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State of health estimation for lithium ion batteries based on an equivalent-hydraulic model: An iron phosphate application

机译:基于等效液压模型的锂离子电池健康状况评估:磷酸铁应用

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摘要

A two-step approach for state-of-health (SOH) estimation of a lithium-ion (Li-ion) battery is developed. In the first step, state-of-charge (SOC) estimation is performed by a constrained extended Kalman filter (EKF) based on the so-called equivalent-hydraulic model. The latter model allows to characterize the internal battery state and main physical parameters while being suitable for on-line computation. The internal battery states are further exploited in the second step of the approach to obtain parameter-based SOH indicators that characterize the long term evolution of the diffusion and charge transfer processes associated to aging. Capacity and power fade indicators are determined by using notably an instrumental variable method in order to obtain unbiased parameter estimates in the presence of heteroscedastic colored noise. The methodology is validated on both simulation and experimental data for a lithium iron phosphate (LFP) half battery cell. This also provides insight on the properties of the LFP electrodes.
机译:开发了一种用于锂离子(Li-ion)电池健康状态(SOH)估计的两步方法。第一步,基于所谓的等效液压模型,通过约束扩展卡尔曼滤波器(EKF)进行充电状态(SOC)估计。后一种模型可以表征内部电池状态和主要物理参数,同时适合进行在线计算。在该方法的第二步中,将进一步利用内部电池状态来获得基于参数的SOH指标,这些指标可表征与老化相关的扩散和电荷转移过程的长期演变。容量和功率衰减指示器通过特别使用仪器变量方法来确定,以便在存在异方差有色噪声的情况下获得无偏参数估计。该方法论已在磷酸铁锂(LFP)半电池的模拟和实验数据中得到验证。这也提供了有关LFP电极特性的见解。

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