{"title":"Feedforward Repetitive Approach to Disturbance Rejection in DAB Converters for EV Charging","authors":"Piyali Pal;Ranjan Kumar Behera;Utkal Ranjan Muduli","doi":"10.1109/TIE.2024.3519565","DOIUrl":null,"url":null,"abstract":"Single-phase ac/dc converters are popular in charging electric vehicle (EV) batteries due to their bidirectional power flow, electrical isolation, high efficiency, and benefits of power quality. However, these converters can suffer from low-frequency output ripple caused by system nonlinearities and power discrepancies, which reduces output power quality and battery life. This article proposes a modified disturbance rejection control method that uses a feedforward repetitive approach in parallel with a traditional disturbance observer for dual active bridge (DAB) converters in EV charging. A low-pass filter is incorporated into the repetitive controller to reduce high-frequency gain and ensure system stability without introducing phase delays. Theoretical analyses of stability and disturbance rejection capabilities have been discussed elaborately which offer insights for tuning control parameters. The efficacy of the proposed controller is validated through implementation in a laboratory-built DAB converter prototype. Experimental results demonstrate superior dynamic performance and low-frequency ripple reduction compared with traditional methods such as disturbance rejection control and PI control, highlighting the effectiveness of this repetitive control-based approach in improving EV battery charging systems.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 8","pages":"7861-7873"},"PeriodicalIF":7.2000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829382","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10829382/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Single-phase ac/dc converters are popular in charging electric vehicle (EV) batteries due to their bidirectional power flow, electrical isolation, high efficiency, and benefits of power quality. However, these converters can suffer from low-frequency output ripple caused by system nonlinearities and power discrepancies, which reduces output power quality and battery life. This article proposes a modified disturbance rejection control method that uses a feedforward repetitive approach in parallel with a traditional disturbance observer for dual active bridge (DAB) converters in EV charging. A low-pass filter is incorporated into the repetitive controller to reduce high-frequency gain and ensure system stability without introducing phase delays. Theoretical analyses of stability and disturbance rejection capabilities have been discussed elaborately which offer insights for tuning control parameters. The efficacy of the proposed controller is validated through implementation in a laboratory-built DAB converter prototype. Experimental results demonstrate superior dynamic performance and low-frequency ripple reduction compared with traditional methods such as disturbance rejection control and PI control, highlighting the effectiveness of this repetitive control-based approach in improving EV battery charging systems.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.