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Abstract

To reduce vehicle manufacturing costs and improve overall vehicle reliability, a lightweight optimization design was implemented on the frame of an electric commercial vehicle based on HyperWorks and Lsight, and its various performance impacts were analyzed and verified. Based on the static, dynamic, and modal performance analysis of the original frame, 19 key components were identified through sensitivity analysis. An optimization model was established using optimal Latin hypercube test sampling method combined with the radial basis function surrogate model, and the multi-objective non-dominated sorting genetic algorithm-II was used to solve the optimal design scheme. The optimized design achieved the goals of reducing frame weight by 12.24% and increasing first-order natural frequency by 14.27%, effectively enhancing ride comfort and structural safety. The optimized frame meets the fatigue life requirements under various working conditions, demonstrating the effectiveness of this design in ensuring vehicle reliability and safety.

Keywords

commercial vehicle frame lightweight design multi-objective optimization fatigue life analysis

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Research on Lightweight Design and Fatigue Life Optimization of a Commercial Vehicle Frame

Abstract

Keywords

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