A power converter is a device used in electrical engineering, power engineering, and the electric power sector to convert electric energy from one form to another, such as converting between AC and DC, changing voltage or frequency, or a combination of these. It is used in a variety of applications, such as industrial drives, power supply, energy generating equipment, consumer goods, electrical vehicles/aeroplanes/ships, smart grids and more.This book will open a door for engineers to design the power converters via the artificial intelligence (AI) method. It begins by reviewing current AI technology in power converters. The book then introduces customized AI algorithms for power converters that take into account the particular characteristics of power converters. The book then presents a set of AI-based design methodologies for power devices, including DC/DC converters, resonant DC/DC converters, bidirectional DC/DC converters, DC/AC inverters, and AC/DC rectifiers. This is the first book to cover all you need to know about using AI to create power converters, including a literature review, algorithm, and circuit design.
Author(s): Xin Zhang, Xinze Li, Hao Ma, Jingjing Huang, Zeng Zheng
Publisher: Springer
Year: 2023
Language: English
Pages: 220
City: Singapore
Preface
Contents
About the Authors
1 Introduction
1.1 Backgrounds
1.1.1 Basic Introduction to DC-DC Converters
1.1.2 Basic Introduction to DC-AC Inverters
1.2 Problem Descriptions
1.2.1 Problems in the Human-Dependent Analysis of Performance of Power Converters
1.2.2 Problems in the Optimization of Power Converters
1.2.3 Problems in the Modulation of Power Converters
1.3 Basic Introduction to Artificial Intelligence Algorithms
1.3.1 Neural Networks
1.3.2 Evolutionary Algorithms
1.3.3 Fuzzy Inference System
1.4 Applications of Artificial Intelligence Algorithms in DC-DC Converters and DC-AC Inverters
1.4.1 Applications of Neural Networks
1.4.2 Applications of Evolutionary Algorithms
1.4.3 Applications of Fuzzy Inference Systems
1.5 Arrangement of This Book
References
2 The Proposed Automated Optimal Design for Power Switch: A Thermo-mechanical-Coordinated and Multi-objective-Oriented Optimization Methodology
2.1 Introduction
2.2 Present Status and Under-Optimization Problem of DSC Power Module
2.2.1 State-of-the-Art of DSC Power Module
2.2.2 Under-Optimization Problem of DSC Power Module
2.3 Modeling of the Proposed Thermo-mechanical Multi-objective Co-design of DSC Power Module
2.3.1 Thermal Resistance Modeling of the DSC Power Module
2.3.2 Mechanical Stress Modeling of the DSC Power Module
2.3.3 The Proposed Multi-objective Optimization Model
2.4 Solution of Proposed Thermo-mechanical-Coordinated Multi-objective Optimization Design
2.4.1 Case Study of Multi-objective Optimization Design
2.4.2 Influence of Material Properties on Optimization
2.5 Experimental Results
2.6 Conclusions
References
3 The Proposed Multi-objective Design of Output LC Filter for Buck Converter via the Coevolving-AMOSA Algorithm
3.1 Introduction
3.2 Problem Descriptions for the Multi-objective Design of the Output LC Filter in Buck Converter
3.2.1 Preliminaries: Introduction to Pareto-Frontier
3.2.2 Problem I: Trade-Off Relationships Among the Three Design Objectives for the Output LC Filter in Buck Converter
3.2.3 Problem II: The Nonuniform and Incomplete Coverage of Pareto-Frontier
3.3 Analysis of the Three Design Objectives for LC Filter: Power Efficiency, Cut-Off Frequency and Volume
3.3.1 Analysis of Design Objective 1: Optimized Total Power Loss for the Buck Converter of Optimal Power Efficiency
