MODULAR MULTILEVEL CONVERTER WITH PS-PWM TECHNIQUE AND SHORTING ALGORITHM FOR BATTERY MANAGEMENT OF AN ELECTRIC VEHICLE
C. Sekar, Karthick, and Dr.Thangadurai. IJIRIS:: International Journal of Innovative Research in Information Security, Volume VII (Issue IX):
81-86(September 2020)1. Noor, Sanjeevikumar Padmanaban , Lucian Mihet-Popa , Mohammad Nurunnabi Mollah and Eklas Hossain, “Components, Technologies, Challenges, Impacts, and Future Direction of Development”, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh. 2. Mattia Ricco, et. al., “A Capacitor Voltage Balancing Approach Based on Mapping Strategy for MMC Applications” at University of Bologna, 40136 Bologna, 2019. 3. Miguel Moranchel, Francisco Huerta, Inés Sanz, Emilio Bueno and Francisco J. Rodríguez, “A Comparison of Modulation Techniques for Modular Multilevel Converters”, Universidad de Alcalá, Escuela Politécnica, 28805 Alcalá de Henares, Spain, 2016. 4. Xiaojie Shi, Zhiqiang Wang, Leon M. Tolbert and Fred Wang Center for Ultra-wide-area Resilient Electric Energy Transmission Networks (CURENT) “A Comparison of Phase Disposition and Phase Shift PWM Strategies for Modular Multilevel Converters” The University of Tennessee Knoxville, TN 37996-2250, USA-2013 5. Binbin Li, Shaoze Zhou, and Dianguo Xu, Stephen J. Finney, and Barry W. Williams “A Hybrid Modular Multilevel Converter for Medium-Voltage Variable-Speed Motor Drives” National Natural Science Foundation of China (51237002) and (51477034). 6. Mahran Quraan, Pietro Tricoli, Salvatore D’Arco, and Luigi Piegari, Senior,“Efficiency Assessment of Modular Multilevel Converters for Battery Electric Vehicles” University of Birmingham, Birmingham, March 2017. 7. Meiqin Mao , , Yong Ding, Liuchen Chang, Nikos D. Hatziargyriou, Fellow, Qiang Chen, Tinghuan Tao, and Yunwei Li, “Multi-Objective Power Management for EV Fleet With MMC-Based Integration Into Smart Grid” Ieee Transactions On Smart Grid, Vol. 10, No. 2, March 2019 8. Jahangeer Soomro, et. al., Comparative Analysis of Modular Multilevel Converter with Cascaded H Bridge Inverter using Five, Seven and Nine levels” International Journal of Recent Technology and Engineering, Vol. 7, Iss. 5S4, 2019 9. Elena Vergori ID, Francesco Mocera ID and Aurelio Somà ,I”Battery Modelling and Simulation Using a Programmable Testing Equipment”, 2018. 10. Yi Wang, et. al., “Nearest Level PWM Method for the MMC in DC Distribution Grids” North China Electric Power University, Baoding 071003, China. 11. Zhan Ma , Feng Gao , Xin Gu , Nan Li , Decun Niu “An Online SOH Testing Method of MMC Battery Energy Storage System” Jinan, China -2018. 12. Amrutvahini College of Engineering, Sangamner, India2,”Chandrakant Rahane, Vaibhav Varpe “Design and fabrication of regenerative braking system”, 2018. 13. Mattia Ricco, et. al., “FPGA-Based Implementation of MMC Control Based on Sorting Networks” at Cergy-Pontoise University, 95031 Cergy Pontoise, France; 2018. 14. Pratik Bhandari, et. al., Regenerative Braking Systems (RBS)” International Journal of Scientific & Engineering Research, University of Mumbai, India, 2017. 15. Sourabh Rathore, Mukesh Kumar Kirar and S. K Bhardwaj, “ H- Bridge Multilevel Inverter Using Pd, Pod, Apod Techniques”, Department of Electrical Engineering, MANIT, Bhopal, 2015..
