confirmed by fake numerical tests with known true values, and the average computation time was only 1.47 times longer than that of FRLS.https://ieeexplore.ieee.org/document/10964739/Battery management systemsrecursive least-squares method with forgetting factorMoore-Penrose inverseparameter identificationeigenvaluepower method |
| spellingShingle | Takeshi Inoue Daiki Komatsu Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor IEEE Access Battery management systems recursive least-squares method with forgetting factor Moore-Penrose inverse parameter identification eigenvalue power method |
| title | Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor |
| title_full | Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor |
| title_fullStr | Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor |
| title_full_unstemmed | Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor |
| title_short | Online Parameter Identification for Equivalent Circuit Model of Li-Ion Batteries Using the Eigenvalue Addition/Deletion Least-Squares Method With a Forgetting Factor |
| title_sort | online parameter identification for equivalent circuit model of li ion batteries using the eigenvalue addition deletion least squares method with a forgetting factor |
| topic | Battery management systems recursive least-squares method with forgetting factor Moore-Penrose inverse parameter identification eigenvalue power method |
| url | https://ieeexplore.ieee.org/document/10964739/ |
| work_keys_str_mv | AT takeshiinoue onlineparameteridentificationforequivalentcircuitmodelofliionbatteriesusingtheeigenvalueadditiondeletionleastsquaresmethodwithaforgettingfactor AT daikikomatsu onlineparameteridentificationforequivalentcircuitmodelofliionbatteriesusingtheeigenvalueadditiondeletionleastsquaresmethodwithaforgettingfactor |