Crash analysis of lithium-ion batteries using finite element based neural search analytical models
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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.
KeywordsNeural networks Model design Finite element Uncertainties Battery packs Mechanical strength
This study was also supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002).
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