State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter
State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter
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DOI：
10.1016/j.jpowsour.2008.11.143
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发表时间：
2009-03-15
影响因子：
9.2
通讯作者：
Sunwoo, Myoungho
中科院分区：
文献类型：
--
作者：
Han, Jaehyun;Kim, Dongchul;Sunwoo, Myoungho
铅酸蓄电池广泛用于传统的内燃机车辆和一些电动车辆中。为了提高电池的寿命、性能、可靠性、密度和经济性，需要精确的荷电状态（SoC）估计。卡尔曼滤波器是用于确定SoC的技术之一。该滤波器假设过程和测量噪声协方差值的先验知识。估计误差可能很大，甚至发散时，不正确的先验协方差值被利用。这些估计误差可以通过使用自适应卡尔曼滤波器来减小，该自适应卡尔曼滤波器自适应地修改协方差。在这项研究中，自适应扩展卡尔曼滤波（AEKF）方法被用来估计SoC。AEKF可以减少SoC估计误差，使其比使用先验的过程和测量噪声协方差值更可靠。(C)2008 Elsevier B.V.保留所有权利。
Lead-acid batteries are widely used in conventional internal-combustion-engined vehicles and in some electric vehicles. In order to improve the longevity, performance, reliability, density and economics of the batteries, a precise state-of-charge (SoC) estimation is required. The Kalman filter is one of the techniques used to determine the SoC. This filter assumes an a priori knowledge of the process and measurement noise covariance values. Estimation errors can be large or even divergent when incorrect a priori covariance values are utilized. These estimation errors can be reduced by using the adaptive Kalman filter, which adaptively modifies the covariance. In this study, an adaptive extended Kalman filter (AEKF) method is used to estimate the SoC. The AEKF can reduce the SoC estimation error, making it more reliable than using a priori process and measurement noise covariance values. (C) 2008 Elsevier B.V. All rights reserved.