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Öğe Batarya yönetim sistemleri(İnönü Üniversitesi, 2019) Ekici, Yunus EmreBu tez çalışmasında elektrikli araçlar teknolojisinde önemli bir yere sahip olan batarya yönetim sistemi, farklı pil kimyaları ve hibrit bir elektrikli araç kullanılarak simule edilmiş ve bir batarya yönetim sistemi modellemesi yapılmıştır. Pil kimyalarına göre literatürde farklı batarya yönetim sistemi geliştirilmiştir fakat farklı pil kimyalarının tek bir batarya yönetim sistemi ile kontrolü henüz sağlanamamıştır. Gerilim seviyelerinin ve kapasitelerinin farklı olması bataryaların ortak bir yönetim sisteminden yönetilmesini zorlaştırmaktadır. Bu durum elektrikli araç teknolojisindeki temel problemlerden biridir. Yapılan çalışmada dört farklı kimyaya sahip olan (Lityum Demir Fosfat, Nikel Metal Hidrit, Nikel Kadminyum ve Kurşun Asit) piller, batarya paketleri haline getirilip aynı batarya yönetim sistemi ile iki farklı şekilde kontrol edilmiştir. İlk olarak sabit bir DC besleme ile tüm bataryaların şarj işlemleri gerçekleştirilmiştir. İkinci olarak bataryalar hibrit bir elektrikli araç modeline entegre edilip araç hareket halinde iken deşarjı sağlanmış ve daha sonra jeneratör ile şarj edilmiştir. Her iki durumda da batarya yönetim sisteminin çalışması analiz edilmiştir. Bu tezde yapılan çalışmalar neticesinde elektrikli araç teknolojilerinin temel problemlerinden biri olan farklı kimyalara sahip bataryaların tek bir batarya yönetim sistemi ile kontrol edilmesi incelenmiştir. ANAHTAR KELİMELER: Batarya yönetim sistemi, bataryalar, elektrikli araçlar, lityum piller, nikel piller.Öğe Effect of outside temperature on energy consumption of electric vehicles: Real-time big data and artificial intelligence-aided seahorse optimization approach(Gazi Univ, Fac Engineering Architecture, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet ArifIn calculating the energy consumption of electric vehicles (EVs); it is very important to optimize the consumption efficiency and driving range by considering the outdoor temperature. Studies have shown that very low and very high temperatures reduce engine efficiency and significantly increase energy consumption, while affecting regenerative energy recovery. Therefore, in the presented study, the effects of outdoor temperature on range and energy consumption were investigated using real-time big data obtained from Electric Buses (EO). The field application of the study was carried out with 22 24.7-meter EOs. The EO route was divided into 4 different regions and the energy consumption for each region and the analysis of the outdoor temperature corresponding to this consumption were obtained using regression techniques. First, the energy consumption model was created and the driving cycle was calculated for each region. Then, the driving cycle for the entire route was created and the energy consumption on the route was expressed as a mathematical model. Trilayered Neural Network (TNN) gave the best result in the calculations of the entire route. Finally, the mathematical model obtained as a result of TNN was reconsidered using the SeaHorse optimization method. Considering the analysis for the entire route (R), it was calculated that the most efficient consumption is 3.02 kWh/km and this consumption value can be achieved with a temperature of 21.5oC. This study has become a reference study for other electric vehicle manufacturers in determining the range of their vehicles in different climate conditions.Öğe Efficiency Analysis of Various Batteries with Real-time Data on a Hybrid Electric Vehicle(2021) Ekici, Yunus Emre; Dikmen, İsmail Can; Nurmuhammed, Mustafa; Karadağ,TeomanBattery selection remains an up-to-date engineering problem for hybrid and electric vehicle manufacturers. Type of battery and its capacity will depend on the trip and vehicle parameters. An electric vehicle produced with the ideal bat-tery type will undoubtedly be preferred by customers. Data collected from black boxes of trolleybuses operated by Malatya Metropolitan Municipality were used in this study. The real road and driver characteristics were included in the study with the experimentally obtained data. These data are the accelerator pedal data obtained from vehicles driven by different drivers in regular and congested traf-fic hours. In this study, four different battery chemistries were run separately on a hybrid vehicle model and analyzed. Chosen battery chemistries are the most commonly used by manufacturers. These are Lead Acid, Nickel Cadmium, Nickel Metal Hydride and Lithium Iron Phosphate batteries. The results of the study are presented in detail comparatively. Among the battery chemistries, Lithium iron phosphate is observed to be the most ideal battery type for hybrid electric vehi-cles.