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Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

机译:基于正交配置和改进扩展卡尔曼滤波器的基于锂离子电池热电化学模型的状态估计

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摘要

This paper investigates the state estimation of a high-fidelity spatially resolved thermal-electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1% in less than 200 s despite a 30% error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.
机译:本文研究了高保真空间分辨热电化学锂离子电池模型的状态估计,该模型通常称为伪二维模型。构成模型的偏微分代数方程(PDAE)使用Chebyshev正交搭配在空间上离散,从而可以在高达C速率的情况下进行快速而准确的仿真。然后将伪2D模型的这种实现方式与扩展的Kalman滤波算法结合使用,以用于微分代数方程式,以估计模型的状态。状态估计算法能够从电流,电压和温度测量值快速恢复模型状态。结果表明,尽管电池初始充电状态和附加测量噪声的误差为30 m%(标准差为10 mV和0.5 K),但状态估计值的误差在不到200 s内降至1%以下。

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