ASSOCIATE PROF. DR. TEY KOK SOON
Department of Computer System &Amp; Technology
Faculty of Computer Science and Information Technology
koksoonum.edu.myView CV | |
View 1-Page CV | |
Publons | |
Scopus Link | |
Biography | |
Tey Kok Soon received his BEng degree in Electrical Engineering and PhD degree from the University of Malaya, Malaysia, in 2011 and 2014 respectively. Since 2011, he has been a Research Assistant with the Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya. In 2015, he joined Department of Computer System and Technology, Faculty of Computer Science and Information Technology (FCSIT) as a Senior Lecturer. His research interests include renewable energy control system, energy management, power efficiency of PV system and inverter control of PV system. |
Publication
Finance
Project Title | Progress | Status |
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Sustainable Iot Applications In Lorawan Through Optimized Photovoltaic Energy Harvesting And Hybrid Battery Management Scheme |
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new |
Battery Charging Circuit for The Scrubber Machine |
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new |
Autonomous Wireless Charger For Floor Scrubber At Medical Facilities |
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on going |
This information is generated from Research Grant Management System |
A New Cross-Overlapped Decoupling Coil Structure for EV Dynamic Inductive Wireless Charging System
Design and performance evaluation of a solar-assisted biogas and air-source heat pump hybrid system for radiant heating applications
State of health indicators for second life battery through non-destructive test approaches from repurposer perspective
State of Health (SoH) estimation methods for second life lithium-ion battery-Review and challenges
Lithium-ion Battery Model Parameter Identification Using Modified Adaptive Forgetting Factor-Based Recursive Least Square Algorithm
Modular Voltage Equalizer Topology with Reduced Number of Switch Count for Enhancing the Energy Yield During Partial Shading Conditions For PV System
Lithium-ion Battery State of Energy Estimation Using Deep Neural Network and Support Vector Regression
Battery State of Charge Estimation Using Adaptive Extended Kalman Filter for Electric Vehicle application