A Data-Driven Sequential Adaptive Optimization Method for Lightweight Design of Complex Vehicle Structures

To address high-dimensional coupling and extrapolation errors in vehicle lightweighting, this paper proposes a “Macroscopic Topology—Microscopic Data-Driven Size Synergy” methodology. Macroscopically, strain-energy-driven topology optimization on a simplified skeleton reduces mass by 9.4% (2835.8 kg to 2566.9 kg). Microscopically, a global ANOVA mechanism compresses 169 thickness variables to 39 core dimensions, mitigating the curse of dimensionality. Crucially, an active learning-based sequential approximate optimization (SAO) framework rectifies severe static model extrapolation errors (up to 475%) by injecting high-entropy boundary samples, boosting the R2 accuracy to near 0.90. Consequently, this approach secures the true Pareto solution, reducing full vehicle mass by 2.59% (to 6229.4 kg) while strictly adhering to EN12663 and EN15227 standards. This paradigm effectively resolves epistemic uncertainties, unlocking extreme lightweighting potential in complex systems.