ABSTRACT: In this paper a neuro-fuzzy-sliding mode control (NFSMC) with extended state observer (ESO) technique is designed to ensure the traction of an electric vehicle with two separate permanent magnet synchronous motor (PMSM). Each PMSM systems ( source-converter-motor) are coupled to an electronic differential (ED) in order to compensate the tendencies of direction of the vehicle and maintain a steady speed by adjusting the difference in speed of each motor-wheel according to the direction in the case of a turn. To ensure the control and performance of the vehicle a hybrid control scheme employs two types of controllers , neuro-fuzzy sliding mode control on the direct current loop and disturbance rejection control laws (ESO) on the Speed loop ,and quadrature current loop of the PMSM by taking into account the dynamic of the vehicle. Simulation under Matlab/Simulink to evaluate the efficiency and robustness of the proposed control method and ED on the closed loop system. INTRODUCTION The The number of vehicles in circulation continues to increase, and even though new engines are becoming less polluting, pollution problems are increasingly critical, especially for the greenhouse gases responsible for global warming of the planet. Currently, the most effective way to combat these emissions is the implementation of increasingly restrictive standards for new vehicles. Despite the technological advances, the limits imposed by the standards will be too restrictive to be respected by the thermal engines, in more or less long term. These restrictive standards are forcing automakers to introduce new technologies that will make vehicles less polluting. Electric vehicles have emerged, by definition an electric vehicle is a vehicle whose propulsion is provided by an engine operating exclusively with electrical energy. The electric car is advanced by all players in the automotive field as one of the cleanest and greenest transportation solutions. It could indeed be an alternative to this alarming pollution, especially since the road transport sector emits more pollutants into the atmosphere than the industry sector. However, despite the extensive research on the power train and batteries, the electric vehicle is still in the experimental stage and is subject to modification or improvement. Like anything else, cars adopting this type of consumption have advantages as well as disadvantages. The greatest strength of this class of cars lies in the electric nature of their engine. At the same time for technical reasons because the electric motors are more flexible than the thermal engines (high torque at low speed). Much research has been done on electric vehicles in particular the control of its traction chain, which in turn translates into the control of these engines wheels. The performance-dependent operation of the engine- wheel assembly system is characterized by smooth rotation throughout the engine speed range, total torque control at zero speed, and rapid acceleration and deceleration. On a classic vehicle the drive wheels are connected by half-shafts controlled by a bevel gear. On each of the wheels there is a torque equal to half of that provided by the return angle. Because of the rigid connection formed with the drive shaft, the two drive wheels rotate at the same angular speed, which has no particular disadvantage in walking in a straight line [5] [6]. The path of the inner wheel is reduced compared to that of the outer wheel; these two distances to be performed in the same time interval, it is necessary that the angular velocity of the two wheels is different. If the wheels are not driving, no problem, otherwise it is necessary to interpose a differential mechanism allowing the wheels to rotate at different speeds. there is a system in conventional vehicles called a mechanical differential that ensures the difference in speeds while ensuring torque for the two driving wheels [7]. In this work we will replace the mechanical differential with an electronic differential in order to reduce the drive line components, thus improving the overall reliability and efficiency. This option will also reduce the drive line weight since mechanical differential and gear reduction are not used. However, one of the main issues in the design of these EVs (without mechanical differential) is to ensure vehicle stability in particular while cornering or under slippery road conditions [8] . This calls for a specific traction control system . The control of the traction effort transmitted by each wheel is at the base of the command strategies aiming to improve the stability of the vehicle. Each wheel is controlled independently by using an electric motorization. in this context a Permanent magnet synchronous motor has been adopted as the electric vehicle (EV) propulsion. Permanent magnet synchronous motors (PMSMs) have extensive industrial applications including electric vehicles, robotics, and wind energy conversion systems[1],[2],[3]. This can be attributed to desirable performance characteristics including high power density, high torque to weight ratio, high reliability and high efficiency [1]. Many control techniques for PMSMs have been developed in literature studies. While the PI controller is still a popular choice due to its simplicity and ease such as a neuro-fuzzy-sliding mode control (RNFSMC) with extended state observer (ESO) to ensure the traction of the electric vehicle. The FIS forms are a useful computing framework based on the concepts of fuzzy set theory. neural network . Based on the ESO. . Vehicle dynamics In this work the proposed control strategy takes into account the dynamic vehicle. etc. control theories. more and more advanced control methods are introduced to the PMSM control