Öğe Elektrikli araçlarda menzil artışına yönelik yapay zekâ tabanlı sürdürülebilir ulaşım modelinin geliştirilmesi, optimizasyonu ve analizi(İnönü Üniversitesi, 2025) Ekici, Yunus Emre; Karadağ, TeomanTez çalışmasında, elektrikli araçlarda menzil uzatmak için gerçek zamanlı büyük veriler kullanarak elektrikli aracın kritik parametreleri, yol parametreleri ve coğrafi parametreleri de dahil ederek yeni bir enerji tüketim tahmin modeli geliştirilmiştir. Bu model ile elektrikli araç dönüşümü yapılacak olan rotalarda, elektrikli aracın enerji tüketim tahmini, kullanılacak olan aracın motor tipi ve güç seçimi, rotaya göre batarya kapasitesi seçimi yapılmıştır. Çalışma boyunca büyük veriler kullanılarak enerji tüketim tahmin modelleri elde edilmiştir. Bu modeller oluşturulurken aracın ve rotanın kritik parametreleri arasında yer alan hız, ağırlık, eğim, dış ortam sıcaklığı, elektriksel ve mekaniksel arıza, ivmelenme ile faydalı frenleme gücü kullanılmıştır. Tez çalışması altı adımda ele alınmıştır. İlk adımda literatür taraması yapılmış ve ikinci adımda ise Trambüs'lerin kara kutusunda 1 milyar satır veri toplanmıştır. Üçüncü adımda, BAP doktora tez projesi kapsamında yeni bir kara kutu alınıp araca yerleştirilerek veriler alınmış, eski ve yeni veriler arasında incelemeler yapılmıştır. Dördüncü adımda, Lineer Regresyon yöntemi ile MTECM enerji tüketim tahmin modeli elde edilmiştir. Beşinci adımda GPR kullanılarak GMTECM elde edilmiştir. Son adımda ise SHO-EBECM enerji tüketim tahmin modeli SeaHorse optimizasyon tekniği ile oluşturulmuştur. Modeller, Malatya'da halihazırda otobüs işletmesi yapılan en yoğun rotalara uygulanmış ve işletmenin içten yanmalı araçlar yerine elektrikli araç dönüşümü yapıldığı taktirde rotalar için olası enerji tüketim değerleri hesaplanmıştır. Geliştirilen tüm tahmin modelleri, literatürde yer alan diğer tüketim modelleri ile hata oranı açısından karşılaştırmalı olarak olarak değerlendirilmiş sunulmuştur. Anahtar Kelimeler: Büyük veri, Elektrikli araçlar, Regresyon, SeaHorse, Optimizasyon.Öğe Energy consumption model with real-time data for driving range extension of electric buses(Elsevier, 2025) Ekici, Yunus Emre; Aydin, Ahmet Arif; Karadag, Teoman; Akdag, Ozan; Ates, AbdullahPreventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a more environmentally sustainable transportation model. This contributes significantly to reducing greenhouse gas emissions and mitigating the environmental impact of transportation. In this study, 250,000 rows of real-world data were collected from electric Trolleybus vehicles for a realistic energy consumption estimation of EVs. First, a mathematical model was obtained from these data using Gaussian Process Regression (GPR) method. To reduce the error rate of this model and increase the accuracy of consumption estimation, it was necessary to re-analyze it with an optimization technique. The accuracy of the consumption prediction model is extremely important for increasing the range of EVs and enabling uninterrupted travels. To solve range anxiety problem, the mathematical model obtained by GPR method is re-optimized by SeaHorse optimization and a new energy consumption prediction model, SHO-EBECM (Seahorse Optimized-Electric Bus Energy Consumption Model), is obtained. The trained SHO-EBECM was applied to 20 real routes of public transportation with internal combustion engine buses in a metropolitan city and the RMSE (Root Mean Square Error) value has been calculated to be between 0.1470 and 0.2920. Based on the achieved error rate, it can be inferred that SHO-EBECM offers a solution with a reduced error rate in comparison to four other optimization techniques. Furthermore, considering global warming, carbon emissions and ecological balance, it is concluded that approximately 12,060 tons/year of CO2, 372.75 tons/ year of NO and NO2 gases can be prevented from being emitted to nature by converting internal combustion engine buses on 20 different routes to electric buses (E-Bus) with the help of SHO-EBECM.