Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs
Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs
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DOI：
10.1109/ccdc.2018.8408010
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发表时间：
2018-06
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影响因子：
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通讯作者：
Martin Kupper;J. Brenneisen;Oliver Stark;Stefan Krebs;S. Hohmann
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文献类型：
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作者：
Martin Kupper;J. Brenneisen;Oliver Stark;Stefan Krebs;S. Hohmann
本文提出了一种用于大规模电池系统荷电状态和分支电流估计的级联分数阶卡尔曼滤波器。作为一个集中的方法来估计一个大规模的系统是昂贵的努力和时间方面，分区成较小的，因此，更简单的子系统的应用。由于整个系统被划分为更小的单元，因此允许局部计算并降低了复杂性。在这些分布式系统中，通常，子系统之间进行通信以交换相关数据。使用基于网格电流的模型，我们得到一个级联的系统结构，导致所有子系统的层次安排。这导致了单向的信息流，因此减少了整体的沟通工作。使用该方法，不仅可以局部估计每个分支的状态，而且可以在已知总电流的情况下计算分支电流。最后给出了一个用真实的测量数据进行的实际测试。
In this paper, a cascaded fractional Kalman filter for state of charge and branch current estimation of large- scale battery systems is proposed. As a centralized approach for the estimation of a large-scale system is costly in terms of effort and time, a partition into smaller and, therefore, simpler subsystems is applied. Since the overall system is divided into smaller units, a local computation is allowed and complexity reduced. In these distributed systems, usually, the subsystems communicate with each other to exchange relevant data. Using a model based on mesh currents, we receive a cascaded system structure which results in a hierarchical arrangement of all subsystems. This concludes in a one-directional information flow and, therefore, reduces the overall communication effort. Using this proposed approach, it is not only possible to estimate the states of each branch locally but also to calculate the branch currents when the total current is known. Finally, a practical test with real measurement data is presented.