Automotive Science and Engineering

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This paper introduces a technique that relates the coefficients of the Magic Formula tire model to the physical properties of the tire. For this purpose, the tire model is developed by ABAQUS commercial software. The output of this model for the lateral tire force is validated by available tire information and then used to identify the tire force properties. The Magic Formula coefficients are obtained from the validated model by using nonlinear least square curve fitting and Genetic Algorithm techniques. The loading and physical properties of the tire such as the internal pressure, vertical load and tire rim diameter are changed and tire lateral forces for each case are obtained. These values are then used to fit to the magic formula tire model and the coefficients for each case are derived. Results show the existing relationships between the Magic Formula coefficients and the loading and the physical properties of the tire. In order to investigate the effectiveness of the method, different parameter values are selected and the lateral force for each case are obtained by using the estimated coefficients as well as with the simulation and the results of the two methods are shown to be very close. This proves the effectiveness and the accuracy of the proposed method.

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In this paper, an adaptive cruise control system is designed that is controlled by a neural network model. This neural network model is trained with data resulting from the simulation of a multi-objective nonlinear predictive adaptive cruise control system. For this purpose, first, an adaptive cruise control system was designed using the concept of model predictive control based on a nonlinear model to maintain the desired speed of the driver, maintain a safe distance with the car in front, reducing fuel consumption and increasing ride comfort. Due to the time-consuming computations in predictive control systems and the consequent need for powerful and expensive hardware, it was decided to use the extracted data from the simulation of this designed cruise control system to train a neural network model and use this model to achieve control objectives instead of the predictive controller. Using the neural network model in the cruise control system, despite a significant reduction in computation time, the control objectives were well achieved, and in fact a combination of model predictive controller accuracy and neural network controller speed was used.

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In this paper, the optimization of the suspension system’s parameters is performed using a combined Taguchi and TOPSIS method, in order to improve the car handling and ride comfort. The car handling and ride comfort are two contradictory dynamic indices; therefore, to improve both car handling and ride comfort, there is a need for compromising between these two indices. For this purpose, the criteria affecting these two are first identified. The lateral acceleration and the body roll angle were used to evaluate the handling, and the RMS of vertical acceleration of the vehicle body was used to evaluate the ride comfort. The design factors including stiffness of springs and damping coefficient of dampers in the front and rear suspension system were also taken into account. On this basis, the results obtained from the vehicle’s motion in the DLC test were evaluated in the CarSim software. Then, the ideal tests were identified using the combined entropy and TOPSIS technique; this method has been proposed for managing the handling and ride comfort criteria. Finally, the optimal level of the suspension system’s factors was extracted using Taguchi method. It is evident from the results that, for different speeds, the body roll angle was improved up to 6.5%, and the RMS of the vertical acceleration of the vehicle body was optimized up to 4% to 7%.