In this article, a two-level methodology is proposed to optimize the design of a hybrid excitation synchronous machine (HESM) for a given electric vehicle (EV) over an arbitrary-selected driving cycle. We are looking at a huge analysis problem of finding an optimal hybridization ratio (HR) between the two excitation sources, namely, permanent magnet (PM) and wound excitation (WE). To find the optimal HR, the HR is scanned from 0 to 1 or from pure WE to pure PM excitation. For each HR, the motor is optimally designed at the component level, its cost is minimized, and its global efficiency over the selected driving cycle is calculated. Then, at the system level, the global efficiencies associated with each HR are compared in order to find the optimal HR. The complexity of the design optimization at the component level is addressed by nondominated sorting genetic algorithm II (NSGA-II). To make a compromise between the accuracy and speed of calculations, a nonlinear 3-D dynamic magnetic equivalent circuit (MEC) model is developed and evaluated by commercial finite element analysis (FEA) software. Following the proposed methodology and due to 300 h of computations with 48 CPU cores in parallel, the final HESM design can achieve up to 18.65% higher global efficiency than pure WE and 15.8% higher than pure PM excitation.

中文翻译：

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三轮电动汽车混合励磁同步电机的系统级优化

在本文中，提出了一种两级方法来优化给定电动汽车 (EV) 的混合励磁同步电机 (HESM) 在任意选择的驾驶循环中的设计。我们正在研究寻找两个励磁源（即永磁体 (PM) 和绕线励磁 (WE)）之间的最佳杂交比 (HR) 的巨大分析问题。为了找到最佳 HR，从 0 到 1 或从纯 WE 到纯 PM 激发扫描 HR。对于每个 HR，电机在组件级别进行了优化设计，其成本最小化，并计算了其在选定驾驶周期内的整体效率。然后，在系统级别，比较与每个 HR 相关的全局效率，以找到最佳 HR。非支配排序遗传算法 II (NSGA-II) 解决了组件级设计优化的复杂性。为了在计算精度和速度之间做出折衷，开发了非线性 3-D 动态磁等效电路 (MEC) 模型并通过商业有限元分析 (FEA) 软件进行评估。按照所提出的方法，由于 48 个 CPU 内核并行计算 300 小时，最终的 HESM 设计可以实现比纯 WE 高 18.65% 的全局效率和比纯 PM 激励高 15.8% 的全局效率。