الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
In the past few decades, the rapid development of hybrid vehicles technologies has a vital effect on the control system structure. According to forecasts, the rise in fuel prices will continue until at least 2023, putting an added burden on the global economy. As a result, consumers are increasingly purchasing vehicles that conserve energy and minimize environmental pollution, such as electric vehicles (EVs) and hybrid electric vehicles (HEVs). Hence, research initiatives have been carried out to improve the intelligence of these cars in terms of energy conservation, dealing with uncertainties, and the complexity of driving situations. Plug-in hybrid electric vehicles (PHEVs) have greater freedom by adding multiple sources such as a diesel engine, electric motor with battery, and fuel cell to get the required power. Moreover, generating energy during deacceleration, saving it, and improving fuel economy to reduce exhaust emissions. With a variety of different sources of energy in PHEV, energy management strategies (EMSs) are developed to achieve intelligent coordination management of several energy sources to obtain the best fuel economy, monitoring the engine to work at the optimal region, increasing the battery life, decreasing CO2 emissions and overcoming the uncertainties and the complexity of driving conditions. This thesis presents model for various types of hybrid vehicles. Online intelligent controller using adaptive neuro fuzzy controller for managing power is presented. A proposed technique is presented for saving more energy and increasing battery life. This technique depends on long period optimization and short period controller. In the long-term power management, the motor and engine torque are optimized using improved generalized particle swarm optimization (IGPSO) and chaotic IGPSO. In order to reduce the computation time, a five-mode rule-based control system is employed, where the optimization techniques estimate the optimal values for the motor and engine torques in a hybrid mode, which manages the power between the motor and engine in accordance with a cost function. This cost function reduces fuel usage as well as the drawn current from the battery, taking into account the process of the battery aging. Moreover, the short-term controller is designed using fuzzy controller an interval type-2 fuzzy Takagi-Sugeno-Kang (IT2TSK) algorithms which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is achieved. The proposed strategy reduces the energy consumption compared to other strategies such as improved multi-objective PSO and generalized PSO algorithm. The simulation results for the engine, motor, and battery are performed using real data to demonstrate the viability and effectiveness of the proposed approach with comparative results.