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A method for joint estimation of state-of-charge and available energy of LiFePO_4 batteries

机译:一种联合估计LiFePO_4电池充电状态和可用能量的方法

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摘要

The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.
机译:充电状态(SOC)是电动汽车(EV)电池管理系统(BMS)的关键指标。但是,在能量存储系统中,可用能量也起着重要作用。通过能量状态(SOE)的估计结果,我们可以进一步估计如果应用低功率需求,高功率需求甚至动态功率需求,电池将持续多长时间。与SOC不同,SOE不仅是电流的积分,而且还是电压的积分,其中包括锂离子电池的非线性。由于存在由电流或电压测量噪声引起的累积误差,因此提出了一种基于粒子滤波的联合估计器来估计SOC和SOE。基于IFP1865140型电池在恒定和动态电流条件下进行了验证实验。为了进一步验证所提出方法的鲁棒性,在动态温度下进行了实验。实验结果证明,该方法能够获得准确,鲁棒的SOC和SOE估计结果。

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