Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach
Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification suc...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8231243 |
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| author | Ming Zhang Kai Wang Yan-ting Zhou |
| author_facet | Ming Zhang Kai Wang Yan-ting Zhou |
| author_sort | Ming Zhang |
| collection | DOAJ |
| description | Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation. |
| format | Article |
| id | doaj-art-5deb4b92c716428b96d99eef0e94d8d7 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5deb4b92c716428b96d99eef0e94d8d72025-08-20T02:06:03ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/82312438231243Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering ApproachMing Zhang0Kai Wang1Yan-ting Zhou2College of Electrical Engineering, Qingdao University, Qingdao 266071, ChinaCollege of Electrical Engineering, Qingdao University, Qingdao 266071, ChinaCollege of Electrical Engineering, Qingdao University, Qingdao 266071, ChinaFiltering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.http://dx.doi.org/10.1155/2020/8231243 |
| spellingShingle | Ming Zhang Kai Wang Yan-ting Zhou Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach Complexity |
| title | Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach |
| title_full | Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach |
| title_fullStr | Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach |
| title_full_unstemmed | Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach |
| title_short | Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach |
| title_sort | online state of charge estimation of lithium ion cells using particle filter based hybrid filtering approach |
| url | http://dx.doi.org/10.1155/2020/8231243 |
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