Control of pre and post transmission parallel hybrid vehicles with fuzzy logic method and comparison with other power systems

Abstract

 Hybrid vehicle was controlled by fuzzy logic method  Creation of pre and post transmission parallel hybrid, serial hybrid, conventional vehicle models  Determination of the least fuel-consuming vehicle structure under NEDC, US06 and FTP 75 driving cycle conditions Figure A. Pre-transmission parallel hybrid vehicle diagram, operating point of ICE and electric motor under FTP 75 driving cycle Purpose: In this study, it is aimed to determine the vehicle structure with minimum average fuel consumption under different driving cycle conditions by controlling the pre-transmission, post-transmission parallel hybrid power systems with fuzzy logic method and comparing them with serial hybrid and conventional vehicle structures. Theory and Methods: Pre-transmission, post-transmission parallel hybrid, serial hybrid and conventional vehicle structures are modeled in MATLAB / Simulink environment using mathematical equations of systems and subsystems. Transfer functions representing the power systems of the pre-transmission, the post-transmission parallel hybrid, serial hybrid and conventional vehicles are derived. In order to evaluate all vehicle structures under the same conditions, the same system and subsystems were used on all vehicle structures. While the gas and brake pedal control is provided with PID in all vehicle structures, the torque distribution of the parallel hybrid vehicle is provided by the fuzzy logic control system. All vehicle models have dual clutch gearbox and lithium-ion battery pack. The battery model was created with the equivalent circuit Rint method. Results: The lowest average fuel consumption with the values of 5.73 L in the US 06 driving cycle, 4.00 in the FTP 75 driving cycle, 3,96 in the NEDC driving cycle and 4.18 L / 100 km in the EUDC driving cycle of the pretransmission parallel hybrid vehicle structure controlled by the fuzzy logic method. It is concluded that it has. In the ECE 15 driving cycle conditions, it was obtained that the series hybrid vehicle structure has the lowest average fuel consumption with a value of 3.62 L / 100 km. In the EUDC driving cycle conditions, which represent the intercity road speed conditions, the serial hybrid vehicle was found to cause 1.47% fuel consumption compared to the conventional vehicle. Conclusion: It is concluded that in all driving cycles conditions, the pre-transmission parallel hybrid vehicle structure consumes less fuel than the post-transmission parallel hybrid vehicle structure. The pre-transmission parallel hybrid vehicle structure controlled by fuzzy logic under NEDC, FTP75 and EUDC driving cycle conditions has been determined to have the lowest average fuel consumption. Under the conditions of the ECE15 driving cycle, which represents urban use, the series hybrid vehicle structure was found to consume the least amount of fuel. The conventional vehicle consumed more fuel under all driving conditions than the parallel hybrid vehicle. Conventional vehicle has become advantageous only in EUDC driving cycle conditions compared to serial hy...

Control and Optimization of Pre-Transmission Parallel Hybrid Vehicle with Fuzzy Logic Method and Comparison with Conventional Rule Based Control Strategy

“…Most of the vehicles that cause high energy consumption and the release of high pollutant gas emissions are conventional vehicles with low efficiency values, powered only by the internal combustion engine [1]. The issue of increasing energy efficiency and emission standards does not fall from the agenda due to the expectation that the harmful gases emitted by conventional vehicles with fossil fuels to the environment and the expectation that most of the fossil fuels will come to an exhaustion in the near future [2][3][4][5]. Hybrid electric vehicle structure was introduced by combining the advantages of electric vehicles and conventional vehicles and minimizing the disadvantages [6,7].…”

Bulanık Ters Model Kullanılarak Doğru Akım Motor Sürücüsü için Referans Model Temelli Uyarlanabilir Bulanık Denetleyici

“…Uyarlamalı bulanık denetleyicide ise gerekli bulanık kurallarını otomatik olarak sistem verilerinden türetmek için sinir ağları, evrimsel algoritma gibi yapay zeka yöntemlerin bir veya birkaçının beraber kullanıldığı çalışmalar bulunmaktadır [11,12]. Bulanık mantık ve bulanık kurallara dayanan bulanık denetleyiciler günümüzde birçok uygulamada yaygın olarak kullanılmaktadır [13][14][15]. Bulanık mantık temelli denetleyicilerin bilinen avantajlarına rağmen bir analitik tasarım yönteminin eksikliği yüzünden bunların belli bir sistem geliştirilmesi halen deneme ve yanılma yoluyla olmaktadır.…”

Investigation on Different Driving Cycle and Scenarios Considering the Autonomous Electric Vehicles

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