Rachid Abdessemed

A Loss-Minimization DTC Scheme for EV Induction Motors

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EVs motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

A loss-minimization DTC scheme for EV induction motors

This paper proposes a strategy to minimize the losses of an induction motor propelling and electric vehicle (EV). The proposed control strategy, based on a direct flux and torque control (DTC) scheme, utilizes the stator flux as control variable and the flux level is selected in accordance with torque demand of the EV to achieve the efficiency optimized drive performance. Moreover, among EVs motor electric propulsion features; the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics is taken into-account. Simulations tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach.

Self-Tuning Fuzzy Logic Controller for a Dual Star Induction Machine

This paper proposes a simple but robust self-tuning fuzzy logic controller for the speed regulation of a dual star induction machine based on indirect field oriented control. For feed the two star of this machine, two voltage source inverters based on sinus-triangular pulse-width modulation techniques are introduced. The simulation results show the robustness and good performance of the proposed controller.

SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears

This paper proposes a sensorless direct torque control (SDTC) neural network traction control approach of an electric vehicle (EV) without differential gears (electrical differential system). The EV is in this case propelled by two induction motor (one for each wheel). Indeed, using two electric in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed that is different from wheels speed characterized by slip in the driving mode, as an input. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed SDTC neural network approach operates satisfactorily.

Comparative Analysis of Control Techniques for Efficiency Improvement in Electric Vehicles

This paper presents system analysis, modeling and simulation of an electric vehicle (EV) with three different control strategies: field oriented control (FOC), direct torque control (DTC), and DTC using space vector modulation (DTC- SVM). The objective is to assess the control strategy impact on the EV efficiency taking into account the vehicle dynamics. Indeed, among EV motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. Simulation tests have been carried out on a 37-kW EV that consists in an induction motor with a three-level IGBT inverter. Preliminary results seem to indicate that the DTC-SVM scheme is the best candidate.

Comparative analysis of estimation techniques of SFOC induction motor for electric vehicles

This paper presents system analysis, modeling and simulation of an electric vehicle with different sensorless control techniques. Indeed, sensorless control is considered to be a lower cost alternative than the position or speed encoder-based control of induction motors for an electric vehicle. Two popular sensorless control methods, namely, the Luenberger observer and the Kalman filter methods are compared regarding speed and torque control characteristics. They are also compared against the well-known model reference adaptive system. Simulations on a test vehicle propelled by 37-kW induction motor lead to very interesting comparison results.

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Gears

This paper presents system analysis, modeling and simulation of an EV with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability in case of no differential gears. Using two electrics in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed, which is different from wheels speed characterized by slip in the driving mode, an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

Direct torque and reactive power control of Grid Connected Doubly Fed Induction Generator for the wind energy conversion

The purpose of this paper is to show the performance of the direct torque and reactive power control technique (DTRPC), combined with fuzzy logic algorithms, applied to a doubly fed induction generator (DFIG) driven by a wind turbine, taking into account the variation of wind allowing the three modes (Sub, Super and synchronous) operation, with a particular focus on synchronous mode, well as the possibility of management and the active-reactive power control. Especially, operation as a local reactive power compensator. This analysis is treated by taking account some constraints that reflect the real operation of a wind driven, such as the pursuit of the references and the influence of the system physical state. The captured wind energy is optimized to extract the maximum power using the MPPT algorithm. The studied system control is tested and validated through numerical simulation. The obtained results show the effectiveness of the control strategy leading to best performances.

Adaptive Backstepping Speed Control for a Permanent Magnet Synchronous Motor

The application of the backstepping feedback design technique to the speed control of a PMSM is investigated. Using backstepping method, both feedback laws and Lyapunov based designs are applied in the controller design. The proposed stabilizing feedback law for the PMSM is shown to be globally asymptotically stable in the context of Lyapunov theory. It takes into account nonlinearities of the system and uncertainties of the parameters. To validate the contribution of this control, the performances are analyzed and highlighted by simulation results.