Identification Methods for Vehicle System Dynamics

The paper presents a survey on parameter identification techniques for complex vehicle models. In order to cope with the complexity of the model, the information on the system available from the equations of motion has to be included in the identification process. Basic methods for the solution of this problem are shown. The application of the approach is demonstrated by identification of the vertical automobile dynamics. It is concluded that the presented techniques will become more important with increasing applications of theoretical modeling in vehicle system dynamics.     

Development of electrostatic diesel particulate matter filtration systems combined with a metallic Flow-Through Filter and electrostatic methods

A 3000 cc diesel engine attached to an engine dynamo was used to test three newly developed electrostatic Diesel Particulate matter filtration Systems (DPS 1, 2, and 3) under four steady-state engine operating conditions: idle, 2000 rpm with no load, and 2000 rpm under 25% and 50% loads. Of the two developed alternatives, DPS 1 and DPS 2, DPS 2 comprises an ionization section, electrostatic field additional section and Flow-Through Filter (FTF), which achieved almost 90% removal of particulate matter (PM) under the engine’s operating conditions, and the efficiency of the FTF was maintained between 20% and 50%. Comparing the long-term performance of DPS 2 and DPS 3 (effectively a serial combination of two DPS 2s) with a commercially-available Diesel Particulate Filter (DPF), the DPS 2 and DPS 3 achieved almost the same efficiency for removing PM as the DPF but had significantly improved (75%∼90% lower) differential pressure drops.     

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.     

Influence of load and inflation pressure on the tyre rolling resistance

Tyre load and inflation pressure are important factors controlling rolling resistance of road vehicles. The article presents results obtained in the Technical University of Gdańsk during laboratory and road measurements of different car tyres rolling on different pavements. The knowledge of rolling resistance characteristics is important for modelling car dynamics as well as fuel consumption. It is also necessary to establish proper test conditions in the future standardized on-road method of measuring rolling resistance. The results indicate that while an increase of load always leads to the increase of rolling resistance force, the influence on Coefficient of Rolling Resistance is more complicated and unpredictable. They also indicate that tyres with high rolling resistance are more sensitive to inflation pressure changes than low rolling resistance tyres.     

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feeding their geometry measurements into large databases. These data can be retrieved, but little is currently done with the data other than the generation of reports of track geometry that exceeds predefined thresholds. There are examples of simpler systems that measure some track geometry parameters more or less directly and accurately, but forego parameters such as gauge. Additionally, there are experimental systems that use mathematics and models to infer track geometry using data from sensors placed on an in-service vehicle. Finally, there are systems that do not claim to measure track geometry, but monitor some other quantity such as ride quality or bogie acceleration to infer poor track geometry without explicitly measuring it.     

Scheduling short-term marine transport of bulk products

A multinational company uses a personal computer to schedule a fleet of coastal tankers and barges transporting liquid bulk products among plants, distribution centres (tank farms), and industrial customers. A simple spreadsheet interface cloaks a sophisticated optimization-based decision support system and makes this system useable via a varity of natural languages. The dispatchers, whose native language is not English, and some of whom presumably speak no English at all, communicate via the spreadsheet, and view recommended schedules displayed in Gantt charts both internationally familiar tools. Inside the spreadsheet, a highly detailed simulation can generate every feasible alternate vessel employment schedule, and an integer linear set partitioning model selects one schedule for each vessel so that all loads and deliveries are completed at minimal cost while satisfying all operational requirements. The optimized fleet employment schedule is displyed graphically with hourly time resolution over a planning horizon of 2-3 weeks. Each vessel will customarily make several voyages and many port calls to load and unload products during this time.