A Graph-Based Lithium-Ion Battery Parameter Estimation Approach to Produce Diverse Synthetic Data,Advanced Theory and Simulations

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A Graph-Based Lithium-Ion Battery Parameter Estimation Approach to Produce Diverse Synthetic Data
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-06-21 , DOI: 10.1002/adts.202200128
Janamejaya Channegowda ₁ , Yugendra Gowda Lachappa ₂ , Shefali Jagwani ₂

Affiliation

Energy dense lithium-ion batteries are extensively used in all portable electronic devices and in electric vehicles as well. State-of-charge estimation of these batteries has been of considerable commercial interest as this key metric can be construed as the available range in electric vehicles. State-of-charge is also important to ascertain the remaining usage time in battery powered devices. In this paper a graph neural network-based approach is employed to estimate key battery parameters such as, voltage, battery capacity, etc. To the best of the authors' knowledge, this is the first paper to employ a graph-based approach to improve battery estimates. The pairwise interdependencies within the battery dataset are exploited to provide better battery estimates. The graph-based approach is compared with related statistical methods to highlight the effectiveness of this approach.

中文翻译：

一种基于图形的锂离子电池参数估计方法，可生成多种合成数据

能量密集型锂离子电池广泛用于所有便携式电子设备和电动汽车。这些电池的充电状态估计具有相当大的商业利益，因为这个关键指标可以解释为电动汽车的可用范围。充电状态对于确定电池供电设备的剩余使用时间也很重要。在本文中，采用基于图神经网络的方法来估计关键电池参数，例如电压、电池容量等。据作者所知，这是第一篇采用基于图的方法来改进电池估计。利用电池数据集中的成对相互依赖性来提供更好的电池估计。

更新日期：2022-06-21

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