Pedram Asef

Biographical  Information 

Pedram Asef (BSc, MEng, PhD, CEng, MIET, SMIEEE, PGCE, FHEA) is a Lecturer (Assistant Professor) in Automotive Engineering specialising in Automotive Electrical and Electromechanical Systems at the Department of Engineering and Technology, University of Hertfordshire. He is a Fellow of Higher Education Academy (FHEA), a Charted Engineer (CEng) registered by Engineering Council, in the UK, and an IEEE Senior Member, recognised internationally due to his contributions to the profession. He was awarded his Ph.D. with Cum Laude grade in Electrical Engineering from the Polytechnic University of Catalonia-BarcelonaTech, in Spain. Prior to joining the University of Hertfordshire, he was a postdoctoral research fellow at the University of Surrey, in the UK, where he developed robust and multi-objective optimisation algorithms for energy consumption reduction and management purposes. He is Editor of Frontiers Energy System and Power Engineering Journal. He is also Lead Representative of IEEE Power and Energy Society, Young Professionals, in Europe region, since 2018. Currently, he is acting as a Chair and Committee Member of multiple IEEE International conferences.

He is the academic advisor for the UH Eco Racing team, responsible for designing, building and racing an energy efficient car in the annual Shell Eco-marathon competition. His previous and/ or current projects were funded by the UKRI, European Commission, and Texas State Center for Port Management on Transportation Electrification, Electrical Machines and Drives, Powertrain Development, and Renewable Energy Systems for urban applications. He has published +20 research papers at international conferences, indexed journals, and registered two patents.

Dr Pedram Asef's Research Interests:
Electrical Machines and Drives, Automotive Electrical Systems, Autonomous and Connected Vehicles
Advanced Optimisation Algorithms, Finite Element Analysis (FEA) in Related Applications (i.e. Electromagnetics), Vehicle Dynamics, Renewable Energy Systems (Wind and Solar Energies)
Machine Learning Methods and High-Dimensional Data Analysis, Systems and Integrations (i.e. Sensors, Filters).