Dr. Arash M. Dizqah
Dr. Arash M. Dizqah is currently the lecturer in Intelligent Transport Systems at the School of Mechanical, Aerospace and Automotive Engineering, which he joined after about 2 years of experience as a post-doctoral research fellow with University of Surrey. He is also a member of the Centre for Mobility & Transport (CMTR) at Coventry University.
He received BSc and MSc degrees in electrical engineering from Sharif and KN Toosi Universities of Technology, Tehran, Iran, in 1998 and 2001, respectively, and a Ph.D. degree from Northumbria University, U.K., in 2014. Before starting his PhD, he used to work as an embedded systems (software and hardware) developer and architect for about 10 years.
His research interests lie in control and optimisation with applications in transportation and renewable energy systems. He is particularly interested in real-time implementation and experimental testing of nonlinear optimisation-based controllers for the applications like electric/hybrid vehicles and wind turbines. The optimisation-based strategies, such as model predictive controllers, handle nonlinearities, uncertainties and constraints in a systematic fashion.
He is a member of institute of electrical and electronics engineering (IEEE) and institution of engineering and technologies (IET).
His Google Scholar is available by clicking here.
- Kamjoo, A., Maheri, A., Dizqah, A.M., and Putrus, G.A. (2016) ‘Multi-Objective Design Under Uncertainties of Standalone Hybrid Renewable Energy System Using NSGA-II and Chance Constrained Programming’. International Journal of Electrical Power and Energy Systems 74, 187–194.
- Dizqah, A.M., Maheri, A., Busawon, K., and Kamjoo, A. (2015) ‘A Multivariable Optimal Energy Management Strategy for the Standalone DC Microgrids’. IEEE Transactions on Power Systems 30 (5), 2278–2287.
- Dizqah, A.M., Maheri, A., Busawon, K., and Fritzson, P. (2015) ‘Standalone DC Microgrids as Complementarity Dynamical Systems: Modeling and Applications’. Control Engineering Practice 35, 102–112.
- Dizqah, A.M., Maheri, A., and Busawon, K. (2014) ‘An Accurate Method for the PV Model Identification Based on a Genetic Algorithm and the Interior-Point Method’. Renewable Energy 72, 212–222.
- Dizqah, A.M., Maheri, A., Busawon, K., and Kamjoo, A. (2014) ‘Modelling and Simulation of Standalone Solar Power Systems’. International Journal of Computational Methods and Experimental Measurements 2 (1), 107-125.
- Dizqah, A.M., Maheri, A., Busawon, K., and Fritzson, P. (2013) ‘Acausal Modelling and Dynamic Simulation of the Standalone Wind-Solar Plant using Modelica’. in UKSim2013, ‘15th International Conference on Computer Modelling and Simulation’. Held 10-12 April 2013 at Cambridge University, UK. IEEE, 580-585.
- Dizqah, A.M., Busawon, K., and Fritzson, P. (2013) ‘Acausal Modeling and Simulation of the Standalone Solar Power Systems as Hybrid DAEs’. SNE Simulation Note Europe 23 (3-4), 171-178.
- Dizqah, A.M., Maheri, A., and Busawon, K. (2012) ‘An Assessment of Solar Irradiance Stochastic Model for the UK’. In Rajbhandari, S., Busawon, K., Marheri, A., and Djemai, M. (eds.) Proceedings of the 2nd International Symposium on Environmental Friendly Energies and Applications, ‘EFEA2012’. Held 25-27 June 2012 in Newcastle upon Tyne, UK. IEEE, 623-627.
- Kamjoo, A., Maheri, A., Ghanim, P., and Dizqah, A.M. (2012) ‘Optimal Sizing of Grid-Connected Hybrid Wind-PV Systems with Battery Bank Storage’. In Fellows, C. (ed.) Proceedings of the World Renewable Energy Forum, ‘WREF 2012’. Held 13-17 May 2012, Denver, CO.
- Integrated Control of Multiple-Motor and Multiple-Storage Fully Electric Vehicles. The key objectives are; A) Integration of the energy management, thermal management, driveability control and vehicle dynamics control into a single supervisory controller, using control allocation and model predictive control techniques between the multiple motors. B) Demonstration of the compatibility of the integrated control software with the actual computational power of novel multi-core automotive control units. C) Integration of the unified controller with cloud-sourced information for the enhanced estimation and prediction of the vehicle states within a cooperative vehicle-road infrastructure, including semi-autonomous driving.
- Energy Management Strategies for Hybrid Renewable Energy Sites. In order to realise constant current-constant voltage (IU) charging regime and increase the life span of batteries, energy management strategies require being more flexible with the power curtailment feature. The main objective of the project was to design and develop a coordinated and multivariable energy management strategy that employs a wind turbine and a photovoltaic array of a standalone DC microgrid as controllable generators by adjusting the pitch angle and the switching duty cycles. The proposed strategy is developed as an online nonlinear model predictive control (NMPC) algorithm.