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SOC Estimation of Lithium Battery Based on Dual Adaptive Extended Kalman Filter
| Content Provider | Scilit |
|---|---|
| Author | Zheng, Yongliang He, Feng Wang, Wenliang |
| Copyright Year | 2019 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering The estimation accuracy of single extended Kalman filter is not high, also it is affected by the initial value of state of charge (SOC). The second-order RC equivalent circuit model of lithium battery is established, and a joint algorithm, dual extended Kalman filter (DEKF) is proposed. Besides, the covariance matching theory is introduced for DEKF under the complex condition of uncertain noise statistical characteristics to improve the estimation accuracy. The improved DEKF is compared with another joint algorithms, i.e. recursive least squares and extended Kalman filter (RLS-EKF). Through the validation of battery test data, the modified dual extended Kalman filter based on covariance adaptive algorithm can realize real-time online estimation of battery SOC and time-varying parameters, and the estimation accuracy of lithium battery SOC and battery time-varying parameters is higher. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/677/3/032077/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/677/3/032077 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 3 |
| Volume Number | 677 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-12-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Industrial Engineering Extended Kalman Filter |
| Content Type | Text |
| Resource Type | Article |
