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.

The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase shift (TPS) can be considered as one of the most advanced modulation techniques for DAB converter. It can widen zero voltage switching range and improve power efficiency significantly. Currently, current stress of the DAB converter has been an important performance indicator when TPS modulation is applied for smaller size and higher efficiency. However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively. Firstly, three degrees of modulation variables in TPS modulation bring challenges to the analysis of current stress in different operating modes. This analysis and deduction process leads to heavy computational burden and also suffers from low accuracy. Secondly, to realize TPS modulation, if a lookup table is adopted after the optimization of modulation variables, modulation performance will be unsatisfactory because of the discrete nature of lookup table. Therefore, an AI-based TPS modulation (AI-TPSM) strategy is proposed in this paper. Neural network (NN) and fuzzy inference system (FIS) are utilized to deal with the two difficulties mentioned above. With the proposed AI-TPSM, the optimization of TPS modulation for minimized current stress will enjoy high degree of automation which can relieve engineers’ working burden and improve accuracy. In the end of this paper, the effectiveness of the proposed AI-TPSM has been experimentally verified with a 1 kW prototype.

Currently, triple phase-shift (TPS) modulation has attracted more and more attention of researchers as an advanced modulation strategy for dual active bridge converter (DAB). Since it has three degrees of freedom, it can realize better performance both in soft switching ranges and power efficiency. However, how to choose these three degrees of freedom to realize optimal power efficiency of DAB converter becomes a concern for researchers. Generally, there exist two difficulties to apply efficiency-oriented TPS modulation. The first difficulty lies in the analysis process in which the main task is to figure out the relationships between modulation parameters and power loss. The three modulation parameters in TPS bring difficulties in analysis and deduction process, which suffers from high computational burden and low accuracy. Additionally, the second difficulty lies in the real-time realization of TPS modulation. If a look-up table is applied to store the optimized modulation parameters, it is highly likely that its discrete nature will result in unsatisfactory modulation performance. Therefore, this article proposes an efficiency-oriented automatic TPS (ATPS) modulation approach, which utilizes neural network, particle swarm optimization, and fuzzy inference system, respectively, in its three stages. The proposed ATPS is able to mitigate labor in computational burden with a highly automatic fashion. Finally, this proposed ATPS has been validated with 1-kW hardware experiments.

With the popularity of dc microgrid, the symmetric CLLC resonant dual active bridge converter usually works as a dc transformer (DCT) when it is applied in dc microgrid. It has received increasing attention as it provides galvanic isolation and also enjoys high power density. In such application, the open-loop control with 50% duty ratio is commonly adopted for the CLLC resonant DCT (CLLC-DCT) for simple control scheme and high efficiency. However, this will bring about difficulties for the design of CLLC-DCT. First, the fluctuating values of the practical inductors or capacitors because of temperature or power variation may lead to deviated voltage conversion ratio (VCR) of CLLC-DCT. Second, chances are that system instability will happen when CLLC-DCT and a constant power load connect in a cascaded structure because of impedance interactions, which will also be affected when parameters fluctuate. Third, high efficiency of the DCT is also expected even when the inductance or capacitance fluctuate. With all the above concerns, the five-stage design methodology for the CLLC-DCT converter is put forward to meet design objectives regarding VCR, system stability, and power efficiency simultaneously. And the effects of fluctuating inductance and capacitance have been fully taken into account by the assistance of particle swarm optimization. Besides, a 1-kW hardware prototype has experimentally validated that the proposed design methodology is feasible.

Dual active bridge (DAB) converter is the key enabler in many popular applications such as wireless charging, electric vehicle and renewable energy. ZVS range and efficiency are two significant performance indicators for DAB converter. To obtain the desired ZVS and efficiency performance, modulation should be carefully designed. Hybrid modulation considers several single modulation strategies to achieve good comprehensive performance. Conventionally, to design a hybrid modulation, harmonic approach or piecewise approach is used, but they suffer from timeconsuming model building process and inaccuracy. Therefore, an artificial-intelligence-based hybrid extended phase shift (HEPS) modulation is proposed. Generally, the HEPS modulation is developed in an automated fashion, which alleviates cumbersome model building process while keeping high model accuracy. In HEPS modulation, two EPS strategies are considered to realize optimal efficiency with full ZVS operation over entire operating ranges. Specifically, to build data-driven models of ZVS and efficiency performance, extreme gradient boosting (XGBoost), which is a state-of-the-art ensemble learning algorithm, is adopted. Afterwards, particle swarm optimization with state-based adaptive velocity limit (PSO-SAVL) is utilized to select the best EPS strategy and optimize modulation parameters. With 1 kW hardware experiments, the feasibility of HEPS has been verified, achieving optimal efficiency with maximum of 97.1% and full-range ZVS operation.