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Modelling of Lithium-ion battery and SOC estimation using simple and extended discrete Kalman Filters for Aircraft energy management

机译:锂离子电池建模和SOC估计,使用简单和扩展的离散卡尔曼滤波器进行飞机能量管理

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As an energy storage unit Lithium-ion batteries play an important role in More Electrical Aircraft and accurate knowledge of its behaviour and state of charge (SOC) are required to ensure the safety of the Aircraft Electrical System (AES) and its energy management. This paper details the conception of a 2nd order equivalent electrical circuit (EEC) battery model with SOC, current (magnitude and direction) and temperature dependant parameters. A 50Ah LiFePO4 battery is subjected to Hybrid Pulse Power Characterisation (HPPC) for parameter identification and constant current discharge tests for model validation. The model shows good performance between 100% & 10% SOC with a maximum error of less than ±1% Subsequently, for SOC estimation, the Discrete Simple Kalman Filter (KF) is applied with the Open-Circuit Voltage (OCV) depending linearly on SOC, I and T. Secondly the Discrete Extended KF (EKF) is applied with the model parameters and OCV varying non-linearly with SOC. Their performance is then compared to that of the Coulomb-counting method with the EKF showing good tracking performance: converging to the true SOC given false initial values and the mean SOC estimation error reducing from ±7% with KF to less than ±1% with EKF.
机译:锂离子电池作为能量存储单元,在“更多电动飞机”中起着重要作用,需要准确了解其行为和荷电状态(SOC),以确保飞机电气系统(AES)的安全性和能源管理。本文详细介绍了具有SOC,电流(幅度和方向)和温度相关参数的二阶等效电路(EEC)电池模型的概念。对50Ah的LiFePO4电池进行混合脉冲功率表征(HPPC)以进行参数识别,并进行恒流放电测试以进行模型验证。该模型显示出100%到10%SOC之间的良好性能,最大误差小于±1%。随后,为了进行SOC估计,对离散简单卡尔曼滤波器(KF)施加了开路电压(OCV),线性依赖于SOC,I和T。第二,应用离散扩展KF(EKF),模型参数和OCV随SOC非线性变化。然后将它们的性能与库仑计数法的性能进行比较,EKF表现出良好的跟踪性能:在给定初始值不正确的情况下收敛到真实的SOC,平均SOC估计误差从KF的±7%降低到KF的±1%以下EKF。

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