Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries
Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries
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DOI：
10.1109/tie.2014.2336599
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发表时间：
2015-02-01
影响因子：
7.7
通讯作者：
Anwar, Sohel
中科院分区：
文献类型：
--
作者：
Sidhu, Amardeep;Izadian, Afshin;Anwar, Sohel
本文将自适应故障诊断技术应用于锂离子电池故障诊断中。诊断过程由多个非线性模型表示签名故障，如过充电和过放电，导致显着的模型参数变化。锂离子（LiFePO4）电池的阻抗谱，沿着与等效电路方法，构建非线性电池的签名故障模型。扩展卡尔曼滤波器被用来估计每个模型的端电压，并产生残差信号。在多模型自适应估计技术中使用残差信号来生成确定签名故障的概率。可以看出，通过使用该方法，可以准确地检测出特征故障，从而提供了一种有效的诊断锂离子电池故障的方法。
In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion (LiFePO4) cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.