Öğe Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach(Elsevier, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Hueseyin OzanElectrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in T & uuml;rkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in T & uuml;rkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO2, NO, and NO2 emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.Öğe Impact of Outside Temperature on Driving Range and Energy Consumption Using Real-Time Big Data for Electric Buses(Institute of Electrical and Electronics Engineers Inc., 2024) Ekici, Yunus Emre; Karadag, Teoman; Aydin, Ahmet Arif; Akdag, OzanCalculating the energy consumption of electric vehicles (EVs) is crucial to optimize efficiency and driving range, taking into account the outdoor temperature. Research shows that low temperatures significantly increase motor and battery energy consumption while inhibiting regenerative energy recovery, with optimum efficiency achieved at around 20-30 degrees Celsius. Furthermore, the use of heating and cooling systems in different seasons also affects the overall efficiency by affecting battery energy consumption. Therefore, outdoor temperature and driving conditions must be taken into account to accurately assess and optimize the energy consumption of EVs. In this study, the effects of outdoor temperature on range and energy consumption are analyzed using real-time big data from Electric Buses (EB). The field application of the study is based on the EB route currently in operation in Malatya. The EB route is divided into 4 different regions and the energy consumption and the corresponding outdoor temperature for each region are analyzed using regression analysis techniques. As a result of the calculations, it was calculated that the most efficient consumption for the entire EB route is 3,02 kWh / km and this consumption value can be achieved with a temperature of 21,5° C. © 2024 IEEE.Öğe A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanElectric vehicles (EVs) have positive impacts on reducing oil dependence and exhaust emissions. However, the range problem of EVs is a factor that raises concerns for individual users and bus operators. For this reason, studies on increasing the range of the electric buses in public transportation is extremely important to ensure optimum operation. In this study, a novel energy consumption model, MTECM (Malatya Trolleybus Energy Consumption Model), is developed using the multi-parameter linear regression method. The real-time big data was collected on the field of Trolleybus vehicles, which have been operated for 8 years in Malatya / Turkiye. Firstly, by calculating the correlation of the parameters affecting this model, the parameters that are suitable for the purpose of our study are determined and regression analysis is performed on the original Trolleybus dataset. A total of 75.497.472 data are analyzed for this model. The RMSE (Root Mean Square Error) of MTECM is calculated as 0.29996. The trained model is applied to the 10 busiest routes in Malatya in terms of passenger density. The RMSE value on these routes is calculated between 0.30299 and 0.31421. Based on the results, with lower error rates, the proposed novel model is more efficient than other studies in the literature. In addition, energy consumption can be calculated for any route planned to establish an electric bus operation with MTECM. Therefore, according to the consumption obtained, the correct determination and selection of parameters that significantly affect the investment cost such as route, vehicle length, engine power, and battery capacity can be made.Öğe Optimization of Proportional-Integral-Derivative Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization(Aves, 2024) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanThe two different motion behaviors of seahorses in nature served as inspiration for the seahorse optimization (SHO) method, which is a new metaheuristic swarm intelligence-based approach to solving fundamental engineering problems. In this study, the propo rtion al-in tegra l-der ivati ve (PID) parameters for the simplified speed control of the manipulator joint using squirrel-cage induction motors were calculated with the SHO algorithm. As a result of these calculations, Kp, Ki, and Kd values were obtained as 0.0430, 0.00474, and 0.03254, respectively. Then, the time for the squirrel-cage motor to reach 50 rpm (revolutions per minute) and 90 rpm was calculated with the help of SHO. In PID + SHO operation, the squirrel-cage electric motor reached 50 rpm in 3 seconds and 90 rpm in 8 seconds. In this study, in which the SHO optimization method was used, it was calculated that the acceleration of the squirrel-cage motor and reaching the desired value gave 50% better results compared to the particle swarm optimization algorithm.