【摘要】 针对纯电动汽车动力总成悬置系统（powertrain mounting system, PMS）参数同时具有不确定性和相关性的复杂情形，基于整车13自由度（degree of freedom, DOF）模型开展纯电动汽车PMS稳健性优化设计研究.首先，基于蒙特卡洛抽样和相关性变换方法提出一种概率参数相关情形下PMS固有特性响应不确定性和相关性分析的UTI-蒙特卡洛（UTI-Monte Carlo, UMC）法；然后，结合相关性变换方法和任意多项式混沌展开，推导一种高效求解PMS响应不确定性和相关性分析的UTI-任意多项式混沌展开（UTI-arbitrary polynomial chaos expansion, UAPCE）法；接着，基于UAPCE法和相关系数赋权法，提出一种考虑响应不确定性和相关性的PMS稳健性优化设计方法；最后，通过算例验证提出方法的有效性，并对比了基于传统6 DOF模型和整车13 DOF模型的分析和优化结果 .结果表明，基于整车13 DOF模型得到的计算结果能更好地反映整车环境下PMS的振动特性；以UMC方法为参考，UAPCE法在求解PMS固有特性响应的不确定性和相关性方面具有良好的计算精度和效率；提出的优化设计方法能够合理配置系统参数，提高系统稳健性.更多还原

【Abstract】 Aiming to handle the complex situation that the parameters of the powertrain mounting system(PMS) of battery electric vehicles are both uncertain and correlated, the robust design optimization of PMS based on the vehicle model with 13 degree of freedom(DOF) is investigated. Firstly, based on Monte Carlo sampling and the correlation transformation method, the UTI-Monte Carlo(UMC) method for the uncertainty and correlation analysis of PMS inherent characteristic responses was proposed, where the probabilistic parameters were correlated. Then, an efficient method named the UTI-arbitrary polynomial chaos expansion(UAPCE) method was derived for the uncertainty and correlation analysis of PMS responses by combining the correlation transformation method and arbitrary polynomial chaos expansion. Next, based on the UAPCE method and correlation coefficient weighting method, a robust design optimization method of PMS was developed by considering the uncertainty and correlation of responses. Finally, a numerical example was used to verify the effectiveness of the proposed method. The analysis and optimization results based on the PMS model with 6 DOF and those based on the vehicle model with 13 DOF were compared. The results show that the calculated results using the vehicle model with 13 DOF can better reflect the vibration performance of PMS under the vehicle environment. Using the UMC as a reference method, the UAPCE method has good computational accuracy and efficiency in conducting the uncertainty and correlation analysis of PMS responses. The proposed robust design optimization method can configure the PMS parameters reasonably and improve the robustness of the system.更多还原