Frontiers in Energy Research (Feb 2023)
Design and optimization of lithium-ion battery protector with auxetic honeycomb for in-plane impact using machine learning method
Abstract
The lithium-ion battery is becoming a very important energy source for vehicles designated as electric vehicles. This relatively new energy source is much more efficient and cleaner than conventional fossil fuel. However, lithium-ion batteries have a high risk of fire during a crash, where the large deformation on the battery during the crash may cause thermal runaway. This research explores that idea by studying the design and optimization of sandwich-based auxetic honeycomb structures to protect the pouch battery cells for the battery pack system of electric vehicles undergoing axial impact load using machine learning methods. The optimization was done using Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm Type II (NSGA-II) combined with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Artificial Neural Network predicted the sandwich structure’s specific energy absorption (SEA) and the maximum battery stress during deformation. NSGA-II combined with TOPSIS optimized the design using both of the predictors. Both creations of the training data and validation were done using the non-linear finite element method. The optimized design has a geometric shape of Double-U, a length of 6 mm, a width of 4.2 mm, cross section’s thickness of 0.6 mm, and consists of 1 layer. The optimum design has a specific energy absorption of 47,997.84 J and can maintain the battery’s von Mises stress to a maximum of 43.16 MPa, well below the designated battery’s von Mises stress limit of 67.97 MPa.
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