Öğe Redefining urban mobility: Real-world regenerative braking optimization via bio-inspired AI for electric buses energy efficiency(Pergamon-Elsevier Science Ltd, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, OzanIn the era of global decarbonization and sustainable urban mobility, optimizing energy recovery in electric public transport systems has become a strategic imperative. This study presents a comprehensive and data-driven investigation into the optimization of regenerative braking (RB) performance in hybrid electric trolleybuses operating in T & uuml;rkiye. Unlike conventional research relying on controlled driving cycles or laboratory simulations, this work employs real-world operational data totaling over 79 million records collected over five years to construct a high-fidelity predictive model. Using a novel meta-heuristic algorithm inspired by the physiological mechanism of water uptake and transport in plants (WUTP), eight critical parameters influencing RB power including vehicle speed, gradient, acceleration, passenger mass, ambient temperature, and auxiliary system loads were integrated into a robust mathematical framework. The resulting model, WUTP-EBREM, accurately predicts regenerative braking power under varying operational conditions, achieving a minimal error rate (RMSE: 0.12 %). This enables a fine-grained understanding of how instantaneous operating dynamics affect energy recovery in electric buses. Subsequently, the model was applied to 50 of the city's busiest bus routes, each with distinct topographical and operational characteristics. Route-based analyses revealed substantial variability in RB potential, highlighting that optimal energy recovery depends not only on vehicle design but also on contextual factors such as slope patterns and thermal loads. The findings offer direct implications for fleet energy planning, battery sizing, and route optimization, providing actionable insights for transit operators and electric vehicle manufacturers alike. Beyond its empirical contributions, this research introduces a scalable modeling architecture adaptable to various geographic regions and climate conditions, bridging the gap between theoretical energy models and field-level implementation. By capturing the stochastic, non-linear nature of urban electric bus operation, the study sets a precedent for integrating artificial intelligence into real-time transport energy optimization. The WUTP-EBREM model stands as a unique decision-support tool for smart transportation planning, offering both academic value and immediate practical utility. This work is poised to inform future studies in energy-aware vehicle control systems, sustainable transit infrastructure, and intelligent fleet management strategies in electrified urban environments.Öğe A Review on Electric Vehicle Charging Systems and Current Status in Turkey(2021) Karadağ, Teoman; Nurmuhammed, Mustafa; Ekici, Yunus Emre; Dikmen, İsmail CanThe reality of global warming brings along an increase in environmental aware-ness. In recent years, this awareness has shifted public focus on electric vehicles. For a large group of people greenhouse gas emission is attributed to internal combustion engines. However, some challenges have arisen for electric vehicles. Limited range due to immature battery technologies and insufficient fast charging technologies that do not meet end user expectations are some major obstacles to overcome. Eventual-ly this situation negatively affects the sales and the wide use of electric vehicles. That is the reason why studies on wired and wireless charging systems play an important role in improving the sales performance of electric vehicles. In this study a thorough review of worldwide electric vehicle charging systems is conducted and discussed in the framework of the electric vehicles, charging stations, installations, and implemen-tation of standards in Turkey. The distribution of charging stations in Turkey are analyzed with respect to location, region, type, infrastructure requirements and future projections. The historical development of charging technologies, modes and charge levels have been studied in detail. As highlight of this study, wireless charging tech-nologies were also discussed and the historical development process was analyzed along with related standards. The current state of electric vehicle sector and charging stations in Turkey are discussed and provided with up to date information.