Multi-condition combined topology optimization design of electric truck aluminum alloy frame based on subjective and objective interactive weights

Abstract

Traditional electric truck frames often utilize channel or round tube beams with uniform thickness, which restricts the in-depth exploration of the frame’s overall mechanical performance and lightweight potential. To address this challenge, real-world multi-scenario driving conditions were considered, and a refined Multi-condition Combined Topology Optimization (MCCTO) method was developed by integrating the Analytic Hierarchy Process (AHP) method with the Criteria Importance Through Intercriteria Correlation (CRITIC) method. This approach incorporates both subjective and objective weights. First, an MCCTO method was developed to identify the optimal arrangement and specific cross-sectional shapes of the aluminum alloy frame’s cross beams. Subsequently, the preliminary aluminum alloy frame was designed based on the principles of equal stiffness and equal strength. Finally, the optimal mechanical performance of the frame was achieved through size optimization. The results show that the designed aluminum alloy frame not only surpasses the benchmark frame in mechanical performance but also achieves a 36% improvement in lightweighting. This study addresses the limitations of traditional single-condition topology optimization methods. The proposed approach not only optimizes the layout of cross beams but also refines the sectional shapes of both longitudinal and cross beams, ensuring optimal mechanical performance of the truck frame under real-world driving conditions. As a result, this method significantly improves material efficiency and greatly enhances the lightweight potential of the frame.