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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., Comment: 12 pages, 20 figures

Modular multilevel converters are wellknown in the energy sector and have significant potential in others, such as electromobility. Generally, their stable operation is at the expense of numerous sensors, communication burdens, and complicated balancing strategies that challenge their expansion to cost driven applications. Hence, the introduction of a sensorless voltage balancing strategy with a simple controller is an attractive objective. Diode clamped MMCs offer a simple yet effective solution by providing a unidirectional balancing path between two modules through a diode, and ideally, the modulation technique should compensate for the lack of bidirectional energy transfer. Hence open loop operation is possible. Although the open loop operation is desirable to reduce costs, good knowledge of the modules voltages for system monitoring and protection functions still appears mandatory. This paper develops a compensated state space model for diode clamped MMCs and integrates it with an optimal estimator to reliably estimate the voltage of all modules without any direct measurement at module terminals. Additionally, the model considers the effect of the diode clamped branches and their balancing effect, resulting in a 30 percent to 50 percent reduction in estimation error compared to the conventional model.