Öğe Tailoring Energy Efficiency for Urban Electric Buses: The GTECM Model for Enhanced Range and Sustainable Operation Using Real-Time Big Data(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Huseyn OzanThe increasing depletion of fossil fuels and growing environmental concerns are increasing the need for energy efficient and sustainable solutions, particularly in transport. At this point, especially in public transport, electric vehicles (EVs) offer a promising alternative; however, issues such as range anxiety and energy efficiency require comprehensive solutions. This study introduces the Gauss-based Trolleybus Energy Consumption Model (GTECM) for electric buses, harnessing real-time big data to mitigate range anxiety and enhance energy efficiency. This model employs Gaussian Process Regression to a large-scale dataset including 100,000 entries collected over six months in T & uuml;rkiye. With an overall Root Mean Square Error (RMSE) of 0.013905, GTECM substantially outperforms linear approaches across T & uuml;rkiye's primary routes, exhibiting route-specific RMSE values between 0.28117 and 0.30540. Empirical findings suggest potential energy savings of up to 50%, alongside a 10% extension in driving range, thereby mitigating an estimated 4,220 tons of CO2 and 129.88 tons of NO2 emissions annually. Moreover, the projected amortization period for diesel-to-electric bus conversion stands at 6.83 years, underscoring GTECM's pragmatic utility for sustainable urban transit optimization. The findings of the study can form the basis for future research and guide policy makers and urban planners in the development of more efficient and sustainable transport networks.Öğe The Impact of Driver Behavior on Electric Bus Energy Consumption: Optimizing Driver Performance with Bio-Inspired WUTP Algorithm with Real-Time Big Data(Society of Automotive Engineers Turkey, 2025) Ekici, Yunus Emre; Akdağ, Ozan; Yıldıran, Nisanur; Karadağ, TeomanThe transition toward sustainable urban mobility requires not only technological innovations in electric buses (E-Buses) but also optimization of operational factors such as driver behavior, which significantly influences energy consumption and driving range. This study develops a novel artificial intelligence framework, integrating real-time big data with a bio-inspired Water Uptake and Transport in Plants (WUTP) algorithm, to optimize E-Bus driver performance under real-world conditions. Data were collected from trolleybus-type hybrid electric buses operating in Malatya, Turkey, encompassing nearly 50 milhon observations across diverse seasonal, topographical, and operational contexts. Through preprocessing and correlation-based feature selection, 14 key parameters—including regenerative braking, auxiliary loads (HVAC and static converters), acceleration, and road slope—were identified as critical determinants of energy consumption. The WUTP algorithm, implemented with 60,000 representative data rows, generated optimized driving profiles and weighting coefficients, enabling precise estimation of optimal operational thresholds. Results reveal that maintaining regenerative braking above 77%, moderating accelerator pedal use at approximately 44%, and stabilizing average vehicle speed significantly extend range and reduce energy demand. Comparative evaluation of six drivers demonstrated efficiency disparities exceeding 30%, underscoring the importance oftraining and monitoring systems. The proposed model is distinguished by its dynamic treatment of auxiliary loads, scalability across routes and climates, and applicability for fleet planning, battery sizing, and eco-driving assessment. Overall, this research contributes a robust, adaptable, and scalable framework that enhances operational efficiency, reduces environmental impact, and supports the broader deployment of sustainable E-Bus systems in global transit networks. © (2025), (Society of Automotive Engineers Turkey). All rights reserved.
