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.

翻译：参数设计对于确保电力变换器整体性能的满意性至关重要。通常，电力变换器的电路参数设计包括两个过程：分析与推导过程以及优化过程。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方法。传统方法严重依赖人工干预。尽管新兴的计算机辅助优化方法实现了优化过程的自动化，但仍需要手动完成分析与推导过程。为减少人工依赖以实现高精度和易实施性，本文提出了一种基于人工智能的设计方法用于电力变换器的参数设计。在所提出的AI-D方法中，为实现分析与推导过程的自动化，采用仿真工具和批归一化神经网络构建优化目标和设计约束的数据驱动模型。此外，为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48V至12V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验证。本文给出了一个以体积、电压纹波和电流纹波为约束的效率最优同步降压变换器设计案例。最后，通过硬件实验验证了所提出的AI-D方法的可行性与准确性。