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Öğe A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)(Elsevier Ltd, 2024) Korkmaz, MehmetDeep learning-based (DL) methods have recently come to the forefront among the data-driven models due to their success in capturing the complexities of the battery. Many previous DL-based studies for SoC estimation have almost exclusively focused on improving DL structure by proposing various architectures. Questions regarding the outlier or atypical predictions have yet to be adequately addressed. Furthermore, few works benefit from optimization algorithms to determine the hyperparameters of DL. In this study, we have addressed the problem of how to obtain the hyperparameter of DL and fix the improper DL predictions. To this aim, we used two different optimization algorithms to determine the hyperparameters of DL and proposed a novel algorithm that considers the previous SoC estimations. The algorithm either approves or rejects the DL predictions for the relevant step and offers new values for the rejected ones. The proposed scheme is evaluated using a battery dataset which includes different driving cycles. According to the results, it is observed that the optimized DL outperforms the empirical one by at least 35% in terms of performance indices. Moreover, the proposed novel algorithm successfully integrates into all variations and significantly improves the performance index scores.Öğe A novel method for SoC estimation of lithium-ion batteries based on previous covariance matrices and variable ECM parameters(Springer Science and Business Media Deutschland GmbH, 2023) Korkmaz, MehmetLithium-Ion battery powered electric vehicles (EVs) offer many benefits, such as having high energy efficiency, requiring lower maintenance, and being cheaper to run. Besides, they play a crucial role in decarbonization. These advantages will probably make it an indispensable option for both drivers and governments in the future decades. However, the bottleneck of them is the batteries. In particular, accurate estimation of state of charge (SoC) of batteries, which refers to the remaining driving range, is one of the most notable challenges for EVs. With this in mind, in this paper, a novel Kalman filter-based estimation method is proposed to increase the accuracy of the SoC. The proposed method considers not only the current but also the previous covariance matrices since abrupt changes in the nonlinear dynamics of the battery may lead to incorrect estimation. Consequently, smoother state transitions are provided, and more accurate SoC estimation is possible. The improved method is supported by the 2-RC Thevenin equivalent circuit model, whose parameters are described as a function of the SoC and temperature. The battery model and proposed method are tested with three different driving cycles to prove the efficiency. According to the results, the proposed method can minimize the RMSE of SoC estimation up to at least 10% and provides better SoC estimations for compact EVs.Öğe Comparison of optimal path planning algorithms(IEEE, 2018) Korkmaz, Mehmet; Durdu, AkifThis work is concerned with path planning algorithms which have an important place in robotic navigation. Mobile robots must be moved to the relevant task point in order to be able to fulfill the tasks assigned to them. However, the movements planned in a frame or random may affect the duty time and even in some situations, the duty might be failed. When such problems are taken into consideration, it is expected that the robots should go to the task point and complete their tasks within the shortest time and most suitable way. It is aimed to give results about a comparison of some known algorithms. With this thought, a map for a real time environment has been created and the appropriateness of the algorithms are investigated with respect to the described starting/end points. According to the results, the shortest path is found by the A* algorithm. However, it is observed that the time efficiency of this algorithm very low. On the other hand, PRM algorithm is the most suitable method in terms of elapsed time. In addition to this, algorithm path length is closer to the A* algorithm. The results are analyzed and commented according to the statistical analysis methods.Öğe Compositional and structural design of thermoplastic polyurethane/carbon based single and multi-layer composite sheets for high-performance X-band microwave absorbing applications(Elsevier Ltd, 2019) Kaşgöz, Alper; Korkmaz, Mehmet; Durmuş, AliIn this study, morphological features, dielectric properties, and microwave absorbing performances of thermoplastic polyurethane composite sheets prepared with solution mixing method by using different amounts and types of carbon fillers, carbon black (CB), graphite (G), carbon nanofiber (CNF), and multiwalled carbon nanotube (MWCNT) were investigated, in detail. Microstructural properties and microwave absorbing characteristics of composites were analyzed with scanning electron microscope and vector network analyzer by transmission line method, respectively. Reflection loss (RL) analyses of composites showed that CNF and CNT were more efficient than CB and G for improving the electromagnetic wave absorbing performances of sheets at low filler contents. It was found that the broadest effective absorption area (EAA) (10.03–12.96 GHz) was obtained with the sample having 3 phr of CNF. However, single layer TPU/carbon composite sheets were not effective “RADAR stealth” materials because their RL values were higher than ?10 dB at X-band. Thus, multilayer (two, three, and four-layer) composite structures were designed to obtain higher microwave absorption performance within this frequency range via genetic algorithm approach. Multilayer design and modeling works showed that a four-layer composite with a total thickness of 4.8 mm yielded excellent microwave absorbing performance and broad EAA, 6.98–13.415 GHz, at X-band.