Unknown Input Observer for Battery Open Circuit Voltage Estimation: an LMI Approach

B. R. Dewangga, S. Herdjunanto, A. Cahyadi
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引用次数: 1

Abstract

Open Circuit Voltage (OCV) is a vital component in Battery Management System which can be utilized to determine the battery condition namely state of charge (SOC) and state of health (SOH). Since OCV can not be measured when a battery is continuously connected to load, the only way to determine its value is through estimation. In this paper, the battery OCV is estimated using Unknown Input Observer (UIO). The parameters of the UIO are formulated into linear matrix inequality (LMI) to satisfy the stability of a chosen Lyapunov function. By solving the LMI, one possible solution for UIO parameters is obtained. To demontrate the effectiveness of the designed UIO in estimating OCV, simulations of a battery discharged using pulse load profile and varying load profile are performed. The results show that the OCV estimation faithfully tracks the actual OCV where the OCV estimation error tends to zero for a given initial OCV estimation error. Furthermore, the employment of UIO may decrease computational complexity since there is no need to include nonlinear SOC-OCV relationship in the OCV estimation.
电池开路电压估计的未知输入观测器:一种LMI方法
开路电压(OCV)是电池管理系统中的一个重要组成部分,它可以用来确定电池的状态,即充电状态(SOC)和健康状态(SOH)。由于当电池连续连接到负载时,无法测量OCV,因此确定其值的唯一方法是通过估计。本文采用未知输入观测器(UIO)对电池OCV进行估计。为了满足所选李雅普诺夫函数的稳定性,将UIO的参数表示为线性矩阵不等式(LMI)。通过求解LMI,得到了UIO参数的一种可能解。为了验证所设计的UIO在估计OCV方面的有效性,分别对脉冲负载曲线和变负载曲线下的电池放电进行了仿真。结果表明,在给定初始OCV估计误差的情况下,OCV估计误差趋于零的情况下,OCV估计忠实地跟踪了实际OCV。此外,由于不需要在OCV估计中包含非线性SOC-OCV关系,因此使用UIO可以降低计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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