Person: AKDOĞAN, Taylan
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Taylan
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AKDOĞAN
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ArticlePublication Open Access Crucial topics in computer architecture education and a survey of textbooks and papers(International Association of Engineers, 2020) Yıldız, A.; Gören, S.; Uğurdağ, Hasan Fatih; Aktemur, B.; Akdoğan, Taylan; Natural and Mathematical Sciences; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih; AKDOĞAN, TaylanWe have been teaching undergraduate computer architecture since 2012 in an unconventional way. Most undergraduate computer architecture courses are based on microprocessors, and they quickly move into advanced topics such as instruction pipelining, forwarding, branch prediction, cache, and even memory management unit. We instead spend only the last one-third of our course on these topics. The first two thirds of the course is devoted to microcontrollers, i.e., simple-minded processors with no memory hierarchy, no branch prediction, sometimes even no pipelining. Our claim is that it is very hard to truly grasp the advanced topics without full grasp of the basics. Equipped with the above approach, this article comes up with an all-inclusive list of crucial topics for computer architecture education, and it surveys 25 computer architecture textbooks as well as 38 computer architecture education papers to see how much they cover these topics. In addition to that, the article contains a concise description of the perspective of our course. One of the pillars of our course is a working CPU on FPGA. We have so far had around 600 students design their own unique CPUs using Verilog given a complete instruction set, close to 70% of them with complete success.
Conference paperPublication Metadata only Electric vehicle model parameter estimation with combined least squares and gradient descent method(IEEE, 2019) Gözüküçük, Mehmet Ali; Uğurdağ, Hasan Fatih; Dedeköy, Mert; Çelik, Mert; Akdoğan, Taylan; Natural and Mathematical Sciences; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih; AKDOĞAN, Taylan; Gözüküçük, Mehmet Ali; Çelik, Mert; Dedeköy, MertEnergy management algorithms have a crucial role in electric vehicles due to their limited driving range. For an energy management algorithm to be effective, we should model the vehicle as accurately as possible. That is, not only the structure of the model should be accurate, but also the parameters of the model should be accurate. In this work, we take the model of an electric vehicle and tune three parameters in it based on trip data, namely, vehicle mass, air drag coefficient, and rolling resistance coefficient. We do this by using Least Squares method to set the initial guess and then by optimizing the parameters using Gradient Descent. To the best of our knowledge, this is the first work that simultaneously estimates these three parameters. Our work is also unique in the sense that it combines Least Squares and Gradient Descent.Conference paperPublication 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, MertEnergy 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.Conference paperPublication Metadata only SOC estimation for li-Ion batteries using extended kalman filter with PID controlled process noise according to the voltage error(IEEE, 2019) Çelik, Mert; Gözüküçük, Mehmet Ali; Akdoğan, Taylan; Uğurdağ, Hasan Fatih; Natural and Mathematical Sciences; Electrical & Electronics Engineering; AKDOĞAN, Taylan; UĞURDAĞ, Hasan Fatih; Çelik, Mert; Gözüküçük, Mehmet AliState of Charge (SOC) estimation is critical for battery powered devices in order to find out the remaining charge level. This process is relatively straightforward when the battery is in the resting state. However, it can be challenging while the device is operating, due to the process disturbances and model uncertainties. Various kinds of approaches have already been proposed in the literature like Neural Networks, Kalman Filtering, and Nonlinear Observers. Nevertheless, proposed methods in the literature do not have fast response for initial condition errors. This paper proposes a new implementation of Extended Kalman Filter, which improves the convergence characteristics of states for SOC estimation. The importance of initial condition errors is articulated in this paper, especially from an automotive perspective.