基于EKF的锂电池SOC估算与试验研究
Li-ion battery SOC estimation and test research based on EKF
查看参考文献14篇
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文摘
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锂离子电池以其无泄漏、无污染、无噪声等优点,近年来广泛应用于工业及生活领域。目前常用的基于扩展卡尔曼滤波(EKF)的锂电池SOC(荷电状态)估计方法由于建模不准确而导致估计结果误差较大,严重影响到电池管理系统的性能及整机系统的控制。针对该问题,采用精度较高的Randles模型,并在拟合电池的OCV(开路电压)-SOC曲线时通过引入自然指数函数并增加多项式阶数等方法提高拟合精度。使用EKF对锂电池SOC进行估计,与理论结果相比模型改进后估计误差的标准差比改进前下降了64.43%。试验结果表明通过改进电池模型大大提高了基于EKF方法的锂电池SOC估计精度,对于提高电池管理系统以及整机系统性能具有重要意义。 |
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其他语种文摘
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Li-ion battery has been broadly used in industrial and commercial areas in recent years for its no leaks, no pollution and no noises. The currently used state of charge (SOC) estimation methods based on extended Kalman filter (EKF) doesn't have a very good accuracy due to the modeling error, which could influence the performance of the battery management system (BMS) and the control of the host machine. Considering about this, Randles model was adopted which has a good precision and exponential function and more orders of the polynomial were introduced to increase modeling precision. In the experiment, the standard deviation of the estimation error based on the improved model declined 64.43% comparing to the original model. The experiment result shows through improving the battery model the SOC estimation accuracy basing on EKF is improved greatly, which is significant for the performance of BMS and the host machine. |
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来源
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电源技术
,2015,39(12):2587-2589,2615 【扩展库】
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关键词
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SOC估计
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扩展卡尔曼滤波
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锂电池
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地址
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中国科学院沈阳自动化研究所, 辽宁, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1002-087X |
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学科
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电工技术 |
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文献收藏号
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CSCD:5598676
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14
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