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The battery-pack system of electric vehicles is prone to collide with low obstacles on the road, causing battery short circuits and even explosions. It poses a great safety threat to passengers and drivers. The honeycomb structure's high energy absorption and lightweight properties have made it a popular choice in the automotive industry. This paper designs different bio-inspired honeycomb structures to a battery-pack system of electric vehicles to improve the crashworthiness performance. The effects of different bio-inspired honeycomb structures on the crashworthiness of a battery-pack system during frontal impact are analyzed based on a nonlinear finite element model. First, the geometric parameters of seven different bio-inspired honeycomb individual units are described. The overall structure of the honeycomb is applied to a battery-pack system. Second, the nonlinear finite element model of a battery-pack system and honeycomb structures are established and verified. Then, collision simulations are conducted. The deformation and the maximum stress of a battery-pack's bottom shell are computed. The energy absorbed by the honeycomb structures during frontal impact are investigated. The results indicate that the proposed bio-inspired honeycomb structure mimicking grass stems improves the safety performance of battery-pack systems most. Finally, a parametric design is carried out on the bio-inspired honeycomb structure. The effects of wall thicknesses and the number of replacement hexagons on the crashworthiness performance are analyzed. The honeycomb structure preforms best when thickness is 1 mm and the number of replacement hexagons is 2 and 4. The optimized bio-inspired honeycomb structure reduces the deformation of the battery-pack' bottom shell by up to 30%, and maximum stress by 10%. • The deformation, maximum stress, and energy absorption are investigated. • The effects of seven bio-inspired honeycomb structures are analyzed. • Parametric design is carried out to further improve the crashworthiness performance. • The deformation of the bottom shell can be reduced by up to 30%. [ABSTRACT FROM AUTHOR]

• Combining ML and FE methods to accelerate the design process of honeycomb structures. • Using 2D unit cell surrogate 3D honeycomb structures. • CDAE with a pixelation strategy is proposed to manage FE data. • LSTM uses latent features and external loads to predict structural dynamic responses. In this study, a novel framework was presented for accelerating the prediction of the mechanical response of honeycomb structures under dynamic crushing, using 2D cells to surrogate 3D honeycomb structures by machine learning (ML). The sizes of different honeycomb structures were designed and the necessary training data obtained through finite element (FE) simulations, but without using any explicit design parameters of the honeycomb cells in the ML model. A pixelization method of FE model was proposed to separate the complete cell structure from the honeycomb FE grid data and convert it into a matching pixel map. FE data was structuralized while reducing the large amount of computational power consumed in identifying complex array structures. Unsupervised automatic extraction of low-dimensional features of pixel maps was performed using a convolutional denoising autoencoder (CDAE). The crushing velocity and the extracted latent features were used as the input of the long-short term memory (LSTM) network to predict the crushing deformation and stress-strain curve of the honeycomb structure under different dynamic loading. Results showed that the constructed ML model could describe the dynamic crushing response behavior. Compared with the traditional FE method, the prediction model was 4.45 × 103 and 1.05 × 103 times faster in predicting the stress-strain and structural deformation response, respectively. The mechanical response prediction model provided a method for rapidly evaluating the dynamic mechanical response behavior of similar periodic array structures using FE model data, which could be beneficial for the design and development of equipment based on bionic structures. [ABSTRACT FROM AUTHOR]