Öğe Human-aware dynamic path planning(Institute of Electrical and Electronics Engineers Inc., 2021) Korkmaz, MehmetThis study is related to human-aware navigation (HAN) of a robot moving in a 2D plane. During the navigation process, the robot first finds a global path given start and goal points and it also dynamically scans the environment for possible new obstacles and rearranges its path plan as to those encountered ones. It is well-known fact that treating humans as an ordinary object by robots while passing them leads to humans feeling stressed since the actions of robots still are not explicit for them. For this reason, besides planning strategy secondly, it is also considered whether obstacles are human or non-human ones via the improved algorithm. By doing so, it is aimed for the robot to navigate in an environment considering human comfort. To examine this intention, two different path planning algorithms improved by dynamically classical and human obstacles aware scheme are tested on a home-like environment. According to the results, it has been observed that the robot dynamically plans its navigation framework given target points and new obstacles. Furthermore, it successfully achieves to keep distance from human obstacles based on the proxemics theory distance requirements.Öğe Nickel-coated carbon fillers for polyurethane foam with improved microwave absorption performance: A comparative analysis(Wiley, 2024) Kurt, Gökçe; Korkmaz, MehmetIn this study, polyurethane foam (PUF) composites were prepared by incorporating different functional fillers, and morphological, rheological, dielectric, and microwave absorption properties of these composite samples were investigated. Graphite (G), nickel (Ni), nickel-coated hybrid carbon (NiC), and iron II-III oxide (Fe3O4) powders were used as functional fillers, while rigid polyurethane foam (PUF) with a 40 kg/m3 density and a closed-cell structure was used as the carrier foam phase. Morphological analyses that were carried out by scanning electron microscopy showed that all fillers were dispersed between the cell walls. In dielectric characterization, the highest electrical permittivity values were obtained by incorporation of G, while the highest magnetic permeability values were obtained by incorporation of Fe3O4 at the same filler concentration. Furthermore, the PUF composites could be prepared using NiC at a higher filler concentration than the others due to the lower viscosity increasing effect of NiC. The highest microwave absorption performance was obtained in the NiC-filled PUF composite at a filler concentration of 120 phr. The minimum reflection loss (RL) value of -45.2 dB at 10.62 GHz and an RL value lower than -10 dB in the entire X-band region were obtained in the NiC-filled PUF composite.HighlightsNickel-coated carbon (NiC) filled polyurethane foam (PUF) was prepared.The absorption performance of NiC was compared to conventional filler.PUF composites containing NiC showed lower viscosity and processability.The highest absorption could be obtained by NiC compared to conventional fillers.Öğe Optimal design of a variable coefficient fractional order PID controller by using heuristic optimization algorithms(Science and Information Organization, 2019) Aydoğdu, Ömer; Korkmaz, MehmetThis paper deals with an optimal design of a new type Variable coefficient Fractional Order PID (V-FOPID) controller by using heuristic optimization algorithms. Although many studies have mainly paid attention to correct the performance of the system's transient and steady state responses together, few studies are interested in both transient and steady state performances separately. It is obvious that handling these two cases independently will bring out a better control response. However, there are no studies using different controller parameters for the transient and steady state responses of the system in fractional order control systems. The major contribution of the paper is to fill this gap by presenting a novel approach. To justify the claimed efficiency of the proposed VFOPID controller, variable coefficient controllers and classical ones are tested through a set of simulations which is about controlling of an Automatic Voltage Regulator (AVR) system. According to the obtained results, first of all it was observed that proposed V-FOPID controller has superiority to the classical PID, Variable coefficient PID (V-PID) and classical Fractional Order PID (FOPID) controllers. Secondly, Particle Swarm Optimization (PSO) algorithm has shown its advantage compared to the Artificial Immune System (AIS) algorithm for the controller design. © 2018 The Science and Information (SAI) Organization Limited.Öğe SoC estimation of lithium-ion batteries based on machine learning techniques: A filtered approach(Elsevier Ltd, 2023) Korkmaz, MehmetAccurate state-of-charge (SoC) estimation is an essential requirement for many situations where Li-Ion batteries (LiBs) are used. This ensures an efficient battery management system (BMS), so the battery can be protected from excessive discharge, and its life span can be maximized. But when it comes to electrified vehicles (xEVs), the SoC estimation accuracy becomes a more critical and indispensable prerequisite. Because the safety of xEVs during driving and the remaining range, which is an indicator of how far the vehicle can go, are directly related to the accurate SoC. However, the complex electrochemical reactions in the battery and the dependence on environmental variables make SoC estimation a challenging task. Traditionally, this is tackled by establishing either electrochemical or electrical battery equivalent models. Both methods suffer from some limitations, such as parameter identification, complex calculations, and model mismatching due to the aging factor. On the other hand, data-driven methods have recently become a popular choice for SoC estimation since they enable building data-based models rather than chemical reactions or equivalent circuit calculations. The model is built based on battery parameters such as current, voltage, battery type, and then used for SoC estimation. However, many studies in the literature examine only a few methods for SoC estimation. Also, these data-driven black box models can lead to outlier data as they are not observers. Thus, the aims of this study are twofold: First, to make a comprehensive comparison based on most of the ML methods. Second, to utilize several filters for outlier removal and measure their effectiveness. For these purposes,18 ML algorithms were handled in three main groups, and SoC estimation results were analyzed. Additionally, five different filters were used to improve the SoC estimation of these methods, and their comparisons were realized. From the results, it is clear that Bagging and ExtraTree algorithms are substantially better than other ML methods for SoC estimation since their Interquartile Range (IQR) is smaller than 3%, performance indices are the lowest ones, and curve matches are the best. Also, Rloess is the best filter among the others, although they all achieved high performance in outlier removal.
