Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology

Author:

Fatiha Indira Cahyani1,Santosa Sigit Puji1,Widagdo Djarot1,Pratomo Arief Nur2

Affiliation:

1. Mechanics of Solids Lightweight Structures Research Group, Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung (ITB), Jalan Ganesha 10, Bandung 40132, Indonesia

2. Department of Mechanical Engineering, Faculty of Defense Science and Technology, Indonesia Defense University, Indonesian Peace and Security Centre (IPSC), Bogor 16810, Indonesia

Abstract

The market for electric vehicles (EVs) has been growing in popularity, and by 2027, it is predicted that the market valuation will reach $869 billion. To support the growth of EVs in public road safety, advances in battery safety research for EV application should achieve low-cost, lightweight, and high safety protection. In this research, the development of a lightweight, crashworthy battery protection system using an excellent energy absorption capability is carried out. The lightweight structure was developed by using metastructure constructions with an arrangement of repeated lattice cellular structures. Three metastructure configurations (bi-stable, star-shaped, double-U) with their geometrical variables (thickness, inner spacing, cell stack) and material types (stainless steel, aluminium, and carbon steel) were evaluated until the maximum Specific Energy Absorptions (SEA) value was attained. The Finite Element Method (FEM) is utilised to simulate the mechanics of impact and calculate the optimum SEA of the various designs using machine learning methodology. Latin Hypercube Sampling (LHS) was used to derive the design variation by dividing the variables into 100 samples. The machine learning optimisation method utilises the Artificial Neural Networks (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to forecast the design that produces maximum SEA. The optimum control variables are star-shaped cells consisting of one vertical unit cell using aluminium material with a cross-section thickness of 2.9 mm. The optimum design increased the SEA by 5577% compared to the baseline design. The accuracy of the machine learning prediction is also verified using numerical simulation with a 2.83% error. Four different sandwich structure configurations are then constructed using the optimal geometry for prismatic battery protection subjected to ground impact loading conditions. An optimum configuration of 6×4×1 core cells arrangement results in a maximum displacement of 7.33 mm for the prismatic battery in the ground impact simulation, which is still less than the deformation threshold for prismatic battery safety of 10.423 mm. It is shown that the lightweight metastructure is very efficient for prismatic battery protection subjected to ground impact loading conditions.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Electrochemistry,Energy Engineering and Power Technology

Reference37 articles.

1. Fleck, A. (2023, July 14). EV Market Revenue Set To Hit $384 Billion in 2022. Available online: https://www.statista.com/chart/28211/electric-vehicles-revenue-projections/.

2. (2023, July 14). The Global Electric Vehicle Market Overview in 2023: Statistics & Forecasts. Available online: https://www.virta.global/en/global-electric-vehicle-market#one.

3. (2023, July 14). Batteries for Electric Vehicles, Available online: https://afdc.energy.gov/vehicles/electric_batteries.html.

4. (2023, July 14). Preventing Fire and/or Explosion Injury from Small and Wearable Lithium Battery Powered Devices, Available online: https://www.osha.gov/sites/default/files/publications/shib011819.pdf.

5. Characterizing and Modeling Mechanical Properties and Onset of Short Circuit for Three Types of Lithium-Ion Pouch Cells;Sahraei;J. Power Sources,2014

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3