Fuzzy Based Stability Enhancement System for a Four-Motor-Wheel Electric Vehicle

The stability of a four motor-wheel drive electric vehicle is improved by independent control of wheel torques. An innovative Fuzzy Direct Yaw Control method together with a novel wheel slip controller is used to enhance the vehicle stability and safety. Also a new speed estimator is presented in this paper, which is used for slip estimation. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease to implement fuzzy controllers gives a practical solution for vehicle stability enhancement. INTRODUCTION Motor-wheels are the revolutionary new electric drive systems that can be housed in vehicle wheel assemblies. All-Wheel-Drive systems have been recognized as a break-through concept that will have a major impact on future electric and hybrid vehicle design. The drive system is versatile and can be configured for all types of electric vehicles including battery only, plug-in hybrid, autonomous hybrid and fuel cells. Motor-wheels permit packaging flexibility by eliminating the central drive motor and the associated transmission and driveline components, including the transmission, the differential, the universal joints and the drive shaft. Inwheel motors provide many advantages in manufacturing electric vehicles such as: • More space for the installation of batteries, or auxiliary power unit for fuel cells. • Better weight distribution. • Low floor possibility for urban buses due to elimination of the axle and differential. • Built-in 4WD capability. • More tractive force and braking regenerative power due to all-wheel-drive capabilities. The unequaled independent torques applied to the wheels provides another important aspect of such a vehicle. That is the ability of vehicle dynamic control to assist the driver with path correction, thus enhancing cornering and straight-line stability and providing enhanced safety. In fact, an electric vehicle with independent driven wheels provides another steering control input, i.e. the torque steering. Controlling of the yaw rate of a vehicle by utilizing this steering method is usually addressed as Direct Yaw-moment Control (DYC). It has been proved that DYC is more effective in enhancing vehicle stability than four wheel steering [1]. Actually, the yaw moment resulting from differential longitudinal tire force in the left and the right wheels is insignificantly influenced by lateral acceleration. On the contrary, the yaw moment generated by four-wheel steering decreases as the lateral acceleration increases [2]. In this paper, a control strategy for a driver assist stability system for a four in-wheel drive electric vehicle with a conventional front wheel steering is presented. The system comprises of a fuzzy logic controller in order to control the yaw rate. The disturbances in yaw moment are counter-balanced by a differential torque applied to the driven wheels of both sides. Another fuzzy controller is employed in order to prevent the wheels to enter saturation region due to the additional torque applied by the yaw controller. Fuzzy controllers have been already proved to have good performance in two-motor-wheel drive vehicles [3, 4]. VEHICLE MODELING VEHICLE MODEL A fourteen-degree of freedom model is used for the simulation purposes. Where, 6 degrees are devoted to the chassis motion and eight degrees are assigned to the wheels rotational and vertical movement. Figure 1 shows different coordinates that are used for the modeling purpose. Figure 1. Coordinate systems for vehicle modeling Where u, v and w are associated with longitudinal, lateral and vertical velocities and ψ, φ and θ denote the yaw, roll and pitch angles. Suspension Model – A model of the suspension system is shown in Figure 2. It is assumed that the suspension is independent for each wheel. The suspension model consists of a spring and damper. Parameters Ksi and Csi correspond to the spring stiffness and the damping factor. A virtual spring and damper assembly (corresponding to Kui and Cui) is considered for the tire elasticity. Zs is the height of the center of gravity of the sprung mass. Zui is assigned for the height of unsprung mass and Zri is due to road roughness. Figure 2. The model of suspension system Tire Model – The accuracy of the simulation results is predominantly determined by the accuracy of the tire model used. The desired simulation for the application herein concerned can be carried out by a steady state tire characteristics. In this paper the simulation is performed using the well known magic formula [5]. The longitudinal force is carried out, in this formulation, from the following formula: v x S C D F + ΒΦ = )) arctan( sin( (1) Where, ( ) )) ( arctan( ) )( 1 ( h h S B E S + Β + + Ε − = Φ λ λ In which λ is the wheel slip and other parameters depend on the normal load and factors, which are determined from experience. There are similar formulas for the lateral force and the self-aligning torque. Figure 3 shows typical longitudinal and lateral forces obtained from the magic formula. Figure 3. Typical tire characteristics at different slip angles DYNAMIC EQUATIONS – Considering the forces acting on the vehicle, as depicted in Figure 4, one can write the vehicle motion equations in horizontal plane as below: ax x x f y f x f y f x s t F F F F F F F pr q h M rv qw u M − + + − + − + + = − +