Automotive Science and Engineering

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This paper presents the prediction of vehicle's velocity time series using neural networks. For this purpose, driving data is firstly collected in real world traffic conditions in the city of Tehran using advance vehicle location devices installed on private cars. A multi-layer perceptron network is then designed for driving time series forecasting. In addition, the results of this study are compared with the auto regressive (AR) method. The least root mean square error (RMSE) and median absolute percentage error (MDAPE) are utilized as two criteria for evaluation of predictions accuracy. The results demonstrate the effectiveness of the proposed approach for prediction of driving data time series.

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Nowadays, due to increasing the complexity of IC engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. Accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. In this paper four network based modeling methods are used and compared to model the behavior of an IC engine: neural networks model (NN), group method of data handling model (GMDH), a hybrid NN and GMDH model (NN-GMDH), and a GMDH model whose structure is determined by genetic algorithm (Genetic-GMDH). The inputs are engine speed, throttle angle, and intake valve opening and closing timing, and the output is the engine brake torque. Results show that NN has the best prediction capability and Genetic-GMDH model has the most flexible and simplest structure and relatively good prediction ability.

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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.