STRUCTURAL STRAIGHT-TAPERED TUBE OPTIMIZATION FOR BATTERY PROTECTION IN ELECTRIC VEHICLES SUBJECTED TO GROUND IMPACT USING MACHINE LEARNING Alvian Iqbal Hanif Nasrullah
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Abstract
Advances in current and future mobility have increasing significantly. The impact of increasing the number of mobility devices such as conventional vehicles has influenced the quality of environment negatively. This condition makes many scientists and researchers moved to explore the field of renewable technology so that future vehicles can escape the dependence on fossil fuels. One solution found is electric vehicle (EV). Even though EV is the best solution for environmentally friendly vehicles, in its development there are many challenges, such as requiring a high level of security for batteries as the main energy source for EV. However, batteries contain highly sensitive material and can explode when Lithium-ions are exposed to the air so this energy source must be protected from external interference. Therefore, an effective protective structure was developed, both in terms of weight and its ability to reduce external interference, for example, due to the ground impact. The case of impact loads that occurred was the throwing of gravel due to being stepped on a car tire and finally crashing into the bottom of the car. Therefore, this study aims to optimize the protective structure and determine the best topology using straight taper tube for the battery^s protective structure. The optimization variables are ratio of shell taper crashworthy components. The purpose of topology optimization is to increase specific energy absorption of battery protectors and minimize battery shortening. This optimization uses artificial neural network, genetic algorithms, multi-objective optimization, and TOPSIS methods.
