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, computer-aided optimization (CAO) approach. In the traditional approaches, heavy humandependence is required. Even though the emerging CAO approaches automate the optimization process, they still require manual analysis and deduction process. To mitigate humandependence for the sake of high accuracy and easy implementation, an artificial-intelligence-based design (AI-D) approach is proposed in this paper 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 V 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 paper, feasibility and accuracy of the proposed AI-D approach have been validated by hardware experiments.
Output LC filter is one of the most important parts for Buck converters. The existing optimization methods for LC filter fail to provide a fully optimized design. The difficulty in a holistic design approach lies in the trade-off relationships among different design targets. For example, smaller volume results in worse filtering capability and lower efficiency. To improve the overall performance of the output LC filter in Buck converter, a multi-objective design is proposed, taking the power loss, cut-off frequency and volume as design targets. This proposed holistic design approach utilizes Pareto-Frontier to achieve a compact LC filter with optimized efficiency and filtering capability. However, Pareto-Frontier generated by the previous multiobjective algorithms suffers from nonuniform or incomplete coverage, which seriously undermines design accuracy. Thus, the coevolving-AMOSA algorithm is proposed to provide a Pareto-Frontier with uniform and complete coverage. Via this proposed multi-objective design for the output LC filter in Buck converter with the coevolving-AMOSA algorithm, output LC filter can be flexibly designed to meet requirements in various applications while maintaining outstanding comprehensive performance. Optimal design cases for three specific application scenarios are presented as examples. Finally, the experimental results validate the effectiveness of the proposed multi-objective approach.INDEX TERMS Output LC filter, multi-objective design, Pareto-Frontier, coevolving-AMOSA.
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