{"title":"Sliding Mode Control of Coupled Inductor Based Bidirectional Converter Topology for Electric Vehicle Application","authors":"Praveena Krishna P. S., J. S., S. A.","doi":"10.1109/DISCOVER50404.2020.9278073","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278073","url":null,"abstract":"This article presents the design and application of a coupled inductor based bidirectional DC-DC converter (BDC) with sliding mode controller on electric vehicle (EV) powertrain. The coupled inductor acts as an auxiliary circuit providing soft switching to minimize the switching losses. The resonant circuit is achieved with the help of mutual inductance where it further improves efficiency. The sliding mode control is applied for the closed-loop operation which provides better results compared to the standard PI controller. This design is verified with the help of MATLAB/Simulink model and the obtained results are in good settlement with the theoretical calculation. The efficiency of the converter with sliding mode control is found to be 98.3% in buck mode and 97.8% in boost mode. Also, the target of the research to maintain the output voltage at a constant level which is quite clear with the better response of the waveform.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114620089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Feature Selection and Modeling using Statistical and Machine learning Methods","authors":"Sofia D'souza, P. V., Balaji S","doi":"10.1109/DISCOVER50404.2020.9278093","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278093","url":null,"abstract":"Feature selection is a necessary step in machine learning regression problems that aims to find relevant and reduced set of features. In this research, we assessed the performance of three different learning models on a Quantitative structure activity relationship (QSAR) dataset. Learning models were developed from a pool of features selected by three different variable selection techniques. The results indicate that the final learning models built using statistically significant features exhibit improved predictive performance. Further, Partial least squares (PLS) learning model has shown better predictive performance compared to other learning models on the external test set.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126373577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from General Chairs","authors":"","doi":"10.1109/bdeim52318.2020.00005","DOIUrl":"https://doi.org/10.1109/bdeim52318.2020.00005","url":null,"abstract":"Message from General Chairs","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125281232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Committee members","authors":"Ranjith Bhat, D. Rao","doi":"10.1109/icict50521.2020.00006","DOIUrl":"https://doi.org/10.1109/icict50521.2020.00006","url":null,"abstract":"Committee members","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131336820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}