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Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles

机译:基于熵的电动汽车锂离子动力电池连接故障检测

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This paper proposes a method of fault detection of the connection of Lithium-Ion batteries based on entropy for electric vehicle. In electric vehicle operation process, some factors, such as road conditions, driving habits, vehicle performance, always affect batteries by vibration, which easily cause loosing or virtual connection between batteries. Through the simulation of the battery charging and discharging experiment under vibration environment, the data of voltage fluctuation can be obtained. Meanwhile, an optimal filtering method is adopted using discrete cosine filter method to analyze the characteristics of system noise, based on the voltage set when batteries are working under different vibration frequency. Experimental data processed by filtering is analyzed based on local Shannon entropy, ensemble Shannon entropy and sample entropy. And the best way to find a method of fault detection of the connection of lithium-ion batteries based on entropy is presented for electric vehicle. The experimental data shows that ensemble Shannon entropy can predict the accurate time and the location of battery connection failure in real time. Besides electric-vehicle industry, this method can also be used in other areas in complex vibration environment. (C) 2015 Elsevier B.V. All rights reserved.
机译:提出了一种基于熵的电动汽车锂离子电池连接故障检测方法。在电动汽车的操作过程中,诸如路况,驾驶习惯,车辆性能等因素总是会通过振动影响电池,从而容易导致电池之间的松动或虚假连接。通过对振动环境下电池充放电实验的仿真,可以获得电压波动的数据。同时,基于电池在不同振动频率下工作时设定的电压,采用离散余弦滤波方法来分析系统噪声的特性,采用了一种最佳滤波方法。基于局部香农熵,集合香农熵和样本熵对经过滤波处理的实验数据进行了分析。提出了一种基于熵的电动汽车锂离子电池连接故障检测方法的最佳方法。实验数据表明,集合香农熵能够实时预测准确的时间和电池连接故障的位置。除了电动汽车行业外,该方法还可以用于复杂振动环境中的其他区域。 (C)2015 Elsevier B.V.保留所有权利。

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