Development of a new sound metric for impact sound in a passenger car using the wavelet transform

Vehicles can experience impacts due to harsh road conditions. Contact with an uneven road surface causes vehicles to vibrate, which generates a loud impact sound. The attenuation of such noise is important because car passengers may complain about the impact noise. However, perfect removal of impact noise is not possible because most of it is caused by external conditions. More research is needed on the objective attributes of impact noise; however, the problem of impact noise is not a simple matter because impact noise is transient in nature and reaches a high level instantaneously. In this paper, a new objective attribute of impact noise is designed using the wavelet transform, which is appropriate for analyzing nonstationary signals, such as an impact signal. The usefulness of the new objective attribute, which is a sound metric, is examined by comparing the mean subjective ratings for real impact noise in passenger cars. The new sound metric has better correlation with the mean subjective rating than currently existing sound metrics.     

卵砾石层管推进工程超挖及卡钻对管材与土壤互制行为的影响研究

非明挖工法在都市施作时,常因困难地质(如卵砾石层)或障碍物而造成施工困难、工期延误或发生灾损。本文主要针对非明挖施工法里的管推进施工法(Pipe-jacking Method),探讨管推进工法施工时可能遭遇的困难,以及施工困难状况下之管材土体互制行为。探讨的施工困难包括超挖、挤压、卡钻等问题。本研究针对超挖范围、卡钻位置、阻力大小以及推进力施加位置等,以ABAQUS有限元素软件进行三维数值仿真,探讨直线推进施工中的问题。     

Design and implementation of a new clamping force estimator in Electro-Mechanical Brake systems

In this paper, an improved clamping force estimator is proposed for Electro-Mechanical Brake (EMB) systems by using the motor rotor position information and the hysteresis characteristics of mechanical parts in the EMB. A cascaded type of a force/position control system with a force sensor or an estimator was designed and implemented to control the clamping force and to keep the clearance gap in EMB systems. The EMB Hardware-In-the-Loop-Simulation (HILS) results show that the proposed force estimator yields better estimation performance than the existing estimator and that the clamping force control system based on the estimator can be also used for the fault tolerant control of the system.     

Review of flywheel based internal combustion engine hybrid vehicles

Hybrid vehicles of different configurations and utilizing different energy storage systems have existed in development for many decades and more recently in limited production. They can be grouped as parallel, series or complex hybrids. Another classification is micro, mild and full hybrids which makes the distinction on the basis of functionality. The common energy storage systems in hybrid vehicles are batteries, supercapacitors and high speed flywheels. This paper aims to review a specific type of hybrid vehicle which involves the internal combustion engine (ICE) as the prime mover and the high speed flywheel as an energy storage device. Such hybrids are now attracting considerable interest given their potential for low cost. It is hence timely to produce a review of research and development in this subject. The flywheel is coupled to the drive line with a continuous variable transmission (CVT). The CVT can be of various types such as electrical, hydraulic or mechanical but usually in this case it is a non-electrical one. Different configurations are possible and the paper provides a timeline of the development of such powertrains with various examples. These types of hybrid vehicles have existed as prototypes for many decades and the authors believe that their development has reached levels where they can be considered serious contenders for production vehicles.     

Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.     

Computational and experimental investigation of the unsteady flow structures around automotive outside rear-view mirrors

The unsteady flow field around two automotive outside rear-view mirrors is investigated. This study includes comprehensive experimental and computational approaches in order to characterize the complex flow structures in the wake of the mirrors. The experiments were carried out in a wind tunnel which included the measurements of the instantaneous and averaged velocity fields as well as mean and unsteady surface pressure distributions. The simulations were performed using Large Eddy Simulation (LES). The LES approach (particularly with the dynamic subgrid viscosity model) provided good agreements with the experiments for the velocity and the surface pressure distributions. The experimental and the computational results of this study will be used as a benchmark to validate the current and the future CFD development and the subsequent aero-acoustic computations.     

Driver curve speed model and its application to ACC speed control in curved roads

A speed control algorithm for an ACC (Adaptive Cruise Control) system for curved roads is proposed based on driver behavior characteristics. As the foundation of this research, a driver speed model for curved roads is developed using a series of experimental data regarding driver behavior. To adapt the model to each driver’s individual curve speed behavior, the coefficients of the model are identified in real time from the data sequences collected during drivers’ manual operation stage by a self-learning algorithm based on a Recursive Least-Square (RLS) method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data collected in the manual operation phase, and the identification results are applied during the ACC automatic control phase. Based on the developed model, the ACC speed control algorithm is modified to provide each individual driver with a customized speed profile for the scenario of a curved road with no car ahead. Tests verify the applicability of the modified system.     

Dual extended Kalman filter for vehicle state and parameter estimation

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.     

Estimation of maximum road friction coefficient based on Lyapunov method

With the real time and accurate information of motor torque and rotation speed of the four-in-wheel-motordrive electric vehicles, a slip based algorithm for estimating maximum road friction coefficient is designed using Lyapunov stability theory. Modified Burckhardt tire model is used to describe longitudinal slip property of the tire. By introducing a new state variable, a nonlinear estimator is proposed to estimate the longitudinal tire force and the maximum road friction coefficient simultaneously. With the appropriate selection of estimation gain, the convergence of the estimation error of the tire longitudinal force and maximum road friction coefficient is proved through Lyapunov stability analysis. In addition, the error is exponentially stable near the origin. Finally the method is validated with Carsim-Simulink co-simulation and real vehicle tests under multi working conditions in acceleration situation which demonstrate high computational efficiency and accuracy of this method.     

Cycle-simulation turbulence modelling of IC engines

Numerical simulations of IC engines are of high interest for automotive engineers worldwide. The simulation models should be as fast as possible, low-computational effort and predictive tool. The correct prediction of turbulence level inside the combustion chamber of spark ignition engines is the most important factor influencing to the engine working cycle. This paper presents a development of the k-ε turbulence model applied to the commercial cycle-simulation software with the high emphasis on the intake part. The validation was performed on two engine geometries with the variation of engine speed and load comparing the cycle-simulation results of the turbulent kinetic energy and in-cylinder temperature with 3-D CFD results. In order to apply the cycle-simulation turbulence model for the simulation of entire engine map, the parameterization model of turbulence constants was proposed. The parameterized turbulence model was optimized using NLPQL optimization algorithm where the single set of turbulence model parameters for each engine was found. A good agreement of the turbulent kinetic energy during the expansion was achieved when the turbulence affects the flame front propagation and combustion rate as well.