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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jinlong Zhang Chaoying Xia |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Electrical Engineering and Automation, Tianjin University, Z.P.300072, China (Jinlong Zhang; Chaoying Xia) |
| Abstract | As a vital part of the battery employed systems, battery management system (BMS) must correctly estimate values descriptive of the battery's present operating condition [1]. Without a doubt, state of charge (SOC) is a key battery state for BMS to estimate. This paper carefully analyzes the operating performance of VRLA battery through a large number of experimental tests; a statistical parameter identification strategy is employed in electromotive force (EMF) SOC estimation method, then this EMF method is combined with ampere-hour (Ah) counting through a parallel feedback weighting structure, finally an improved SOC estimator is built up. A BMS platform based on TI DSP2407 is established; besides, a BMS monitoring system is built in VC++ as an auxiliary tool. The results tend to be satisfactory. |
| Starting Page | 5938 |
| Ending Page | 5942 |
| File Size | 1939928 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424467129 |
| e-ISBN | 9781424467129 |
| DOI | 10.1109/WCICA.2010.5554485 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | System-on-a-chip Batteries Discharges Estimation Parameter estimation Monitoring Resistance electromotive force Battery management system statistical parameter identification state of charge |
| Content Type | Text |
| Resource Type | Article |
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