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Comparison of Three Well-known Filters for the Battery State of Health Estimation Application

机译:三种用于电池健康状态估计的知名过滤器的比较

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Battery State of Health (SOH) is a vital parameter to maintain the battery as well. As a matter of fact this is the ability of a battery to store energy. It is needless to say that during the lifetime of a battery, its performance gradually decreases, so by using a suitable Battery Management System (BMS), safety and improvement of the usage lifetime can be guaranteed. The traditional methods for indicating usable capacity are basically based on output voltage measuring in the discharge process with a constant current pulse. However, due to load changes the discharge current of batteries in operation almost always fluctuates, which makes it hard to measure the online capacity measurement for the traditional methods. To overcome the above problems, a filter design approach is proposed in this paper to estimate the SOH. This paper by using a generic second order equivalent circuit, that has been used for both VRLA and Li-ion batteries before, presents comparison of three well-known filters performance in the battery SOH estimation application. Parameter estimation have been applied in order to compare and contrast. To verify the performance of the methods, simulations were built in Matlab and final results show accuracy of filters and claim merits and demerits of them.
机译:电池健康状态(SOH)是维护电池的重要参数。事实上,这就是电池存储能量的能力。不用说,在电池的使用寿命期间,其性能会逐渐下降,因此,通过使用合适的电池管理系统(BMS),可以确保安全性和使用寿命的提高。指示可用容量的传统方法基本上是基于在放电过程中以恒定电流脉冲测量输出电压的方法。但是,由于负载变化,工作中的电池放电电流几乎总是波动,这使得传统方法难以测量在线容量。为了克服上述问题,本文提出了一种滤波器设计方法来估计SOH。本文通过使用通用的二阶等效电路(之前已用于VRLA和Li-ion电池),介绍了电池SOH估算应用中三个众所周知的滤波器性能的比较。为了比较和对比,已经应用了参数估计。为了验证该方法的性能,在Matlab中进行了仿真,最终结果显示了滤波器的准确性,并声称了它们的优缺点。

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