DOI: https://doi.org/10.26562/ijiris.2020.v0708.001
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MODULAR MULTILEVEL CONVERTER WITH PS-PWM TECHNIQUE AND SHORTING ALGORITHM FOR BATTERY MANAGEMENT OF AN ELECTRIC VEHICLEC. Sekar, Karthick, and Dr.Thangadurai. IJIRIS:: International Journal of Innovative Research in Information Security, Volume VII (Issue IX):
81-86(September 2020)1. Noor, Sanjeevikumar Padmanaban , Lucian Mihet-Popa , Mohammad Nurunnabi Mollah and Eklas Hossain, “Components, Technologies, Challenges, Impacts, and Future Direction of Development”, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh. 2. Mattia Ricco, et. al., “A Capacitor Voltage Balancing Approach Based on Mapping Strategy for MMC Applications” at University of Bologna, 40136 Bologna, 2019. 3. Miguel Moranchel, Francisco Huerta, Inés Sanz, Emilio Bueno and Francisco J. Rodríguez, “A Comparison of Modulation Techniques for Modular Multilevel Converters”, Universidad de Alcalá, Escuela Politécnica, 28805 Alcalá de Henares, Spain, 2016. 4. Xiaojie Shi, Zhiqiang Wang, Leon M. Tolbert and Fred Wang Center for Ultra-wide-area Resilient Electric Energy Transmission Networks (CURENT) “A Comparison of Phase Disposition and Phase Shift PWM Strategies for Modular Multilevel Converters” The University of Tennessee Knoxville, TN 37996-2250, USA-2013 5. Binbin Li, Shaoze Zhou, and Dianguo Xu, Stephen J. Finney, and Barry W. Williams “A Hybrid Modular Multilevel Converter for Medium-Voltage Variable-Speed Motor Drives” National Natural Science Foundation of China (51237002) and (51477034). 6. Mahran Quraan, Pietro Tricoli, Salvatore D’Arco, and Luigi Piegari, Senior,“Efficiency Assessment of Modular Multilevel Converters for Battery Electric Vehicles” University of Birmingham, Birmingham, March 2017. 7. Meiqin Mao , , Yong Ding, Liuchen Chang, Nikos D. Hatziargyriou, Fellow, Qiang Chen, Tinghuan Tao, and Yunwei Li, “Multi-Objective Power Management for EV Fleet With MMC-Based Integration Into Smart Grid” Ieee Transactions On Smart Grid, Vol. 10, No. 2, March 2019 8. Jahangeer Soomro, et. al., Comparative Analysis of Modular Multilevel Converter with Cascaded H Bridge Inverter using Five, Seven and Nine levels” International Journal of Recent Technology and Engineering, Vol. 7, Iss. 5S4, 2019 9. Elena Vergori ID, Francesco Mocera ID and Aurelio Somà ,I”Battery Modelling and Simulation Using a Programmable Testing Equipment”, 2018. 10. Yi Wang, et. al., “Nearest Level PWM Method for the MMC in DC Distribution Grids” North China Electric Power University, Baoding 071003, China. 11. Zhan Ma , Feng Gao , Xin Gu , Nan Li , Decun Niu “An Online SOH Testing Method of MMC Battery Energy Storage System” Jinan, China -2018. 12. Amrutvahini College of Engineering, Sangamner, India2,”Chandrakant Rahane, Vaibhav Varpe “Design and fabrication of regenerative braking system”, 2018. 13. Mattia Ricco, et. al., “FPGA-Based Implementation of MMC Control Based on Sorting Networks” at Cergy-Pontoise University, 95031 Cergy Pontoise, France; 2018. 14. Pratik Bhandari, et. al., Regenerative Braking Systems (RBS)” International Journal of Scientific & Engineering Research, University of Mumbai, India, 2017. 15. Sourabh Rathore, Mukesh Kumar Kirar and S. K Bhardwaj, “ H- Bridge Multilevel Inverter Using Pd, Pod, Apod Techniques”, Department of Electrical Engineering, MANIT, Bhopal, 2015..An integrated framework for decision support in corporate planning.R. Ramesh, and G. Sekar. Decis. Support Syst., 4 (3):
365-375(1988)Elucidating Quantum Semi-empirical Based QSAR, for Predicting Tannins' Anti-oxidant Activity with the Help of Artificial Neural Network.C. Gopalakrishnan, C. Xu, Y. Li, V. Anandhan, S. Gangadharan, M. Paul, C. Ponnusamy, R. Ramalingam, P. Han, and Z. Li. ICIC (2), volume 13394 of Lecture Notes in Computer Science, page 289-301. Springer, (2022)On the Design of a Cost-Effective and Lightweight People Counting Sensor.S. Viswanath, S. Gubba, B. Arunn, C. Veerappan, and C. Yuen. IoT360 (2), volume 151 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 176-182. Springer, (2014)A power aware mechanism for energy efficient routing in MANET.P. Sekar, and H. Mangalam. Int. J. Netw. Virtual Organisations, 21 (1):
3-18(2019)Multi-Agent Cross-Platform Detection of Meltdown and Spectre Attacks.X. Zhao, C. Veerappan, P. Loh, Z. Tang, and F. Tan. ICARCV, page 1834-1838. IEEE, (2018)Taxonomy on malware evasion countermeasures techniques.C. Veerappan, P. Loh, Z. Tang, and F. Tan. WF-IoT, page 558-563. IEEE, (2018)Selection of Bands for Secondary Metabolites in R. Arboreum using Hyperspectral Data.A. Gupta, P. Srivastava, K. Shanker, and K. Sekar. IGARSS, page 7590-7593. IEEE, (2023)STWS approach for Hammerstein system of nonlinear Volterra integral equations of the second kind.R. Sekar, and K. Murugesan. Int. J. Comput. Math., 94 (9):
1867-1878(2017)Metacognitive Scaffolding Amplifies the Effect of Learning by Teaching a Teachable Agent.N. Matsuda, V. Sekar, and N. Wall. AIED (1), volume 10947 of Lecture Notes in Computer Science, page 311-323. Springer, (2018)
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