3.3.2 Analysis of Design Objective 2: Optimized Cut-Off Frequency for the Buck Converter with Optimal Filtering Capability
3.3.3 Analysis of Design Objective 3: Optimal Volume for a Compact Buck Converter
3.4 The Proposed Multi-objective Design Approach for the Output LC Filter in Buck Converter with Coevolving AMOSA Algorithm
3.4.1 Stage 1: Analysis of Three Design Objectives
3.4.2 Stage 2: Multi-objective Optimization of the Three Design Objectives with Coevolving-AMOSA Algorithm
3.4.3 Stage 3: Obtain the Optimal Design Solution Based on Application Requirements
3.5 Design Examples of the Proposed Multi-objective Design for the Output LC Filter in Buck Converter with Coevolving-AMOSA Algorithm
3.5.1 Design Example with Traditional Design Method
3.5.2 Design Examples with the Proposed Multi-objective Design of Output LC Filter for Buck Converter with Coevolving-AMOSA Algorithm
3.6 Experimental Verification
3.6.1 Experimental Waveforms of the Traditional Design Case and Three Optimal Design Cases
3.6.2 Evaluation of the Experimental Results
3.7 Conclusion
References
4 The Proposed Artificial-Intelligence-Based Design (AI-D) for Circuit Parameters of Power Converters
4.1 Introduction
4.2 Problem Descriptions for the Parameter Design Approaches for Power Converters and the Proposed Solutions
4.2.1 Problems in the Existing Circuit Parameter Design Approaches for Power Converters
4.2.2 The Proposed Solutions for the Automated Design for the Circuit Parameters of Power Converters
4.3 AI-D Approach for the Parameter Design of Power Converters
4.3.1 Stage 1: Determine Design Specifications
4.3.2 Stage 2: Create Lookup Tables for Inductors and Capacitors
4.3.3 Stage 3: Build Data-Driven Models for Power Losses, Voltage Ripple and Current Ripple
4.3.4 Stage 4: Search for Optimal Design Parameters fs*, L*, C* via Genetic Algorithm
4.4 Design Case of the Proposed AI-D Approach to Design an Efficiency-Optimal Synchronous Buck Converter in EV
4.4.1 Determine Design Specifications
4.4.2 Create Lookup Tables for Inductors and Capacitors
4.4.3 Build Data-Driven Models for Power Losses and Ripples
4.4.4 Search for Optimal fs*, L*, C* via GA
4.4.5 Average CPU Execution Time for Applying the Proposed AI-D Approach in the Design Case
4.5 Design Case of the Proposed AI-D Approach to Design an Efficiency-Optimal Synchronous Buck Converter in EV
4.5.1 Steady-State Waveforms of the Designed Optimally Efficient Synchronous Buck Converter
4.5.2 Experimental Efficiency of the Designed Converter
4.5.3 Experimental Volume and Ripples of the Designed Converter
4.5.4 Comparison Between the Experimental and Theoretical Efficiency, Volume and Ripples of the Designed Converter
4.6 Conclusion
References
5 The Proposed Efficiency-Oriented Two-Stage Optimal Design Methodology for Special Power Converter in Space Travelling-Wave Tube Amplifier Applications
5.1 Introduction
5.2 Preliminary of the Proposed Efficiency-Oriented Two-Stage Optimal Design Method: Review of the LCLC Resonant Converter and the Calculation of the Total Power Loss
5.2.1 Review of the Calculations of the Main Parameters in the LCLC Resonant Converter
5.2.2 Power Loss Analysis of the LCLC Resonant Converter in the Space TWTA Applications
5.3 The Proposed Efficiency-Oriented Two-Stage Optimal Design Method of the LCLC Resonant Converter in the Space TWTA Applications
5.3.1 The Proposed Efficiency-Oriented Two-Stage Optimal Design Methodology
5.3.2 Stage-I: Extraction of the Optimal Parameters Based on the Proposed GA + PSO
