Browsing by Author "Tasooji, Tohid Kargar"

Now showing 1 - 4 of 4

Metadata only
Design and simulation of an optimal energy management strategy for plug-In electric vehicles
(IEEE, 2018) Gözüküçük, M. A.; Akdoğan, Taylan; Hussain, Waqas; Tasooji, Tohid Kargar; Şahin, Mert; Çelik, M.; Uğurdağ, Hasan Fatih; Natural and Mathematical Sciences; Electrical & Electronics Engineering; AKDOĞAN, Taylan; UĞURDAĞ, Hasan Fatih; Hussain, Waqas; Tasooji, Tohid Kargar; Şahin, Mert
Energy management algorithms play a critical role in improving the energy efficiency of modern electric vehicles. In order to be desirable for the customer, electric vehicles should be capable of long distance driving on a single battery charge with a range which must be comparable to the values of their conventional counterparts. To achieve this goal, both the use of large-capacity battery and the development of a custom energy management algorithm are necessary. Thus, one must solve equations of vehicle dynamics, which is a part of conventional methods used in generalized energy management problems. In this paper, a Monte Carlo method is proposed for probabilistic prediction of the optimum energy to attain a given route. The route in question is obtained from the Google Maps and includes locations and road topologies. First, optimum speed set-points are generated for each state of the journey, and this generated speed array is imported into the vehicle control system to generate the required torque for vehicle propulsion. Then, this process is repeated with a constant average speed for comparison purposes. The simulation results show that an electric vehicle gains significant energy efficiency over a Hardware in the Loop (HIL) emulation, when it is being controlled with the proposed speed set-points generated by the Monte Carlo method.
Metadata only
Energy consumption modeling and optimization of speed profile for plug-in electric vechiles
(2018-05) Tasooji, Tohid Kargar; Akdoğan, Taylan; Uğurdağ, Hasan Fatih; Akdoğan, Taylan; Uğurdağ, Hasan Fatih; Tekin, Ahmet; Poyrazoğlu, Göktürk; Temeltaş, H.; Department of Electrical and Electronics Engineering; Tasooji, Tohid Kargar
In recent years, there is a surge in research on Plug-in Electric Vehicles (PEVs), as PEVs are one of the ways that we can reduce carbon emissions. Dramatic cost reduction in PEVs has resulted in their becoming mainstream vehicles. However, there is still one big hurdle before they can take over other types of vehicles, and that is their limited range. Lack of charging stations exacerbates the situation further, and proliferation of charging stations does not completely alleviate the range problem as charging can take a few hours. Therefore, a PEV should cover as much distance with a given battery charge, or equivalently, should consume as little energy as possible for a given distance. The choice of the instantaneous control algorithm plays a role in this energy minimization problem, but on the other hand, the most critical aspect of this minimization problem is the speed pro le of the vehicle, which is given as a reference input to the instantaneous control algorithm. This thesis o ers a signi cant contribution to the literature in range optimization of PEVs given a realistic vehicle model, which it does through a combination of Monte Carlo and Newton-Raphson optimization methods.
Metadata only
Model predictive controller as a robust algorithm for maximum power point tracking
(IEEE, 2018-01-19) Tasooji, Tohid Kargar; Mostafazadeh, A.; Usta, Ö.; Tasooji, Tohid Kargar
In the recent years, solar energy has attended the world as a clean energy. Many researchers are focused on extracting maximum power from solar panels. So, Maximum Power Point Tracking (MPPT) is used for increasing efficiency of solar PV system. This paper presents Model Predictive Controller (MPC) as a method for Maximum Power Point Tracking (MPPT) and compares it, with Perturb and Observe (P&O) and Incremental Conductance algorithm (IC) techniques. The MATLAB/Simulink and SimPower Systems software packages are used to model a proposed strategy. Simulation results show that Perturb and Observe (P&O) and Incremental Conductance algorithm (IC) techniques have limitation for tracking the exact maximum power point in transient insolation conditions. However, Model Predictive Controller (MPC) method is robust against fast varying of insolation conditions. Also, MPC method is faster than P&O and IC technique according to the simulation results.
Metadata only
Voltage control of PV-FC-battery-wind turbine for stand-alone hybrid system based on fuzzy logic controller
(IEEE, 2018-01-19) Mostafazadeh, A.; Tasooji, Tohid Kargar; Şahin, M.; Usta, Ö.; Tasooji, Tohid Kargar
Most of power systems experience a transient mode. In a special case, the operation of solar panel and wind turbine is depended to solar irradiance and wind speed. In addition, maintenance dc-link voltage and output AC voltage regardless variations of wind speed and solar irradiation can be a challenging topic. In this paper, a new strategy based on the stability of dc-bus voltage for a hybrid power system is proposed. Firstly, the operation of dc-bus voltage for a stand-alone hybrid power system consists of solar panel and wind turbine is investigated. In this case, a PI controller is used to regulating at its reference value. Finally, the performance of dc-link voltage in a stand-alone hybrid system included a solar PV, wind turbine, battery, and the fuel cell is studied. Furthermore, the Fuzzy logic controller is used as a power management energy in this case. The MATLAB/Simulink and SimPowerSystems software packages are used to model a proposed stand-alone hybrid power system. Simulation results show that proposed strategy has better performance under fast variation of irradiance.