期刊
ENERGY CONVERSION AND MANAGEMENT
卷 50, 期 12, 页码 3182-3186出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2009.08.015
关键词
Electric vehicle; Battery management system (BMS); State of Charge (SOC); Adaptive extended Kalman filter (AEKF)
资金
- Scientific Research Foundation for the Returned Overseas Chinese Scholars
- State Education Ministry [2008-890]
- Jiangsu Provincial Natural Science Foundation of China [BK2009144]
- Shaanxi Provincial Natural Science Foundation of China [SJ08E218]
Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as AEKFAh, is proposed. This method uses the adaptive Kalman flitering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting. (C) 2009 Elsevier Ltd. All rights reserved.
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