Reliability-based design optimization of composite battery box based on modified particle swarm optimization algorithm

Zhao Liu, Chao Zhu, Ping Zhu*, Wei Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)239-255
Number of pages17
JournalComposite Structures
StatePublished - Nov 15 2018


  • Composite battery box
  • Finite element simulation
  • Kriging surrogate model
  • Multiscale reliability-based design optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Ceramics and Composites
  • Civil and Structural Engineering


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