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首页> 外文期刊>Journal of power sources >Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries
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Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries

机译:卡尔曼滤波器用于板载锂离子电池的充电状态估计和峰值功率能力分析

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摘要

To evaluate the continuous and instantaneous load capability of a battery, this paper describes a joint estimator for state-of-charge (SOC) and state-of-function (SOF) of lithium-ion batteries (LIB) based on Kalman filter (KF). The SOC is a widely used index for remain useful capacity left in a battery. The SOF represents the peak power capability of the battery. It can be determined by real-time SOC estimation and terminal voltage prediction, which can be derived from impedance parameters. However, the open circuit-voltage (OCV) of LiFePO4 is highly nonlinear with SOC, which leads to the difficulties in SOC estimation. To solve these problems, this paper proposed an onboard SOC estimation method. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery, where the OCV is regarded as a linearized function of SOC. Then, the system states are estimated based on the KF. Besides, the factors that influence peak power capability are analyzed according to statistical data. Finally, the performance of the proposed methodology is demonstrated by experiments conducted on a LiFePO4 LIBs under different operating currents and temperatures. Experimental results indicate that the proposed approach is suitable for battery onboard SOC and SOF estimation. (C) 2016 Elsevier B.V. All rights reserved.
机译:为了评估电池的连续和瞬时负载能力,本文描述了一种基于卡尔曼滤波器(KF)的锂离子电池(LIB)荷电状态(SOC)和功能状态(SOF)联合估计器)。 SOC是电池剩余可用容量的一种广泛使用的指标。 SOF代表电池的峰值功率容量。它可以通过实时SOC估算和端电压预测来确定,这可以从阻抗参数中得出。然而,LiFePO4的开路电压(OCV)与SOC高度非线性,这导致SOC估计困难。为了解决这些问题,本文提出了一种车载SOC估计方法。首先,开发了简化的线性化等效电路模型来模拟电池的动态特性,其中OCV被视为SOC的线性化函数。然后,基于KF估计系统状态。此外,根据统计数据分析影响峰值功率能力的因素。最后,通过在不同工作电流和温度下在LiFePO4 LIB上进行的实验证明了所提出方法的性能。实验结果表明,该方法适用于电池车载SOC和SOF估计。 (C)2016 Elsevier B.V.保留所有权利。

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