TY - JOUR
T1 - Reliability-based design optimization of composite battery box based on modified particle swarm optimization algorithm
AU - Liu, Zhao
AU - Zhu, Chao
AU - Zhu, Ping
AU - Chen, Wei
N1 - Funding Information:
This work was supported by National Natural Science Foundation of China (Grant No. 11772191 , No. 51705312 ) and China Postdoctoral Science Foundation (Grant No. 2017M61156 ). The authors also acknowledge the support from the Adjunct Professor position provided by the Shanghai Jiao Tong University to Prof. Wei Chen.
PY - 2018/11/15
Y1 - 2018/11/15
N2 - The application of carbon fiber reinforced polymer (CFRP) material introduces great challenges to the optimization design process, such as complex non-linear material behavior, the inherent uncertainty of design variables and multilevel characteristics of the structure. This paper aims at developing a reliability-based design optimization (RBDO) method to solve the CFRP battery box lightweight design problem considering both meso- and macro-scopic parameters. The method has three kernel parts: the uncertainty quantification and propagation part, the finite element analysis part and the optimization part. In the first part, the internal geometry variability of plain woven CFRP was obtained by X-ray micro-CT images. Representative Volume Element (RVE) models are established to predict the elastic and strength properties of the studied composites, and the constitutive model of material was adapted in stiffness and strength analysis of the battery box structure in the second part. Then a RBDO procedure considering design variables across two scales is developed using a modified particle swarm optimization and surrogate modeling techniques. The structure of the CFRP battery box achieved by the proposed multiscale optimization procedure realizes a weight loss of 22.14%, and the performance demands are satisfied with high reliability, which further reveals the advantages of using this methodology.
AB - The application of carbon fiber reinforced polymer (CFRP) material introduces great challenges to the optimization design process, such as complex non-linear material behavior, the inherent uncertainty of design variables and multilevel characteristics of the structure. This paper aims at developing a reliability-based design optimization (RBDO) method to solve the CFRP battery box lightweight design problem considering both meso- and macro-scopic parameters. The method has three kernel parts: the uncertainty quantification and propagation part, the finite element analysis part and the optimization part. In the first part, the internal geometry variability of plain woven CFRP was obtained by X-ray micro-CT images. Representative Volume Element (RVE) models are established to predict the elastic and strength properties of the studied composites, and the constitutive model of material was adapted in stiffness and strength analysis of the battery box structure in the second part. Then a RBDO procedure considering design variables across two scales is developed using a modified particle swarm optimization and surrogate modeling techniques. The structure of the CFRP battery box achieved by the proposed multiscale optimization procedure realizes a weight loss of 22.14%, and the performance demands are satisfied with high reliability, which further reveals the advantages of using this methodology.
KW - Composite battery box
KW - Finite element simulation
KW - Kriging surrogate model
KW - Multiscale reliability-based design optimization
KW - Particle swarm optimization
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U2 - 10.1016/j.compstruct.2018.07.053
DO - 10.1016/j.compstruct.2018.07.053
M3 - Article
AN - SCOPUS:85050717291
SN - 0263-8223
VL - 204
SP - 239
EP - 255
JO - Composite Structures
JF - Composite Structures
ER -