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Modeling changes in the state-of-charge open circuit voltage curve by using regressed parameters in a reduced order physics based model

机译:通过在基于物理的降阶模型中使用回归参数来模拟充电状态开路电压曲线的变化

摘要

A method for modeling changes in the state of charge vs. open circuit voltage (SOC-OCV) curve for a lithium-ion battery cell as it ages. During battery pack charging, voltage and current data are gathered for a battery cell. A set of state equations are used to determine the stoichiometry and state of charge of the cathode half-cell based on the charging current profile over time. The voltage and current data, along with the stoichiometry and state of charge of the cathode half-cell, are then used to estimate maximum and minimum solid concentration values at the anode, using an error function parameter regression/optimization. With stoichiometric conditions at both the cathode and anode calculated, the cell's capacity and a new SOC-OCV curve can be determined.
机译:一种用于建模随时间变化的锂离子电池单元的充电状态与开路电压(SOC-OCV)曲线变化的方法。在电池组充电期间,将收集电池单元的电压和电流数据。一组状态方程式用于根据随时间变化的充电电流曲线确定阴极半电池的化学计量和荷电状态。然后,使用误差函数参数回归/优化,将电压和电流数据以及阴极半电池的化学计量和荷电状态一起用于估计阳极处的最大和最小固体浓度值。通过计算阴极和阳极的化学计量条件,可以确定电池的容量和新的SOC-OCV曲线。

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