This study addresses the challenges of excessive weight and the difficulty of simultaneously achieving high mechanical performance and durability in aluminum alloy truck frames for electric trucks. The strongly coupled optimization of frame dimensions, cross-sectional shapes, and connection configurations has not been fully realized. To overcome these limitations, this study develops an automated, integrated multi-objective optimization method based on the software’s secondary development capabilities. For the first time, the approach enables simultaneous optimization of bolt layout parameters (spacing, diameter, and quantity), frame dimensions, and cross-sectional geometries, establishing a strongly coupled mechanism among connection strategies, structural parameters, and performance indicators. A novel variable selection strategy integrating LASSO, XGBoost, and Random Forest algorithms is proposed. The RNSGA-III and NSGA-III multi-objective optimization algorithms, combined with an improved entropy-weighted TOPSIS method, are employed to develop an aluminum alloy truck frame with superior comprehensive performance. Results indicate that under the most critical working condition, maximum deformation is limited to 11.3 mm, the minimum safety factor remains at 2.1, the first-order natural frequency increases to 10.3 Hz, and the predicted fatigue life corresponds to 1.07 × 106 km. After extrusion manufacturing, the optimized frame weight is reduced by 38.9% compared with the conventional steel frame. This study establishes an automatic parametric modeling framework integrated with multi-objective optimization, providing a novel and practical solution for the lightweight and high-performance design of aluminum alloy truck frames in electric trucks.
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