problem. robust control [10]. fuzzy if–then rules and fuzzy reasoning. and propelling the vehicle forward. and fuzzy control [13]. robustness. This observer can estimate both the states and the lumped disturbances.g. etc. accuracy. Finally we terminate by a the conclusion is in section 5 ELECTRIC VEHICLE TRACTION SYSTEM This section is dedicated to the dynamics of the electric vehicle and the different components on board the vehicle and their equations models. electric motor model). a robust control for the whole system is developed to improve the adaptation of the closed-loop system. adaptive control [9]. The hybrid neuro-fuzzy sliding mode control with extended state observer [16] is one of the most common controls associated several techniques: fuzzy control [17] and sliding mode control [18]. combining the extended state observer (ESO) with the adaptive neuro-fuzzy inference system technique together. SMC has found extensive applications in the areas of power electronics and electric machines [14]..[23] regards the lumped disturbances of the system. but for the current electric vehicles. Consequently. So. the transmission efficiency and its inertia. Electronic differential . In this work. Several types of batteries can be distinguished.[20] is a FIS implemented in the framework of an adaptive fuzzy neural network. The reminder of this paper is organized as follows: section 2 reviews the principle components of the traction system and their model equations (vehicle dynamic. and disturbances rejection laws to exploit the advantages of all the techniques to limit the disadvantages of regulation by conventional adjustment algorithms and to improve the performance of the controller system (stability. lead-acid and nickel-cadmium batteries are frequently used .. it does not take disturbances and uncertainties into account. Usually. neural network control [12]. a control method was proposed to reduce or completely eliminate the chattering. so the first step in vehicle performance modeling is to write an electric force model. as a new state of the system. speed. which originated from the interaction between parasitic dynamics and high frequency switching control [15]. which consists of internal dynamics and external disturbances.. sliding mode control [11]. power electronics. chattering phenomenon.of implementation. e. The gear is modeled by the gear ratio i . The elementary equation describing the longitudinal dynamics of a vehicle in the road is in the following form: The speed gear ensures the transmission of the motor torque to the driving wheels. Sliding mode control is a popular control due to its ability to reject internal parameter variations and external disturbances. the parameters of the FIS should be determined optimally [21]. It combines the explicit knowledge representation of a FIS with the learning power of ANNs. This force must overcome the road load and accelerate the vehicle [24]. SMC has its own disadvantage. Section 4 a simulation results verify the validity of the proposed technique of control. SMC has been widely and successfully applied into the position and velocity control of PMSM. i. This is the force transmitted to the ground through the drive wheels. However. the transformation of human knowledge into a fuzzy system (in the form of rules and membership functions) does not give the target response accurately. Section 3 shows the theory of control ( sliding mode control and Neuro-fuzzy-sliding mode control with disturbance rejection law model ) to remedy the chattering phenomena. Extended state observer (ESO) [22]. a feed forward compensation for the disturbances can also be employed in the control design..e. These methods can improve the performance of the electric vehicle based on the PMSM from different aspects. Due to the development of digital signal processor technology. leading to poor performance [4]. lithium-ion.). The ANFIS [19]. In order to avoid the phenomenon. If the vehicle is turning left.. Since the control structure employed here is of cascade control loops for the speed regulation problem. the control vdref is imposed by the current regulation idref Direct axis control design The resulting error will be corrected by a regulator operating in the sliding mode and the surface of this control is given by: s1 idref id (18) Considering the expression of the current id deducted in the equation system Eq. The electronic differential however acts immediately on the two motors reducing the speed of the inner wheel and increases the speed of the outer wheel.J.Slotine to determine the sliding surface : n : Relative degree. ESO regards the lumped disturbances of system.6 . The principe of extended state observer method Different kinds of disturbances appears in real applications of electric vehicle based on PMSM motors. i. chattering phenomenon. It can .[30]. If the vehicle is turning right. For simplicity. Since the two rear wheels are directly driven by two separate motors. the right wheel speed is increased and the left wheel speed remains equal to the common reference speed r _ ref [8].e. When the vehicle begins a curve. and external disturbance. the driver imposes a steering angle to the wheels. and in a curve trajectory the difference between the two wheel velocities ensure the vehicle trajectory over the curve. Among many FIS models. in which a circle indicates a fixed node. See Figure 3. respectively. friction forces and unmodeled dynamics. we consider two inputs x. Three surface control strategy We take the general equation Proposed by J. back electromotive forces. like load disturbances. It may happen that the control as a function of the system state switches at high frequency. and built-in optimal and adaptive techniques [34]. Modeling of The