任意概率不确定情形下的电动汽车悬置系统固有特性分析

针对电动汽车动力总成悬置系统 （Powertrain Mounting System,PMS） 参数可能被处理为不同类型概率变量的情形,提出了一种基于任意多项式混沌 （Arbitrary Polynomial Chaos,APC） 展开和最大熵原理 （Maximum Entropy Principle,MEP） 的电动汽车 PMS固有特性不确定性分析方法。采用概率模型描述任意概率不确定情形下的 PMS参数,通过APC展开获得任意概率不确定情形下PMS固有特性不确定性响应的前几阶统计矩,通过MEP拟合不确定性响应的概率密度函数 （Probability Density Function,PDF） 和累积分布函数 （Cumulative Distribution Function,CDF） 等信息,通过算例分析了5种概率不确定情形下的电动汽车PMS固有特性响应。分析结果表明,以蒙特卡洛法作为参考,所提出的方法可有效地分析不同概率不确定情形下的PMS固有特性响应,分析具有较高的计算精度和计算效率,能进一步获得响应满足设计要求的可靠度。

Inherent Characteristics Analysis for the Powertrain Mounting System of Electric Vehicles Under Arbitrary Probabilistic Uncertainties

Since the parameters of electric vehicle powertrain mounting system (PMS) may be regarded as different types of probabilistic variables, an uncertainty analysis of the inherent characteristics of electric vehicle PMS was conducted based on the arbitrary polynomial chaos (APC) expansion and the maximum entropy principle (MEP). Firstly, the probabilistic model was used to describe the PMS parameters under arbitrary probabilistic uncertainties. Then, a few low-order statistical moments of inherent characteristic responses under arbitrary probabilistic uncertainties were calculated by utilizing the APC expansion. Next,the MEP was applied to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of uncertainty response. Finally the inherent characteristic responses of an electric vehicle PMS under five probabilistic uncertainty conditions were analyzed by numerical examples. The results show that, compared with the Monte Carlo method, the proposed method can analyze the inherent characteristic response of PMS under different probabilistic uncertainties with high accuracy and efficiency. The proposed method can be further applied to obtain the response with reliability that meets the design requirements.