Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network
Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network
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DOI：
10.1016/j.apenergy.2020.116159
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发表时间：
2021-01-15
期刊：
影响因子：
11.2
通讯作者：
Van Mierlo, Joeri
中科院分区：
文献类型：
--
作者：
Khaleghi, Sahar;Karimi, Danial;Van Mierlo, Joeri
电池健康诊断对于保证其运行的应用的可用性和可靠性至关重要。数据驱动的健康诊断方法，特别是机器学习方法，由于其简单性和准确性而受到关注。然而，需要一种机器学习方法，该方法可以科普电池单元的非线性行为，但它避免了高计算复杂性，从而在在线应用中有效地工作。机器学习方法的准确性和鲁棒性在很大程度上取决于覆盖各种电池老化模式的全面电池退化数据集的可用性。虽然许多研究未能解决上述要求，本研究试图解决这些问题。二十一个镍锰钴氧化物电池在各种操作条件下循环了两年多，以获取数据。探索部分充电电压曲线，以提取描述电池健康轨迹的健康指标。之后，一个非线性自回归外源（NARX）模型的开发，以捕捉健康指标和电池的健康状态之间的依赖关系。最后，验证了该方法的准确性和鲁棒性。结果表明，NARX的能力，以健康诊断的锂离子电池的最大均方根误差为0.46的未经训练的数据。这表明，该模型具有较高的估计精度，较低的计算复杂度，以及无论其老化模式的电池健康估计的能力。这些特点指出了所提出的在线健康诊断技术的实用性。
Battery health diagnostics is extremely crucial to guaranty the availability and reliability of the application in which they operate. Data-driven health diagnostics methods, particularly machine learning methods, have gained attention due to their simplicity and accuracy. However, a machine learning method is desired which can cope with the nonlinear behavior of battery cells and yet it avoids high computational complexity to work efficiently in online applications. The accuracy and robustness of machine learning methods strongly depend on the availability of a comprehensive battery degradation dataset that covers a variety of battery aging patterns. While many studies fail to address the aforementioned requirements, this study attempts to address them. Twenty-one nickel manganese cobalt oxide battery cells have been cycled in various operating conditions for more than two years to acquire the data. The partial charging voltage curve is explored to extract the health indicators that describe the health trajectory of the battery. Afterward, a nonlinear autoregressive exogenous (NARX) model is developed to capture the dependency between the health indicators and state of health of battery cells. Finally, the accuracy and robustness of the proposed method are validated. The results demonstrate the ability of NARX to health diagnosis of lithium-ion batteries with a maximum root mean squared error of 0.46 for untrained data. This indicates that the proposed model has high estimation accuracy, low computational complexity, and the ability of battery health estimation regardless of its aging pattern. These features point out the practicability of the proposed technique on online health diagnostics.