Jamel Ghouili

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.

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.

A real time energy management for electrical vehicle using combination of rule-based and ECMS

The studies on energy management and optimization are still at an early stage. The energy management of a fuel cell (FC) hybrid vehicle supported with a storing device such as batteries or supercapacitors, is a challenge. Indeed, regardless of the followed route, the available power must be distributed among the various components, in order to minimize hydrogen consumption and increase their life expectancy. This article present a real-time energy management strategy for a fuel cell vehicle equipped with a battery. It is based on the rule-based method combined with the equivalent consumption minimization strategy ECMS. This strategy is developed and simulated by using a dynamic model of the vehicle developed in the Matlab/Simulink environment. Eventually, simulation results are provided to validate the strategy under various vehicle masses.

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.

Design and implementation of an Electric Differential for traction application

The use of an Electric Differential (ED) constitutes a technological advance in vehicle design along with the concept of more electric vehicles. EDs have the advantage of replacing loose and heavy mechanical differentials and transmissions with lighter and smaller electric motors directly coupled to the wheels via a single gear or an in-wheel motor. This paper deals then with an Electric Differential System (EDS) for an Electric Vehicle (EV) directly driven by dual induction motors in the rear wheels. A sensorless control technique is preferred to a position or speed encoder-based control one to reduce the overall cost and to improve the reliability. The EDS main feature is the robustness improvement against system uncertainties and road conditions. The EDS control performances are validated through experiments on a dSPACE-based test bench. The experimental results show that the proposed controller is able to track the speed reference and the curvature angle with good static and dynamic performances.

Detection of Electrical Arc Faults in a Distribution Network

In this paper, a new method for the detection of electrical arc faults is presented It is possible, by using a digital signal processor programmed with Matlab/Simulink, to create a system able to detect the presence of unwanted frequencies in the distribution network. Those frequencies are often generated by an electrical arc in the network.

Strategies Comparison for Optimization of Multi Objective Function in a Fuel Cell Electrical Vehicle

Fuel cell vehicles (FCVs) appear to be one of the viable solutions for the growing concerns for environmental protection and the fast rate of depletion of world oil supply. FCVs can become a viable alternative to the internal combustion engine only if they are able to meet certain reliability, safety, performance, and cost criteria. Improvement of efficiency of induction motor traction drives and optimal power/energy management methods for multi-source energy systems are important issues in FCV. In this paper, a model of FCV that includes fuel cell, battery, ultracapacitor and induction machine drives is presented and comparison of strategies for global optimization of FCV is presented