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Technical Paper

The Diesel Exhaust Aftertreatment (DEXA) Cluster: A Systematic Approach to Diesel Particulate Emission Control in Europe

2004-03-08
2004-01-0694
The DEXA Cluster consisted of three closely interlinked projects. In 2003 the DEXA Cluster concluded by demonstrating the successful development of critical technologies for Diesel exhaust particulate after-treatment, without adverse effects on NOx emissions and maintaining the fuel economy advantages of the Diesel engine well beyond the EURO IV (2000) emission standards horizon. In the present paper the most important results of the DEXA Cluster projects in the demonstration of advanced particulate control technologies, the development of a simulation toolkit for the design of diesel exhaust after-treatment systems and the development of novel particulate characterization methodologies, are presented. The motivation for the DEXA Cluster research was to increase the market competitiveness of diesel engine powertrains for passenger cars worldwide, and to accelerate the adoption of particulate control technology.
Technical Paper

Automation of Road Vehicles Using V2X: An Application to Intersection Automation

2017-03-28
2017-01-0078
Today, automated vehicles mostly rely on ego vehicle sensors such as cameras, radar or LiDAR sensors that are limited in their sensing capability and range. Vehicle-to-everything (V2X) communication has the potential to appropriately complement these sensors and even allow for a cooperative, proactive interaction of vehicles. As such, V2X communication might play a vital role on the way to smart and efficient traffic solutions. In the public funded research project UK Autodrive, we are currently investigating and experimentally evaluating V2X-based applications based on dedicated short range communication (DSRC). Moreover, the novel application intersection priority management (IPM) is part of the research project. IPM aims at automating intersections in such a way that vehicles can pass safely and even more efficiently without the use of traffic lights or signs.
Technical Paper

Lightweight Automobiles ALLIANCE Project: First Results of Environmental and Economic Assessment from a Life-Cycle Perspective

2018-05-30
2018-37-0027
In the last years the research activities in the field of lightweighting have been advancing rapidly. The introduction of innovative materials and manufacturing technologies has allowed significant weight reduction. Despite this, novel technologies and materials have not reached a wide distribution. The reasons for this are mainly high production costs and environmental impacts of manufacturing that do not compensate benefits during operation. The paper deals with the AffordabLe LIghtweight Automobiles AlliaNCE (ALLIANCE) project which has the goal of developing novel advanced automotive materials and production technologies, aiming at an average 25% weight reduction over 100 k units/year, at costs of <3 €/kg. The article is focussed on Work Package 1 (WP1) of the project, aimed at estimating the full attributes of innovative design solutions by assessing costs, energy demand and GWP over the entire vehicle Life Cycle (LC).
Technical Paper

Efficient Test Bench Operation with Early Damage Detection Systems

2019-09-09
2019-24-0192
The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.
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