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- Brake set-up (2)
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- UAV (2)
- Active humidity control (1)
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- Cost function (1)
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- Diesel Engine (1)
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- European Transient Cycle (1)
- Extension–twist coupling (1)
- Full-vehicle crash test (1)
- Human factors (1)
- Impedance analysis (1)
- Incident analysis (1)
- Lifting propeller (1)
- Local path planning (1)
- Molten salt receiver system (1)
- Molten salt solar tower (1)
- Multi-objective optimization (1)
- PEM fuel cell (1)
- Parasitic drag (1)
- Predictive battery discharge (1)
- Relative air humidity (1)
- Selective Catalytic Reduction (1)
- Shunting (1)
- Train composition (1)
- Transient flux distribution (1)
- Trapeze effect (1)
- Two-phase modelling (1)
- Unmanned aerial vehicles (1)
- adaptive systems (1)
- artificial intelligence (1)
- assistance system (1)
- eVTOL development (1)
- eVTOL safety (1)
- fuel cell vehicle (1)
- gamification (1)
- intelligent control (1)
- intelligent energy management (1)
- machine learning (1)
- manufacturing (1)
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Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
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.