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Scientific questions
- How can a non-stationary heat offering in the commercial vehicle be used to reduce fuel consumption?
- Which potentials offer route and environmental information among with predicted speed and load trajectories to increase the efficiency of a ORC-System?
Methods
- Desktop bound holistic simulation model for a heavy duty truck incl. an ORC System
- Prediction of massflows, temperatures and mixture quality (AFR) of exhaust gas
Heavy-duty trucks are one of the main contributors to greenhouse gas emissions in German traffic. Drivetrain electrification is an option to reduce tailpipe emissions by increasing energy conversion efficiency. To evaluate the vehicle’s environmental impacts, it is necessary to consider the entire life cycle. In addition to the daily use, it is also necessary to include the impact of production and disposal. This study presents the comparative life cycle analysis of a parallel hybrid and a conventional heavy-duty truck in long-haul operation. Assuming a uniform vehicle glider, only the differing parts of both drivetrains are taken into account to calculate the environmental burdens of the production. The use phase is modeled by a backward simulation in MATLAB/Simulink considering a characteristic driving cycle. A break-even analysis is conducted to show at what mileage the larger CO2eq emissions due to the production of the electric drivetrain are compensated. The effect of parameter variation on the break-even mileage is investigated by a sensitivity analysis. The results of this analysis show the difference in CO2eq/t km is negative, indicating that the hybrid vehicle releases 4.34 g CO2eq/t km over a lifetime fewer emissions compared to the diesel truck. The break-even analysis also emphasizes the advantages of the electrified drivetrain, compensating the larger emissions generated during production after already a distance of 15,800 km (approx. 1.5 months of operation time). The intersection coordinates, distance, and CO2eq, strongly depend on fuel, emissions for battery production and the driving profile, which lead to nearly all parameter variations showing an increase in break-even distance.
The results of a statistical investigation of 42 fixed-wing, small to medium sized (20 kg−1000 kg) reconnaissance unmanned air vehicles (UAVs) are presented. Regression analyses are used to identify correlations of the most relevant geometry dimensions with the UAV’s maximum take-off mass. The findings allow an empirical based geometry-build up for a complete unmanned aircraft by referring to its take-off mass only. This provides a bridge between very early design stages (initial sizing) and the later determination of shapes and dimensions. The correlations might be integrated into a UAV sizing environment and allow designers to implement more sophisticated drag and weight estimation methods in this process. Additional information on correlation factors for a rough drag estimation methodology indicate how this technique can significantly enhance the accuracy of early design iterations.
A hybrid-electric propulsion system combines the advantages of fuel-based systems and battery powered systems and offers new design freedom. To take full advantage of this technology, aircraft designers must be aware of its key differences, compared to conventional, carbon-fuel based, propulsion systems. This paper gives an overview of the challenges and potential benefits associated with the design of aircraft that use hybrid-electric propulsion systems. It offers an introduction of the most popular hybrid-electric propulsion architectures and critically assess them against the conventional and fully electric propulsion configurations. The effects on operational aspects and design aspects are covered. Special consideration is given to the application of hybrid-electric propulsion technology to both unmanned and vertical take-off and landing aircraft. The authors conclude that electric propulsion technology has the potential to revolutionize aircraft design. However, new and innovative methods must be researched, to realize the full benefit of the technology.
We present first results from a newly developed monitoring station for a closed loop geothermal heat pump test installation at our campus, consisting of helix coils and plate heat exchangers, as well as an ice-store system. There are more than 40 temperature sensors and several soil moisture content sensors distributed around the system, allowing a detailed monitoring under different operating conditions.In the view of the modern development of renewable energies along with the newly concepts known as Internet of Things and Industry 4.0 (high-tech strategy from the German government), we created a user-friendly web application, which will connect the things (sensors) with the open network (www). Besides other advantages, this allows a continuous remote monitoring of the data from the numerous sensors at an arbitrary sampling rate.Based on the recorded data, we will also present first results from numerical simulations, taking into account all relevant heat transport processes.The aim is to improve the understanding of these processes and their influence on the thermal behavior of shallow geothermal systems in the unsaturated zone. This will in turn facilitate the prediction of the performance of these systems and therefore yield an improvement in their dimensioning when designing a specific shallow geothermal installation.
The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition.
In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon.
To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed.