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A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model
IEEE Transactions on Industrial Electronics ( IF 7.7 ) Pub Date : 2019-10-01 , DOI: 10.1109/tie.2018.2880668
Jinhao Meng , Daniel-Ioan Stroe , Mattia Ricco , Guangzhao Luo , Remus Teodorescu

The performance of model-based state-of-charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the lithium-ion battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model-based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, partial least squares regression is able to establish a series of piecewise linear battery models automatically. One element state-space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the extended Kalman filter with two resistance and capacitance equivalent circuit model and the adaptive unscented Kalman filter with least squares support vector machines.

中文翻译:

一种基于简化模型的锂离子电池动态线性模型充电状态估计方法

基于模型的充电状态 (SOC) 估计方法的性能依赖于准确的电池模型。因此提出了非线性模型来准确描述锂离子电池的外部特性。需要非线性估计算法和在线参数识别方法来保证非线性电池模型基于模型的SOC估计的准确性。本文提出了一种形成动态线性电池模型的新方法,该方法能够应用线性卡尔曼滤波器进行 SOC 估计,同时避免使用在线参数识别方法。通过移动窗口技术,偏最小二乘回归能够自动建立一系列分段线性电池模型。然后获得一个元素状态空间方程来估计线性卡尔曼滤波器的 SOC。在 LiFePO4 电池上的实验证明了该方法与具有两个电阻和电容等效电路模型的扩展卡尔曼滤波器和具有最小二乘支持向量机的自适应无迹卡尔曼滤波器相比的有效性。
更新日期:2019-10-01
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