Asef Pedram, Vagg Christopher
Advanced Propulsion Laboratory (APL), Department of Mechanical Engineering, Faculty of Engineering Sciences, University College London, London, E20 3BS, UK.
Department of Mechanical Engineering, Institute for Advanced Automotive Propulsion Systems (IAAPS), Faculty of Engineering and Design, University of Bath, Bath, BA2 7AY, UK.
Sci Rep. 2024 Feb 24;14(1):4526. doi: 10.1038/s41598-024-54965-2.
Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.
先进的槽和绕组设计对于制造未来的高性能电机至关重要。因此，设计和提高槽满率（SFF）方法的开发引起了大量研究。制造工艺的最新进展，如增材制造和替代材料，也凸显了对新型高保真设计技术的需求，以开发高性能的复杂几何形状和拓扑结构。因此，本研究引入了一种新颖的基于物理的机器学习（PIML）设计优化过程，以提高电动汽车中使用的牵引电机的SFF。使用最大熵采样算法（MESA）为基于物理的贝叶斯优化（PIBO）算法提供初始点，其中目标函数及其近似值由高斯过程（GP）生成。所提出的PIBO-MESA与二维有限元模型（FEM）相结合，以执行基于GP的代理模型，并首次展示了电机复杂设计变量的最佳组合。使用新的PIBO-MESA方法实现了显著的计算增益，比现有随机方法（如非支配排序遗传算法II（NSGA-II））快45%。有限元分析结果证实，新的设计优化过程和梯形导线在电阻率相似的情况下，可实现更高的SFF（即提高20%）和电磁性能改善（如最大转矩提高12%）。因此，新开发的PIBO-MESA设计优化过程在高性能电机设计中具有显著优势，可减少开发时间和成本。
