基于人工智能的功率变换器电路参数设计
Artificial-Intelligence-Based Design for Circuit Parameters of Power Converters
AI总结：
本文提出基于人工智能的功率变换器参数设计方法，利用批归一化神经网络和遗传算法自动完成分析推导与优化，并在电动汽车同步降压变换器设计中验证了其可行性与准确性。
摘要：
参数设计对于确保功率变换器令人满意的整体性能具有重要意义。通常，功率变换器的电路参数设计包括两个过程：分析推导过程和优化过程。现有的参数设计方法包括两种类型：传统方法和计算机辅助优化（CAO）方法。在传统方法中，需要高度依赖人工。尽管新兴的CAO方法实现了优化过程的自动化，但它们仍然需要人工进行分析推导过程。为了减少人工依赖，以实现高精度和易于实施，本文提出了一种基于人工智能的设计（AI-D）方法，用于功率变换器的参数设计。在所提出的AI-D方法中，为了实现分析推导过程的自动化，采用仿真工具和批归一化神经网络（BN-NN）为优化目标和设计约束建立数据驱动模型。此外，为了实现优化过程的自动化，使用遗传算法搜索最优设计结果。所提出的AI-D方法在电动汽车48至12 V附件负载供电系统中同步降压变换器的电路参数设计中得到了验证。提供了一个在体积、电压纹波和电流纹波约束下效率最优的同步降压变换器设计案例。在本文末尾，通过硬件实验验证了所提出的AI-D方法的可行性和准确性。
英文摘要：
Parameter design is significant in ensuring a satisfactory holistic performance of power converters. Generally, circuit parameter design for power converters consists of two processes: analysis and deduction process and optimization process. The existing approaches for parameter design consist of two types: traditional approach and computer-aided optimization (CAO) approach. In the traditional approaches, heavy human-dependence is required. Even though the emerging CAO approaches automate the optimization process, they still require manual analysis and deduction process. To mitigate human-dependence for the sake of high accuracy and easy implementation, an artificial-intelligence-based design (AI-D) approach is proposed in this article for the parameter design of power converters. In the proposed AI-D approach, to achieve automation in the analysis and deduction process, simulation tools and batch-normalization neural network (BN-NN) are adopted to build data-driven models for the optimization objectives and design constraints. Besides, to achieve automation in the optimization process, genetic algorithm is used to search for optimal design results. The proposed AI-D approach is validated in the circuit parameter design of the synchronous buck converter in the 48 to 12 V accessory-load power supply system in electric vehicle. The design case of an efficiency-optimal synchronous buck converter with constraints in volume, voltage ripple, and current ripple is provided. In the end of this article, feasibility and accuracy of the proposed AI-D approach have been validated by hardware experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
