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To achieve lightweight design for an electric light-duty cargo vehicle and enhance its overall reliability and endurance mileage, a finite element modeling of its frame is conducted. Static performance, modal performance and fatigue life analysis are performed to identify the weak spots in its strength under torsional conditions. Through sensitivity analysis, 19 key plate thicknesses and 2 crossbeam section widths are identified as design variables. An Optimal Latin Hypercube Design (OLHD) is employed to generate 260 training samples and 28 validation samples. A high-precision surrogate optimization model is constructed using a Radial Basis Function (RBF) network model. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized for multi-objective optimization, aiming to minimize the frame mass and the maximum deformation under torsional conditions. The validation results showed that after optimization, the total frame mass decreased from 574 kg to 533 kg, the maximum stress reduced by 19.73%, the safety factor increased to 1.58, the maximum deformation under torsional conditions decreased by 5.61%, the first-order natural frequency increased by 4.21%, the fatigue life under the weakest torsional condition improved by 3.96%, and the comprehensive safe driving mileage increased to 87.300×10⁴ km, meeting and exceeding the design requirements.

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为实现某电动轻型载货汽车轻量化设计、提升整车可靠性与续航里程，对其车架进行有限元建模，并开展静态性能、模态性能及疲劳寿命分析，识别出其在扭转工况下的强度薄弱点。通过灵敏度分析确定了19个关键板件厚度和2个横梁截面宽度作为设计变量，采用最优拉丁超立方设计（OLHD）生成260个训练样本和28个验证样本，结合径向基函数（RBF）网络模型构建了高精度代理优化模型，运用非支配排序遗传算法-Ⅱ（NSGA-Ⅱ）以车架质量和扭转工况最大变形量最小化为目标进行多目标优化。验证结果表明：优化后车架总质量由574 kg降低至533 kg，最大应力降低19.73%，安全系数提升至1.58，扭转工况最大变形量减小5.61%，一阶固有频率提高4.21%，最薄弱的扭转工况疲劳寿命提升3.96%，综合安全行驶里程提升至87.300×10⁴ km，满足并超越设计要求。

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