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001-es BibID:	BIBFORM118893
035-os BibID:	(Cikkazonosító)1116
Első szerző:	Almusawi, Husam Abdulkareem (mérnök)
Cím:	Development of a Volkswagen Jetta MK5 Hybrid Vehicle for Optimized System Efficiency Based on a Genetic Algorithm / Husam A. Neamah, Mohammed Dulaimi, Alaa Silavinia, Aminu Babangida, Péter Tamás Szemes
Dátum:	2024
ISSN:	1996-1073
Megjegyzések:	Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing overdependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model`s accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk drivetrain genetic algorithm (GA) internal combustion engine (ICE) 1.9 TDI PD engine 2.0 TDI CR diesel engine
Megjelenés:	Energies. - 17 : 5 (2024), p. 1-25. -
További szerzők:	Dulaimi, Mohammed Silavinia, Alaa Babangida, Aminu (1988-) (Informatics)(mérnök) Szemes Péter Tamás (1976-) (gépészmérnök, villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM105830
Első szerző:	Muhammad Maaruf
Cím:	Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones : Application to Quadrotors / Muhammad Maaruf, Aminu Babangida, Husam A. Almusawi, Peter Szemes Tamas
Dátum:	2022
ISSN:	2715-5072 2715-5056
Megjegyzések:	Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV).
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Quadrotor unmanned aerial vehicle backstepping control radial basis function neural network dead-zone nonlinear systems
Megjelenés:	Journal of Robotics and Control (JRC). - 3 : 6 (2022), p. 735-742. -
További szerzők:	Babangida, Aminu (1988-) (Informatics)(mérnök) Almusawi, Husam Abdulkareem (mérnök) Szemes Péter Tamás (1976-) (gépészmérnök, villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: