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Topology and modular size optimization of small electric vehicle frame based on cross-section contribution analysis
- Source :
- Structural and Multidisciplinary Optimization. 64:4287-4304
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- In this paper, a cross-section contribution analysis (CSCA) method is proposed to optimize the size of a truss frame. A finite element model of the initial truss frame was established to analyze the static-dynamic stiffness and the full load strength. The reliability of the model was then verified by experiments. An improved wireframe model was obtained from the secondary design of the topological conceptual model, and the contribution rate (CR) of each tube to the performance was analyzed. A “modular line” was defined to modularize the frame tubes based on the cross-section CR (CSCR). The performance and light weight of the modular frames (MFs) with different tube layout schemes were studied. The results of modular size optimization show that the optimized truss frame for small electric vehicles (EVs) reduces the weight and improves the performance significantly. In addition, this method can have better results in continuous variable optimization (with non-standard tube size) as a reference for industry. In discrete variable optimization (with standard tube size for manufacturing feasibility), the method could greatly shorten the development cycle and lead to production rapidly.
- Subjects :
- Control and Optimization
business.product_category
business.industry
Computer science
Frame (networking)
Truss
Topology (electrical circuits)
Modular design
Topology
Computer Graphics and Computer-Aided Design
Finite element method
Computer Science Applications
Cross section (physics)
Control and Systems Engineering
Electric vehicle
business
Engineering design process
Software
Subjects
Details
- ISSN :
- 16151488 and 1615147X
- Volume :
- 64
- Database :
- OpenAIRE
- Journal :
- Structural and Multidisciplinary Optimization
- Accession number :
- edsair.doi...........761c984ba0c85dfdbe48d2c0176b4498