Browsing by Subject "Traction motors"

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    Design and Finite Element Analysis of a 6/4 Pole Multi-Layer Fully Pitched Switched Reluctance Motor to Reduce Torque Ripple
    (Taylor and Francis Ltd., 2023) Sahin C.; Basaran S.
    —In this study, the design and analysis of multi-layer fully pitched winding switched reluctance motor (MFP-SRM) for general use (submersible pump, electric vehicles, etc.) have been performed. It is seen that the multi-layer switched reluctance motor (SRM) has higher output power when compared to the single-layer SRM. In multi-layer SRM, the motors in the layers are electromagnetically independent of each other although they are identically the same motors with the same characteristics in terms of performance and geometry. Each layer of the MFP-SRM which is designed in this study consists of a 6/4 pole fully pitched SRM (FP-SRM) and these motors are magnetically independent of each other. In the MFP-SRM, which is designed as a double layer, there is a 15° phase difference between the rotor position angles and torque profile curves of each layer. With the phase difference that changes depending on the number of layers, each layer contributes to the total torque production of the profile, ensuring a smooth profile. According to the results of the 3D FEM analysis, it is seen that the proposed multi-layer motor structure has high starting torque and low torque ripple properties. In the analysis carried out in the range of 3–15 Amperes, the torque ripple of the traditional FP-SRM varies between 31.99% and 38.19%, while the torque ripple of the proposed MFP-SRM only varies between 3.23% and 7.11%. © 2023 Taylor & Francis Group, LLC.
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    Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkuşu Y.S.; Bayhan N.; Tiryaki H.
    In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient ((Formula presented.)), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less (Formula presented.) emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data. © 2023 by the authors.
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    Investigation With Rule-Based Controller of Energy Consumption of Parallel Hybrid Vehicle Model in Different States of Charge
    (Istanbul University, 2024) Arıkuşu Y.S.; Bayhan N.; Tiryaki H.
    In this study, a parallel hybrid electric vehicle has been modeled and a new rule-based and battery-priority control method has been proposed, which will reduce fuel consumption and carbon emission values to minimum values. This control method is based on running the electric motor more and operating the internal combustion engine in the most efficient region. In the proposed control method, it is also ensured that the electric motor is operated as a generator. The control method is used in New European Driving Cycle (NEDC), ECE-15 (Urban Driving Cycle), and in Extra Urban Driving Cycle (EUDC) driving cycle conditions. In this study, two different simulation studies are achieved in accord with the critical state of charge (SOC) of the battery. The SOC value is selected 55% and 65% for its effect on fuel consumption in these driving cycles. According to the results, the parallel hybrid electric vehicle which has a 65% SOC value, gasoline efficiency becomes executed 35.7% inside the NEDC cycle, 25.3% in the EUDC cycle, and 52.3% in the ECE-15 cycle. Furthermore, for the parallel hybrid electric vehicle with a 55% SOC value, fuel efficiency is 29.3% in the ECE-15 driving cycle, 17.6% in the NEDC driving cycle, and 9.6% in the EUDC cycle. The proposed control approach yields the parallel hybrid vehicle's fuel usage and fuel efficiency. © 2024 Istanbul University. All rights reserved.