In this paper, we study methods of motion control for an electric vehicle (EV) with four independently driven in-wheel motors. First, we propose and simulate a novel robust dynamic yaw-moment control (DYC). DYC is a vehicle attitude control method that generates yaw from torque differences between the right and left wheels. The results of simulations, however, identify a problem with instability on slippery, low roads. To solve this problem, a new skid detection method is proposed that will be a part of traction control system (TCS) for each drive wheel. The experimental results show that this method can detect a skidding wheel, without any information on chassis velocity. Therefore, this method will be of great help during cornering or braking in a TCS. These methods will be integrated and tested in our new experimental EV.
In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS).
SUMMARYA novel algorithm for the dynamic driving/braking force distribution is proposed for electric vehicles (EV) with four in-wheel motors. In such EVs, the vehicle lateral motion can be controlled by a yaw moment, generated by the torque difference between wheels. This method is known as DYC (Direct Yaw moment Control) in ordinary engine vehicle engineering; however, the torque difference can be generated more directly with in-wheel motors. One problem of DYC is its instability on slippery roads, such as wet or snowy asphalt. To achieve high stability, the loads of wheels are preferably equal. The load on each wheel can be evaluated as the square root of the sum of squares of driving/braking force and side force. Therefore, the driving/braking forces, or motor torques, should be distributed depending on the side forces of the wheels, to minimize the load imbalance between wheels. The proposed algorithm can solve this optimization problem approximately with little calculation cost, and thus this method can be applied for real-time calculation within a control period. Approximate solutions obtained with the proposed method are evaluated by comparison with numerical solutions that require much calculation time. The difference between these solutions is shown to be negligible, indicating the effectiveness of the proposed method. © 2001 Scripta Technica, Electr Eng Jpn, 138(1): 7989, 2002
In this paper, we propose the optimal slip ratio estimation method based on Fuzzy Inference. One of the remarkable advantages of electric vehicle (EV) is the quick and precise torque response of electric motor, which realizes a novel traction control system (TCS). To prevent wheel skid phenomena, the optimal slip ratio control has been successfully developed. It maintains the slip ratio to the optimal value which gives the maximum driving force. The remaining problem is how to generate the optimal slip ratio command to the controller. First, we indicate that the effective estimation of the optimal slip ratio is difficult for a simple gradient method, which is one of the well-known optimization methods. Various knowledge obtained by experiments can be easily installed into Fuzzy Inference. Therefore, its estimation performance can be easily improved by accumulation of human experiences. This is a remarkable advantage in nonlinear estima tion of actual road-tire characteristics. The effectiveness of the proposed estimation and control methods is confirmed by numerical simulation.
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