首页|Study Data from Institut Teknologi Bandung (ITB) Update Understanding of Machine Learning (Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology)
Study Data from Institut Teknologi Bandung (ITB) Update Understanding of Machine Learning (Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Bandung, Indonesia, by NewsRx editors, research stated, “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.” The news journalists obtained a quote from the research from Institut Teknologi Bandung (ITB): “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.”
Institut Teknologi Bandung (ITB)BandungIndonesiaAsiaCyborgsEmerging TechnologiesMachine Learning
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