  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.