5.3.3 Stage-II: Realization of the Optimal Parameters Based on the Proposed Single-Layer Partially-Interleaved Transformer Structure
5.4 Experimental Validations
5.4.1 Verifications of the ZVS and ZCS Characteristics of the Optimal LCLC Resonant Converter
5.4.2 Verifications of Proposed Efficiency-Oriented Two-Stage Optimal Design Method of the LCLC Resonant Converter
5.5 Conclusions
References
6 The Proposed AI Based Two-Stage Optimal Design Methodology for High-Efficiency Bidirectional Power Converters in the Hybrid AC/DC Microgrid Applications
6.1 Introduction
6.2 Working Principles and the Circuit Analysis of the CLLC Resonant Converter in Hybrid AC/DC Microgrid Applications
6.2.1 Working Principles of the CLLC Resonant Converter as a DC Transformer
6.2.2 The Circuit Analysis of the CLLC Bidirectional Converter
6.2.3 Conditions of ZVS and ZCS
6.3 The Proposed AI Based High Efficiency Oriented Two-Stage Optimal Design Method for CLLC Bidirectional Power Converters in the Hybrid AC/DC Microgrid
6.3.1 Preliminary of the Proposed AI Based Two-Stage Optimal Design Method: Total Power Loss Equation of the CLLC Bidirectional Converter
6.3.2 The Proposed AI Based (GA + PSO) Two-Stage Optimal Design Methodology for the CLLC Bidirectional Converter
6.4 Simulation and Experimental Validations of the Proposed AI Based Two-Stage Optimal Design Method
6.4.1 Design of a Planar Transformer with the Desired Parasitic Parameters for a CLLC Bidirectional Converter
6.4.2 Experimental Validation of the Proposed AI Based Two-Stage Optimal Design Methodology
6.5 Conclusions
References
7 The Proposed Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter with Minimized Current Stress
7.1 Introduction
7.2 Operating Principle of TPS Modulation and the Existing Challenges
7.2.1 Operating Principle of TPS Modulation for DAB Converter
7.2.2 Challenge Descriptions for Optimization of TPS Modulation with Minimized Current Stress
7.3 The Proposed AI-Based TPS Modulation
7.3.1 Stage I: NN-Based Analysis of Current Stress
7.3.2 Stage II: Optimization with PSO Algorithm
7.3.3 Stage III: Realization of TPS with FIS
7.4 Design Case of Applying the Proposed AI-TPSM
7.4.1 Stage I: NN-Based Analysis of Current Stress
7.4.2 Stage II: Optimization with PSO Algorithm
7.4.3 Stage III: Realization of TPS with FIS
7.4.4 Computational Resources to Apply the Proposed AI-TPSM Approach in the Design Case
7.5 Experimental Verification
7.5.1 Rated Operating Waveforms
7.5.2 Operating Waveforms Under Different Output Power P and Output Voltage V2
7.5.3 Transient Response Under Power and Voltage Step
7.5.4 Current Stress and Efficiency Performance of the Optimal TPS Modulation via the Proposed AI-TPSM
7.5.5 Comparisons Between the Experimental and Theoretical Results of the Optimal Modulation
7.6 Conclusion
References
8 The Proposed Optimal Design and Heterogeneous Integration of DC/AC Inverter for Electric Vehicle
8.1 Introduction
8.2 State-of-the-Art and Main Barriers of Air-Cooling SiC Inverter
8.2.1 State-of-the-Art of EV Inverter
8.2.2 Challenges of Light and Compact EV Inverter
8.3 The Proposed Design Methodology for Air-Cooling Inverter
8.4 The Proposed Design Methodology for Air-Cooling Inverter
8.4.1 The Proposed Multi-physics-Based Design of SiC Power Module
8.4.2 The Proposed Optimal Selection of DC-Link Capacitance
8.4.3 The Proposed Thermal Design of Heat Sink
8.5 Experimental Results
8.5.1 Prototypes of Power Module and Air-Cooling Inverter
8.5.2 Experimental Results of SiC Power Module
8.5.3 Experimental Results of SiC Inverter
8.6 Conclusions
References
