汽车工程
撞车
耐撞性
电池(电)
有限元法
计算机科学
组分(热力学)
模拟
机械工程
工程类
结构工程
物理
功率(物理)
量子力学
热力学
程序设计语言
作者
Feng Zhu,Krishna Prasath Logakannan
出处
期刊:SAE International Journal of Advances and Current Practices in Mobility
日期:2022-03-29
卷期号:4 (5): 1667-1677
被引量:4
摘要
<div class="section abstract"><div class="htmlview paragraph">Lithium-ion battery systems have been used as the main power source for electric vehicles due to their lightweight and high energy density. The impact safety of these battery systems has been a primary issue. In this work, the crashworthiness design of a typical vehicle battery module is implemented through numerical (finite element) simulations integrated with machine learning algorithms (decision trees). The module with multiple layered porous cells is modeled with a simplified, homogeneous material law, and subjects to the impact of a cylindrical indenter. The main protective component on the module - cover plate is designed as an energy absorbing sandwich structure with a core of cellular solids. Large scale simulations are conducted with various design variable values for the sandwich structure, and the results form a design (simulation) dataset. Based on the dataset, machine learning is applied to the sandwich cover plate design to: (1) correlate the design variables to the response; (2) investigate the complex inter-relationship between design variables; and (3) derive decision-making rules to achieve the designs with highest energy absorbing capability.</div></div>
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