有源短时外部短路下基于aekf的电池荷电状态估计方法

C. Xue, Yifeng Zhao, Zeyu Chen, Bo Zhang
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引用次数: 0

摘要

荷电状态(SOC)估计是电池管理系统的一项关键功能。本研究探讨了在短时间外部短路(ESC)过程中估算电池SOC的可能性。主动短时间ESC有时在可能不会造成严重后果的特殊应用中很有用。本文主要介绍了以下内容:首先,设计了不同SOC和温度下的电池实验ESC。然后利用遗传算法(GA)识别模型参数,并基于自适应扩展卡尔曼滤波(AEKF)估计SOC;结果表明,该方法可以很好地应用于有源ESC过程,误差小于0.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AEKF-based Method of SOC Estimation for Batteries under the Active Short-time External Short Circuit
State of charge (SOC) estimation is a key function of battery management system. This study examines the possibility of estimating the battery SOC under a certain short time External short circuit (ESC) process. Active short time ESC is sometimes useful in special applications that may not cause serious outcomes. The following contents are introduced in this papery. Firstly, ESC of cell experiment is designed under different SOC and temperatures. Then, genetic algorithm (GA) is used to identify model parameter, and SOC is estimated based adaptive extended Kalman filter (AEKF). It is illustrated that the presented SOC estimation can be well used in active ESC process with error less than 0.2%.
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