Stress sensitivity analysis and optimization of automobile body frame consisting of rectangular tubes

At conceptual design stage, beam element is extensively used to create the frame structure of automobile body, which can not only archive the accurate stiffness but also reduce much computational cost. However, the stress definition of beam element is very complex so that the stress sensitivity and optimization are difficult to analytically derive and numerically program. This paper presents an solution to this problem and an application in the lightweight optimization design of automobile frame. Firstly, maximal Von Mises stress of rectangular tube is calculated by using the superposition of stress, which is together induced by the axial force, bending moments, torsional moment and shear force. Secondly, the sensitivity of Von Mises Stress with respect to size design variables: breadth, height and thickness are derived, respectively. Thirdly, an optimal criterion is constructed by Lagrangian multiplier method to solve the frame optimization with stress constraints. Lastly, numerical example of car frame proves that the proposed method can guarantee the stress of each beam element almost fully reaches at the yielding stress.     

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.     

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.     

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.     

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.     

德国铁路的内燃牵引

ＤＢ（德国铁路）的电气化铁路占４４％，却完成了８３．７％的牵引任务。内燃牵引主要用在运输量少的线路和调车、车辆的交换输送上。ＤＢ铁路现有的内燃机车最少也有２３年车龄了，最老的机车是１９５５年制造的。有时，机车的维修量很大。列车编组的基础设施的合理化改造改变了机车运用条件。假设总牵引吨位将达到２０００ｔ，而网络２１的技术规范将把干线的最大速度提高到１００ｋｍ／ｈ。如果按总牵引吨位和速度来考虑货运量对     

Modified lateral control of an autonomous vehicle by look-ahead and look-down sensing

This paper presents a modified lateral control method for an autonomous vehicle with both look-ahead and look-down sensing systems. To cope with sensor noise and modeling uncertainty in the lateral control of the vehicle, a modified LMI-based H lateral controller was proposed, which uses the look-ahead information of the lateral offset error measured at the front of vehicle and the look-down information of the vehicle yaw angle error between the reference lane and the centerline of the vehicle. To verify the safety and the performance of the lateral control, a scaled-down vehicle was developed, and the positioning of the vehicle was estimated with USAT. The proposed controller, which uses both look-ahead and look-down information, was tested for lane changing and reference lane tracking with both simulation and experiment. The simulation and experimental results show that the proposed controller has better tracking and handling performance compared with a controller that uses only the look-ahead information of the target heading angle error.