Automotive Science and Engineering
Volume:3 Issue: 4, Autumn 2013

By new advancements in vehicle manufacturing vehicle quality evaluation and assurance has become a more critical issue. In present work, the vibration transfer path analysis and vibration path ranking of a car interior has been performed. The method is similar to classical multilevel TPA methods but has distinct differences. The method is named VIVS which stands for Vehicle Interior Vibration Simulation. Performance of some tests like chassis dyno test, virtual mass function test and body transfer function test are required in this approach. The accelerations on both sides of the engine mounts are measured on chassis dyno by which the virtual mass and body transfer functions are measured at engine mounts. Using the concept of multilevel TPA, the vibration share from each path is calculated. The overall vibration magnitude at target point is calculated by summing the shares. Path ranking can be done by having the share of each path from overall vibration magnitude. Using this method on a sample vehicle, some modification has been proposed to decrease the vibration at target point, and the side effect of the modifications on the powertrain dynamic behavior has been evaluated. The proposed method needs less analysis time than classical TPA methods and its ability in optimization of vibration magnitude at target points is proven.

In this study, an extended stress-based forming limit diagram (FLD) for prediction of necking based on the Marciniak and Kucznski (M-K) model is represented and applied in tube hydroforming. The bulge forming of a straight tube is simulated by finite element method and verified with published experimental data. This adaptive simulation technique is based on the ability to detect the onset and growth of defects (e.g., bursting and wrinkling) and to promptly readjust the loading paths. Thus, a suitable load path is determined by applying Adaptive Simulation Method in ANSYS Parametric Design Language (APDL).

In the present study, a model of a large Lithium Polymer (Li-Po) battery for use in the simulation of Hybrid Electric Vehicles (HEVs) is developed. To attain this goal, an Equivalent Circuit (EC) consisting of a series resistor and two RC parallel networks is considered. The accuracy and the response time of the model for use in an HEV simulator are studied. The battery parameters identification and model validation tests are performed in low current with a good accuracy. Similar test process is implemented in high current for another cell and the simulation is verified with experimental results. The validation tests confirm the accuracy of the model for use in HEV simulator. Finally, the battery model is used to model a Vehicle, Fuel and Environment Research Institute (VFERI) hybrid electric city bus using ADVISOR software and its compatibility with other components of the vehicle simulator are demonstrated in a drive cycle test.

Kinematic synthesis of a trailing six-member mechanism has been carried out to achieve five precision points of an automotive steering mechanism. The inner wheel can be rotated up to forty five degrees with fair accuracy. Results show that the divergent end behavior of Ackermann Steering Mechanism has been overcome by the present mechanism. The work is similar to earlier work by the present author. But the present mechanism is a trailing mechanism instead of a leading one. This helps to eliminate the spur gears used earlier to bring the mechanism on the rear side of the front axle.

In spark ignition (SI) engines, the accurate control of air fuel ratio (AFR) in the stoichiometric value is required to reduce emission and fuel consumption. The wide operating range, the inherent nonlinearities and the modeling uncertainties of the engine system are the main difficulties arising in the design of AFR controller. In this paper, an optimization-based nonlinear control law is analytically developed for the injected fuel mass flow using the prediction of air fuel ratio response from a mean value engine model. The controller accuracy is more increased without chattering by appending the integral feedback technique to the design method. The simulation studies are carried out by applying severe changes in the throttle body angle to evaluate the performance of the proposed controller with and without integral feedback. The results show that the proposed controller is more effective than the conventional sliding mode controller in regulating the AFR without chattering.

This paper presents a reduce roll vibration of the full vehicle model with passive suspension systems using vibration absorber to change the dynamic system matrix stat’s eigenvalue. Since using the controller system has been splurged and required to energy consuming, in this research the vehicle body roll vibration has been reduced and supplied vehicle stability using a vibration absorber for the passive suspension system. In this paper a new manner is introduced to reduce body roll angle and body's roll acceleration. The transverse instability in the independent suspension is a main problem, roll angle decreased transverse stability, that it has been reduced using vibration absorber. The optimal value of vibration absorber’s mass, spring and damping coefficient has been determined by using genetic algorithms (GA) to achieve developed roll angle behavior. The main purpose of this article is to reduce vehicle body roll angle that has been acquired using vibration absorber, this manner is better than other ways for roll reduction of vehicle body because it has done without any energy consuming.

Artificial neural network was considered in previous studies for prediction of engine performance and emissions. ICA methodology was inspired in order to optimize the weights of multilayer perceptron (MLP) of artificial neural network so that closer estimation of output results can be achieved. Current paper aimed at prediction of engine power, soot, NOx, CO2, O2, and temperature with the aid of feed forward ANN optimized by imperialist competitive algorithm. Excess air percent, engine revolution, torque, and fuel mass were taken into account as elements of input layer in initial neural network. According to obtained results, the ANN-ICA hybrid approach was well-disposed in prediction of results. NOx revealed the best prediction performance with the least amount of MSE and the highest correlation coefficient(R) of 0.9902. Experiments were carried out at 13 mode for four cases, each comprised of amount of plastic waste (0, 2.5, 5, 7.5g) dissolved in base fuel as 95% diesel and 5% biodiesel. ANN-ICA method has proved to be selfsufficient, reliable and accurate medium of engine characteristics prediction optimization in terms of both engine efficiency and emission.