Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions. To address these limitations, we propose an end-to-end automated IPMSM design optimization framework that integrates retrieval-augmented generation (RAG) for structured problem definition with an uncertainty-aware FEA-AI hybrid optimization pipeline. A Design agent, connected to a motor textbook through RAG, provides domain-knowledge-based options and engineering tips, and compiles an optimization card and a design-of-experiments plan for AI-model training. A Training agent automates electromagnetic FEA, records geometry-validation and solver-failure logs, analyzes failed geometries using ANOVA-based data analysis and LLM reasoning, and invokes a Design Sampling agent to redefine the design space and generate additional samples. An Optimization agent performs GA-based search with uncertainty-driven switching: low-uncertainty candidates are evaluated by AI-surrogate inference, whereas high-uncertainty and reliability-critical Pareto-front or top-K candidates are corrected by high-fidelity FEA and reused for iterative retraining. The framework converts manual, experience-dependent configuration into a reproducible workflow that balances computational cost and prediction reliability. Experimental results under a matched high-fidelity FEA budget show that the proposed hybrid approach achieves better objective performance while maintaining low and further reducible predictive uncertainty, outperforming FEA-only search, which is limited by early budget exhaustion, and AI-only search, which converges to a low-confidence optimum.

翻译：内置式永磁同步电机（IPMSM）设计需平衡相互冲突的目标与多物理场约束，而现代优化流程面临三大瓶颈：手动问题设置、高有限元分析（FEA）成本，以及在稀疏或分布外区域中不可靠的代理模型搜索。为解决上述局限，我们提出了一种端到端自动化 IPMSM 设计优化框架，该框架将基于检索增强生成（RAG）的结构化问题定义与不确定性感知的FEA-AI混合优化管线相结合。通过RAG连接电机教科书的设计智能体，可提供基于领域知识的选项与工程提示，并编译用于AI模型训练的优化卡与实验设计方案。训练智能体自动化电磁FEA流程，记录几何验证与求解器故障日志，利用基于方差分析的数据分析与大语言模型推理分析故障几何结构，并调用设计采样智能体重构设计空间并生成额外样本。优化智能体执行基于遗传算法的搜索，采用不确定性驱动的切换机制：低不确定性候选方案通过AI代理推理评估，而高不确定性及可靠性关键的帕累托前沿或Top-K候选方案则通过高保真度FEA进行校正并用于迭代重训练。该框架将依赖经验的人工配置转化为可复现的工作流程，平衡了计算成本与预测可靠性。在匹配的高保真度FEA预算下的实验结果表明，所提出的混合方法在实现更优目标性能的同时保持了较低且可进一步降低的预测不确定性，性能优于受早期预算耗尽限制的纯FEA搜索以及收敛于低置信度最优解的纯AI搜索。