An electric vehicle (EV) powertrain is comprised of a motor and reduction gear. Thus, it must be designed by considering both components to improve its dynamic and economic performances. To obtain the optimal design of powertrain components for an EV, this study employs a two-stage analysis model focusing on the motor and vehicle at each stage for accuracy and efficiency. In the first stage, a motor system model analyzes the motor characteristics, such as the maximum and minimum torque and motor losses. Using the motor design parameters, these characteristics are converted to torque curves and an efficiency map. In the second stage, a vehicle system model analyzes the target performance using converted motor data for efficient analysis of the performance. An optimization problem is formulated to minimize the maximum motor power, acceleration time, and energy consumption with dynamic constraints, including the maximum vehicle speed and ascendable gradient. To reduce the excessive computational effort when conducting the multi-objective optimization, surrogate models with respect to performance are effectively constructed by using the adaptive sampling method. From the optimization results, a Pareto front having various solutions among the objective functions is obtained.

电动汽车（EV）动力总成由电动机和减速齿轮组成。因此，必须在设计时考虑这两个方面，以改善其动态和经济性能。为了获得用于电动汽车的动力总成组件的最佳设计，本研究采用了两阶段分析模型，着重于每一阶段的电动机和车辆的准确性和效率。在第一阶段，电动机系统模型分析电动机特性，例如最大和最小转矩以及电动机损耗。使用电动机设计参数，这些特性将转换为转矩曲线和效率图。在第二阶段，车辆系统模型使用转换后的电机数据分析目标性能，以有效地分析性能。制定了一个优化问题，以最大程度地减少最大电动机功率，加速时间，以及具有动态约束的能耗，包括最大车速和上升坡度。为了减少进行多目标优化时的过多计算量，通过使用自适应采样方法可以有效地构建关于性能的代理模型。从优化结果中，获得在目标函数中具有各种解的帕累托前沿。