Electronic Differential The proposed archictecture propulsion system allows one to develop an electronic differential to ensure that the over astraight trajectory of the two wheel drives roll exactly at the same velocity. equal to the number of times it derives the output for the command to appear. y and one output z. as a new state of system. the structure comprises a speed control loop which generates the current reference iqref which imposes the control vqref . like parameters variation. which originated from the interaction between parasitic dynamics and high frequency switching control. SMC has its own disadvantage. Sliding mode control of the electric traction system using the principle of the cascade control method. d the distance between the wheels of the same axle and r _ L and r _ R the angular speeds of the left and right wheel drives. This observer is one order more than the usual state observer. Adaptif Neuro Fuzzy Sliding control algorithm is employed. For the d-axis current loop. the steering angle. the derivative of the surface becomes: A typical architecture of an ANFIS (Adaptive Neuro Fuzzy Inference System) is shown in Fig.[31]. For the speed loop and q-axis current loop. this motion is called sliding mode. torque ripples. These disturbances may degrade the performance of the electric vehicle system if the controller does not have enough ability to reject them. including internal disturbance of the motor. several control methods were proposed in the literature. Figure 3(b) shows the vehicle structure describing a curve. two ESOs are introduced to estimate the different disturbances in each loop respectively. the left wheel speed is increased and the right wheel speed remains equal to the common reference speed r _ ref . which consists of internal dynamics and external disturbances. (6). where L represents the wheelbase. the Sugeno fuzzy model is the most widely applied one for its high interpretability and computational efficiency. In order to avoid the phenomenon. the control scheme includes a speed loop and two current loops. this helps the tyres from losing traction in turns. whereas a square indicates an adaptive node. Design of sliding mode controller Sliding modes is phenomenon may appear in a dynamic system governed by ordinary differential equations with discontinuous right-hand sides. the torque returns to its initial value. The electronic differential subsequently acts to equalize the speed of the drive motors. In this case. The EV traction drive system uses two separate PMSM back drive based wheels. e. an ESO-based controller is designed for the q-axis current loop. At t= 180 s the vehicle approaches a left turn. Based on the ESO. as it is shown in Figure 9. Current controller design It is well known that the current loops have parameters variations. a feed-forward compensation for the disturbances can also be employed in the control design. The disturbances do not affect the performances of the driving motors. accelerations. inaccurate back-electromotive force (EMF) model. The results obtained by simulation show that this structure allows the design and implementation of an electronic differential and ensures good dynamic and static performances. a neuro-fuzzy-sliding mode control (NFSMC) with extended state observer (ESO) technology is used to realize asymptotic tracking of speed trajectory and stator currents and guarantee stability of whole the field-oriented PMSM drive system. it can be seen that the electronic differential acts immediately on both electric motors by lowering the speed of the left wheel and. The speed difference of the driving wheels is shown in figure 8.. thus improving the overall reliability and efficiency since mechanical differential and gear reduction are not used. briefly becoming negative. Once the speed of the right wheel is stabilized . The measurements are performed while engine is hot. thus the driver applies an inverse steering angle to the front wheels. variations of stator resistance. Referring to the figure 8 and figure 9. At t= 67 s the vehicle exits the curved section of road. These parameter variations may primarily degrade the control performance [36]. The proposed ED has been developed to handle the EV stability while cornering or under slippery road conditions. cruising and decelerations. after a standard warming procedure. the vehicle turn right at t= 62 s. in essence. It simulates both urban and motorway cycle. The equation of q-axis current loop is written as follows: It should be noted that the simulation run under a Japanese urban driving cycle 11 mode 4 that shall be requested by the driver which shows wheel speed during steering for 0 < t < 481 sec. In order to eliminate unfavourable effects caused by these factors. decelerations. The 11 mode 4 Japanese cycle is being used for emissions and fuel consumption certification in Japan. .It is obvious that the electric vehicle operates satisfactorily according to the complicated series of accelerations. The ED system will reduce the drive line components. During this cycle the vehicle approaches a right turn at 62 (positive steering angle) s and a left turn at 180 s (negative steering angle) . including idling.g. This working phase can be exploited for energy recuperation. and frequent stops. Negative torque is. in compensation the torque of the right wheel decreases. This speed is illustrated in Figure 8. For that purpose. braking mode. The paper shows that the electronic differential controls the driving wheels speeds with high accuracy either in flat roads or curved ones. CONCLUSION This paper has dealt with an ED based EV. The variation of phase currents are shown in Figure 10. unlike that on the right.estimate both the states and the lumped disturbances.