动态条件下锂离子电池退化模型的多变量分数多项式
Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions
摘要：
锂离子电池的使用寿命和安全性通过电池工作条件的有效监测和调节而得到促进。因此，在电池管理系统上实现快速准确的健康状态（SoH）监控算法至关重要。由于导致电池退化的因素的复杂性和众多性，特别是因为不同的退化过程发生在不同的时间尺度上，并且它们的相互作用起着重要的作用，因此这项任务具有挑战性。数据驱动的方法通过用统计或机器学习模型来近似复杂的过程，从而绕过了这个问题。本文提出了一种数据驱动的方法，尽管它简单且易于计算，但在电池退化的背景下尚未得到充分研究：多变量分数多项式（MFP）回归。模型从一个耗尽的电池的历史数据训练，并用于预测其他电池的SoH。这些数据的特点是通过模拟动态运行条件的变化负载。两个假设的情况下被认为是：一个假设，最近的容量测量是已知的，另一个是只基于标称容量。结果表明，受检电池的退化行为受其历史数据的影响，这得到了低预测误差的支持（考虑到电池寿命终止时的数据，均方根均方误差为1.2%至7.22%）。此外，我们还提供了一个多因素的视角，分析了每个不同因素的影响程度。最后，我们比较了长短期记忆神经网络和其他作品从文献上相同的数据集。我们的结论是，MFP回归是有效的和有竞争力的当代作品，并提供了一些额外的优势，例如在可解释性，概括性和可实施性方面。
英文摘要：
Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite its simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are characterised by varying loads simulating dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent capacity measurement is known, the other is based only on the nominal capacity. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
