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In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. A LIB is composed of several unit cells. Therefore, one of the most important factors that determine the performance of a LIB are the characteristics of the unit cell. The design of LIB cells is a challenging problem since it involves the evaluation of expensive black-box functions. These functions lack a closed-form expression and require long-running time simulations or expensive physical experiments for their evaluation. Recently, Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition function that guides the optimization.

This investigation introduces a methodology to design dynamically crushed thin-walled tubular structures for crashworthiness applications. Due to their low cost, high-energy absorption efficiency, and capacity to withstand long strokes, thin-walled tubular structures are extensively used in the automotive industry. Tubular structures subjected to impact loading may undergo three modes of deformation: progressive crushing/buckling, dynamic plastic buckling, and global bending or Euler-type buckling. Of these, progressive buckling is the most desirable mode of collapse because it leads to a desirable deformation characteristic, low peak reaction force, and higher energy absorption efficiency. Progressive buckling is generally observed under pure axial loading; however, during an actual crash event, tubular structures are often subjected to oblique impact loads in which Euler-type buckling is the dominating mode of deformation.

Additive manufacturing has been a promising technique for producing sophisticated porous structures. The pore's architecture and infill density percentage can be easily controlled through additive manufacturing methods. This paper reports on development of sandwich-shape extruded cross sections with various architecture. These lightweight structures were prepared by employing additive manufacturing technology. In this study, three types of cross-sections with the same 2-D porosity were generated using particular techniques. a) The regular cross section of hexagonal honeycomb, b) the heterogeneous pore distribution of closed cell aluminum foam cross section obtained from image processing and c) linearly patterned topology optimized 2-D unit cell under compressive loading condition. The optimized unit cell morphology is obtained by using popular two-dimensional topology optimization code known as 99-line code, and by having the same volume fraction as the heterogeneous foam.