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State of Health Estimation of LiFePO4Battery based on Probability Density Function

机译：基于概率密度函数的LiFepo4battery健康估算状态

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Battery Management System (BMS) is very important for most of the electric vehicle (EV) and battery energy storage system (BESS), BMS can guarantee the safety, operation and even the life of the battery system. Checking and controlling the status of battery within their specified safe operating conditions is exactly the major function of BMS. The state of health (SOH) is a critical parameter of a Li-ion battery, an accurate on-line estimation algorithm of the SOH is important for forecasting the EV driving range and BESS power dispatching. A widely used method to estimate SOH is based on battery capacity, due to the uncertainty, including unit-to-unit variation, measurement noise, operational uncertainties, and model inaccuracy, it's difficult to estimate the SOH by using battery capacity. In this paper, a new method, probability density function to estimate the capacity of LiFePO4 battery by analyzing the charge and discharge data is presented. A comparison of the probability density function and differential voltage analysis (DVA) is provided, shows that the mathematical basis of the algorithm and DVA are in agreement, then present the relationship of dQ/dV vs V, synthesize derivation curve of anode and cathode. Further, in order to get the relationship between derivation curve and capacity of LiFePO4 battery over the lifecycle, the peak intensity, peak voltage, peak number and peak shift is analyzed. Finally, by utilizing the actual operation data, experiments and numerical analysis were conducted, show that this capacity estimation algorithm based on probability density function has better robust performance of the practical application of LiFePO4 battery, and the superiority of this method is verified.
机译：电池管理系统（BMS）是大多数电动汽车（EV）和电池储能系统（BESS）非常重要，BMS可以保证电池系统的安全性，营运，甚至是生命。检查和其指定的安全工作条件范围内控制电池的状态正是BMS的主要功能。健康状态（SOH）是一种锂离子电池的一个关键参数，所述SOH的精确的在线估计算法为预测EV行驶范围和BESS电力调度重要。来估计SOH一种广泛使用的方法是基于电池容量，由于不确定性，包括单元到单元的变化，测量噪声，操作的不确定性，以及模型的不准确性，这是难以通过使用电池容量来估计SOH。在本文中，一种新的方法，概率密度函数，以通过分析所述电荷估计LiFePO4电池的容量和放电的数据被呈现。概率密度函数和差分电压分析（DVA）的比较提供，表明该算法和DVA的数学基础是一致的，那么呈现dQ的/ DV的VS V，阳极和阴极的合成推导曲线的关系。此外，为了克服生命周期峰值电压微分曲线和LiFePO4电池的容量之间的关系时，峰值强度，峰值数和峰位移进行分析。最后，通过利用实际的操作数据，实验和数值分析进行的，结果表明，基于概率密度函数这个容量估计算法具有LiFePO4电池的实际应用的更好的鲁棒性能，并且该方法的优越性进行了验证。

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