Tensor Network-Based MIMO Volterra Model for Lithium-Ion Batteries
Tensor Network-Based MIMO Volterra Model for Lithium-Ion Batteries
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DOI：
10.1109/tcst.2022.3232894
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发表时间：
2023-07
影响因子：
4.8
通讯作者：
Yangsheng Hu;R. D. de Callafon;Ning Tian;H. Fang
中科院分区：
文献类型：
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作者：
Yangsheng Hu;R. D. de Callafon;Ning Tian;H. Fang
准确的电池建模是电池管理系统正常运行并在不违反限制的情况下充分发挥电池潜力的基础。在本文中，开发了一种基于张量网络（TN）的锂离子电池Volterra双电容器（VDC）模型，以提高非线性双电容器（NDC）模型的预测性能。结果表明，VDC模型在考虑倍率容量效应和电压恢复效应方面保持了NDC模型的优点。此外，VDC模型能够以更准确的方式同时预测静态和动态非线性。为了估计 VDC 模型中的 TN 核心，提出了 Bond Core Sweeping 算法，并证明该算法可以产生低秩表示。基于实验数据的比较表明，VDC模型比NDC模型和Thevenin模型具有更高的预测精度，显示出增强未来电池应用的巨大前景。
Accurate battery modeling is fundamental for the battery management system to function well and extract the full potential from a battery without violating constraints. In this article, a tensor network (TN)-based Volterra double-capacitor (VDC) model for lithium-ion batteries is developed to improve the prediction performance of the nonlinear double-capacitor (NDC) model. It is shown that the VDC model maintains the advantages of the NDC model to account for the rate capacity effect and the voltage recovery effect. In addition, the VDC model is capable of predicting both static and dynamic nonlinearities simultaneously in a more accurate way. To estimate the TN-cores in the VDC model, a Bond Core Sweeping Algorithm is proposed and shown to lead to a low-rank representation. A comparison based on experimental data demonstrates that the VDC model gives greater prediction accuracy than the NDC model and Thevenin model, showing significant promise to enhance future battery applications.