This paper, investigates the traction control of an electric vehicle (EV) that is equipped with two motor drives. A new yaw moment control scheme via an Adaptive Neuro-Funy Inference System (ANFIS) is proposed. The ANFIS is an attractive compromise between the adaptability of a neural network and interpretability of a fuzzy inference system. In a 2WD EV, because of independent torque control of the motors, simultaneous torque control of several drive motors is the main objective. Due to nonlinear vehicle model and parameters variations, an intelligent controller on base .ANFIS, using emotional learning is presented and is used for tracking the reference yaw rate. The designed controller calculates the reference torque of motor drives. Mechanical differential is eliminated and its operation is done electronically. Because of large magnitude of control effort, assistance brake control also is applied to limit the control effort value. Various.simulations with a full nonlinear and uncertain 7-DOF vehicle model show that the proposed controller enhances the vehicle performance and stability.
One of the most important issues in power systems is the energy. Due to the fossil fuel downfall, global warming and greenhouse effect which are important environmental effects of fossil fuel burning to produce energy and energy price variation, energy saving, and recycling in industry especially in electrical vehicles and electrical trains are very important and vital. In this paper, the main aim is using a battery and a supercapacitor for train kinetic energy recovery in the braking mode and grid demand reduction in the acceleration mode. In this regard, a new DC-DC converter is proposed. An algorithm control that manages energy flow between the battery and super-capaciror (SC) in the proposed converter is designed. The control method is based on the sliding mode. Because of immeasurable internal and initial voltage of the supercapacitor, an observer extracts the internal voltage of the supercapacitor. In addition, acceleration and braking conditions of electrical trains and energy storage systems are simulated in MATLAB Simulink, and effectiveness of the proposed converter, energy management algorithm, and control system in different phases has been proven.
In recent years, urban rail systems have developed drastically. In these systems, when induction electrical machine suddenly brakes, a great package of energy is produced. This package of energy can be stored in energy storage devices such as battery, ultra-capacitor and flywheel. In this paper, an electrical topology is proposed to absorb regenerative braking energy and to store it in ultracapacitor and battery. Ultra-capacitor can to deliver the stored energy to DC grid and to charge the battery for auxiliary applications such as lighting and cooling systems. The proposed system is modeled based on large signal averaged modeling, which leads to the simplicity of calculations. The control system is based on Lyapunov stability theorem which guarantees system stability. Also, an energy management algorithm is proposed to control energy under braking and steady-state conditions. Finally, the simulation results validate the effectiveness of the proposed control and energy management system.
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