Online State-of-Health Estimation of VRLA Batteries Using State of Charge
Online State-of-Health Estimation of VRLA Batteries Using State of Charge
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DOI：
10.1109/tie.2012.2186771
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发表时间：
2013-01-01
影响因子：
7.7
通讯作者：
Farrokhi, Mohammad
中科院分区：
文献类型：
--
作者：
Shahriari, Mehrnoosh;Farrokhi, Mohammad
本文提出了一种在线评估阀控式铅酸（VRLA）电池健康状态（SOH）的方法。所提出的方法基于电池的充电状态（SOC）。使用扩展卡尔曼滤波器和电池的神经网络模型来估计 SOC。然后，利用模糊逻辑和递归最小二乘法，根据SOC和电池开路电压之间的关系在线估计SOH。为了在电池工作时获得开路电压，采用反射充电过程。实验结果表明可以很好地估计 VRLA 电池的 SOH。
This paper presents an online method for the estimation of the state of health (SOH) of valve-regulated lead acid (VRLA) batteries. The proposed method is based on the state of charge (SOC) of the battery. The SOC is estimated using the extended Kalman filter and a neural-network model of the battery. Then, the SOH is estimated online based on the relationship between the SOC and the battery open-circuit voltage using fuzzy logic and the recursive least squares method. To obtain the open-circuit voltage while the battery is operating, the reflective charging process is employed. Experimental results show good estimation of the SOH of VRLA batteries.