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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 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 Measurement-based assessment and optimization of electric bus energy consumption under thermal variability using long-term field data(Elsevier Sci Ltd, 2026) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Huseyin OzanThis study investigates the effect of ambient temperature on the energy consumption of overhead battery-hybrid electric buses, also known as trolleybuses. The analysis uses real field data. The data were collected from 22 hybrid buses over 24 months. Each bus was monitored during daily passenger service. The onboard black-box units recorded the main operating signals at 1 Hz. This allowed second-by-second changes in speed, braking, passenger load, and energy use to be examined under real route conditions. The study is not based on standard driving cycles or simulation data. It uses measured data from daily bus operation. The dataset includes energy consumption, regenerative braking, vehicle speed, passenger load, road gradient, auxiliary power demand, and ambient temperature. Before the analysis, missing, corrupted, and inconsistent records were removed. This step was used to improve the reliability of the measurement dataset. In this study, outside temperature is not treated as a direct electric motor parameter. It is considered a factor that affects total energy use through battery behavior and heating, ventilation, and air conditioning demand. Different prediction methods were tested on the processed data. These methods include Decision Trees, Ensemble Learning, Gaussian Process Regression, Support Vector Machines, and Three-Layer Neural Networks. The Three-Layer Neural Network optimized with the Modified Tunicate Swarm Algorithm achieved the best predictive performance. According to the analysis results, the model aimed to provide a practical basis for seasonal energy forecasting and route-based fleet planning by evaluating the sensitivity of energy consumption over a wide temperature range.Öğe Optimization of Propo rtion al-In tegra l-Der ivati ve Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization(2024) ekici, 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 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.
