{"title":"Dynamic Modeling and Online Parameter Identification of a Coupled-Inductor-Based DC-DC Converter with Leakage Inductance Effect Consideration","authors":"Amir Farakhor, H. Fang","doi":"10.1109/IECON48115.2021.9589404","DOIUrl":null,"url":null,"abstract":"In this paper, the dynamic modeling of a coupled-inductor-based DC-DC converter is investigated. Due to the time varying characteristics of converter parameters such as the output load and input voltage, the operating point of the converter changes within time. Therefore, traditional small signal modeling approaches are not accurate since the converter is linearized around a specific operating point. This paper seeks to address this problem by employing an online parameter identification technique to dynamically estimate the parameters. The identification is achieved through Kalman filtering. First, the small signal modeling of the converter is derived including the leakage inductance effect. Then, the Kalman filter is improved and applied to identify the control-to-output transfer function parameters. Extensive simulation results are provided to validate the robust and proper operation of presented modeling procedure.","PeriodicalId":443337,"journal":{"name":"IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON48115.2021.9589404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the dynamic modeling of a coupled-inductor-based DC-DC converter is investigated. Due to the time varying characteristics of converter parameters such as the output load and input voltage, the operating point of the converter changes within time. Therefore, traditional small signal modeling approaches are not accurate since the converter is linearized around a specific operating point. This paper seeks to address this problem by employing an online parameter identification technique to dynamically estimate the parameters. The identification is achieved through Kalman filtering. First, the small signal modeling of the converter is derived including the leakage inductance effect. Then, the Kalman filter is improved and applied to identify the control-to-output transfer function parameters. Extensive simulation results are provided to validate the robust and proper operation of presented modeling procedure.