Automotive Science and Engineering

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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%.

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Pure electric vehicles are increasingly important for reducing transportation emissions, improving energy efficiency, and supporting sustainable mobility. However, their performance depends strongly on battery health, energy consumption, and vehicle dynamic behavior. This study proposes a genetic-algorithm-optimized fuzzy active suspension controller for a pure electric vehicle by simultaneously considering ride comfort, suspension travel, battery state of charge, and battery degradation. An integrated EV–active suspension simulation framework is developed by combining the electric powertrain, battery aging model, and full-car active suspension model. The fuzzy controller membership functions are optimized using a genetic algorithm and evaluated under UDDS, NEDC, and WLTP Class 3 driving cycles. The objective function combines weighted ride comfort, front and rear suspension travel, final SOC, and battery capacity loss. The results show that the optimized controller improves ride comfort by 15.32% in NEDC, 2.18% in UDDS, and 2.36% in WLTP Class 3. Battery aging is also reduced by 3.51%, 5.17%, and 4.58% under the same cycles, respectively. Overall, the proposed GA-based fuzzy controller provides an effective compromise between passenger comfort, suspension performance, actuator energy demand, and battery health preservation.