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Crash analysis of lithium-ion batteries using finite element based neural search analytical models

机译:基于有限元神经搜索分析模型的锂离子电池碰撞分析

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摘要

The electric operated road vehicles are frequently powered by lithium ion batteries due to its low cost and ease of manufacturing. However, unforeseen impacts in road conditions can lead to fire hazard due to short circuiting of the battery pack. The impact strength of the battery pack can hence provide a key design input for manufacturing next generation batteries with a durable safe limit. In this work, a finite element based neural search approach is proposed for determining the effects of various uncertain phenomena on the strength of the battery. The approach combines the actual impact mechanics of battery as determined by the finite element model along with the high accuracy and robustness provided by neural search algorithm. The derived model is able to satisfactorily predict the variances in the mechanical strength even with slightest uncertainties in the phenomena which can affect the strength of the battery pack. It is anticipated that the proposed model will be of utmost importance in design of next generation safe and durable lithium ion battery packs.
机译:电动道路车辆由于其低成本和易于制造而经常由锂离子电池供电。但是,由于电池组的短路,无法预料的道路碰撞会导致火灾危险。因此,电池组的冲击强度可以为制造具有持久安全极限的下一代电池提供关键的设计输入。在这项工作中,提出了一种基于有限元的神经搜索方法来确定各种不确定现象对电池强度的影响。该方法结合了由有限元模型确定的实际电池冲击机理以及神经搜索算法提供的高精度和鲁棒性。即使在影响电池组强度的现象具有最小的不确定性的情况下,派生的模型也能够令人满意地预测机械强度的变化。可以预料,提出的模型在设计下一代安全耐用的锂离子电池组时将至关重要。

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