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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zahid, T. Guoqing Xu Weimin Li Lei Zhao Kun Xu |
| Copyright Year | 2014 |
| Description | Author affiliation: Shenzhen Inst. of Adv. Technol., Shenzhen, China (Zahid, T.; Guoqing Xu; Weimin Li; Lei Zhao; Kun Xu) |
| Abstract | For the development of an innovative battery management system an accurate and a reliable technique for reliability of battery operations for a hybrid/electric vehicle. This paper illustrates an online SOC estimation method of a $LiFePo_{4}$ battery for applications in electric vehicles by using a particle filter. Additionally, a five comparison experiments with different open circuit voltage curves exhibits that the particle filter is a promising alternative, even if it is computationally more demanding then extended Kalman filter. |
| Starting Page | 1061 |
| Ending Page | 1065 |
| File Size | 472451 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479941001 |
| DOI | 10.1109/ICInfA.2014.6932806 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Decision support systems open circuit voltage(OCV) particle filter (PF) extended Kalman filter(EKF) Estimation Battery management systems state of charge(SOC) Particle filters battery management system (BMS) Batteries Kalman filters Thevenin equivalent circuit model |
| Content Type | Text |
| Resource Type | Article |
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