Automotive Science and Engineering

Intelligent Control System Design for Car Following Maneuver Based on the Driver’s Instantaneous Behavior

A. Khodayari

Due to the increasing demand for traveling in public transportation systems and increasing traffic of vehicles, nowadays vehicles are getting to be intelligent to increase safety, reduce the probability of accident and also financial costs. Therefore, today, most vehicles are equipped with multiple safety control and vehicle navigation systems. In the process of developing such systems, simulation has become a cost-effective chance for the fast evolution of computational modeling techniques. The most popular microscopic traffic flow model is car following models which are increasingly being used by transportation experts to evaluate new Intelligent Transportation System (ITS) applications. The control of car following is essential to its safety and its operational efficiency. This paper presents a car-following control system that was developed using a fuzzy model predictive control (FMPC). This system was used to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU) and was developed based on a new idea to calculate and estimate the instantaneous reaction of a DVU. At the end, for experimental evaluation, the FMPC system was used along with a human driver in a driving simulator. The results showed that the FMPC has better performance in keeping the safe distance in comparison with real data of human drivers behaviors. The proposed model can be recruited in driver assistant devices, safe distance keeping observers, collision prevention systems and other ITS applications.

Prediction of Driver’s Accelerating Behavior in the Stop and Go Maneuvers Using Genetic Algorithm-Artificial Neural Network Hybrid Intelligence

J. Marzbanrad

Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption. Although there have been many attempts to model stop and go maneuver via traffic models, but predicting the future vehicle's acceleration in steps ahead has not been studied much in this models. The main contribution of this paper is in designing integrated genetic algorithm-artificial neural network (GA-ANN) which is a soft computing method to simulate and predict the future acceleration of the stop and go maneuver for different steps ahead based on US federal highway administration’s NGSIM dataset in real traffic flow. The results of this study are compared with two methods, back propagation based artificial neural network model (BP-ANN) and standard time series forecasting approach called ARX model. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination or R-squared (R2) are utilized as three criteria for evaluating predictions accuracy. The results showed the effectiveness of the proposed approach for prediction of driving acceleration time series. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and driver assistant systems (DAS) such as adaptive cruise control (ACC) and etc. The outcomes of this study can be used for the automotive industries who have been seeking accurate and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon, which can be used for designing efficient predictive controllers based on human behaviors.

A comparative analysis of two neural network predictions for performance and emissions in a biodiesel fuelled diesel Engine

S. Jafarmadar

An Efficient Numerical Scheme for Evaluating the Rolling Resistance of a Pneumatic Tire

H. Golbakhshi

The viscoelastic effect of rubber material on creation of rolling resistance is responsible for 10-33% dissipation of supplied power at the tire/road interaction surface. So, evaluating this kind of loss is very essential in any analysis concerned with energy saving. The transient dynamic analysis for including the rolling effects of the tire requires long CPU time and the obtained results are prone to considerable numerical oscillations. By adding the equivalent loads to static interaction of tire with the road, an efficient 3D FE analysis is presented for evaluating the dissipated energy of a rolling tire. The results closely match the related experimental and numerical investigations.

Hybrid energy storage optimal sizing for an e-bike

M. Masih-Tehrani

The Energy Storage System (ESS) is an expensive component of an E-bike. The idea of Hybrid Energy Storage System (HESS), a combination between battery and Ultra-Capacitor (UC), can moderate the cost of E-bike ESS. In this paper, a cost function is developed to use for optimal sizing of a HESS. This cost function is consisted of the HESS (battery, UC and DC/DC converter) cost and the cost of battery replacements during 10 years. The battery lifetime and riding pattern limit the life span of ESS. The “Portuguese standard NP EN 1986-1” riding pattern is used in this research. The Genetic Algorithm (GA) is used to solve the optimization problem. The results show that the cost and weight of HESS are clearly better than optimally sized battery ESS.

Thermo-mechanical analysis of diesel engines cylinder heads using a two-layer viscoelasticity model with considering viscosity effects

H. Ashuri

Loading conditions and complex geometry have led the cylinder heads to become the most challenging parts of diesel engines. One of the most important durability problems in diesel engines is due to the cracks valves bridge area. The purpose of this study is a thermo-mechanical analysis of cylinder heads of diesel engines using a two-layer viscoelasticity model. The results of the thermo-mechanical analysis indicated that the maximum temperature and stress occurred in the valves bridge. The results of the finite element analysis correspond with the experimental tests, carried out by researchers, and illustrated the cylinder heads cracked in this region. The results of the thermo-mechanical analysis showed that when the engine is running the stress in the region is compressive caused by the thermal loading and combustion pressure. When the engine shut off the compressive stress turned into the tensile stress because of assembly loads. The valves bridge was under the cyclic tensile and compressive stress and then is under low cycle fatigue. After several cycles the fatigue cracks will appear in this region. The lifetime of this part can be determined through finite element analysis instead of experimental tests. Viscous strain was more than the plastic strain which is not negligible.

Using PCA with LVQ, RBF, MLP, SOM and Continuous Wavelet Transform for Fault Diagnosis of Gearboxes

Heidari

A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction next, the continuous wavelet coefficients (CWC) are evaluated for some different scales. As a new method, the optimal range of wavelet scales is selected based on the maximum energy to Shannon entropy ratio criteria and consequently feature vectors are reduced. In addition, energy and Shannon entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. To prevent the curse of dimensionality problem, the principal component analysis applies to this set of features. Finally, the gearbox faults are classified using these statistical features as input to machine learning techniques. Four artificial neural networks are used for faults classifications. The test result showed that the MLP identified the fault categories of gearbox more accurately for both real wavelet and complex wavelet and has a better diagnosis performance as compared to the RBF, LVQ and SOM.