This article presents a new reliability-based design optimization procedure for the vertical vibration issues raised by a modified electric vehicle using fourth-moment polynomial standard transformation method. First, the fourth-moment polynomial standard transformation method with polynomial chaos expansion is used to obtain the reliability index of uncertain constraints in the reliability-based design optimization which is highly precise and saves computing time compared with other common methods. Next, the half-car model with nonlinear suspension parameters for the modified electric vehicle is investigated, and the response surface methodology is adopted to approximate the complex and time-consuming vertical vibration calculation to the polynomial expressions, and the approximation is validated for reliability-based design optimization results within permissible error level. Then, reliability-based design optimization results under both deterministic and uncertain load parameters are shown and analyzed. Unlike the traditional vertical vibration optimization that only considers one or several sets of load parameters, which lacks versatility, this article presents the reliability-based design optimization with uncertain load parameters which is more suitable for engineering. The results show that the proposed reliability-based design optimization procedure is an effective and efficient way to solve vertical vibration optimization problems for the modified electric vehicle, and the optimization statistics, including the maximum probability interval, can provide references for other suspension dynamical optimization.

中文翻译：

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基于四阶矩多项式标准变换方法的改进型电动汽车垂直振动可靠性设计优化

本文提出了一种新的基于可靠性的设计优化程序，用于解决使用四阶矩多项式标准变换方法的改进型电动汽车引起的垂直振动问题。首先，基于多项式混沌展开的四阶矩多项式标准变换方法用于获得基于可靠性的设计优化中不确定约束的可靠性指标，与其他常用方法相比精度高，节省计算时间。接下来，研究了改进型电动汽车具有非线性悬架参数的半车模型，采用响应面方法将复杂耗时的垂直振动计算近似为多项式，并且该近似值在允许的误差水平内针对基​​于可靠性的设计优化结果进行了验证。然后，展示并分析了确定性和不确定性载荷参数下基于可靠性的设计优化结果。与传统的垂直振动优化只考虑一组或几组载荷参数缺乏通用性不同，本文提出了更适合工程的载荷参数不确定的基于可靠性的设计优化。结果表明，所提出的基于可靠性的设计优化程序是解决改装电动汽车垂直振动优化问题的有效方法，优化统计数据包括最大概率区间、