卡尔曼滤波与神经网络在电池状态估计中的比较研究

A. Hussein
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引用次数: 26

摘要

电池管理系统(BMS)必须准确估计电池的荷电状态(SOC),以延长电池的使用寿命并确保其可靠运行。由于电池具有广泛的应用,因此SOC估算要求和方法因应用而异。本文比较了扩展卡尔曼滤波(EKF)和人工神经网络(ANN)两种SOC估计方法。EKF是一种非线性最优估计器,用于用状态空间模型估计非线性动态系统的内部状态。另一方面,人工神经网络是一种数学模型,由受生物神经网络启发的相互连接的人工神经元组成,用于根据系统的一些历史数据预测动态系统的输出。为了收集数据来评估这些方法,在商用锂离子(Li-ion)电池上进行了脉冲放电测试。给出了结果并进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study
Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.
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