Artificial-Intelligence-Based Design for Circuit Parameters of Power Converters

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 tw...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 69; H. 11; S. 11144 - 11155
Hauptverfasser: Li, Xinze, Zhang, Xin, Lin, Fanfan, Blaabjerg, Frede
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Analytical models
Artificial intelligence
Artificial neural networks
Automation
Buck converters
Circuit design
Computational modeling
Deduction
Design optimization
Design parameters
Electric vehicles
evolutionary algorithm (EA)
Genetic algorithms
neural network (NN)
Neural networks
Optimization
parameter design
power converter
Power converters
Ripples
ISSN:0278-0046, 1557-9948
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Zusammenfassung: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.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3088377