Real-Driving-Based Comparison of the Eco-Impact of Powertrain Concepts using a Data-Driven Optimization Environment

In order to limit the effects of man-made climate change, the assessment of the ecological impact of different powertrain concepts is of increasing relevance and intensely studied. In this contribution we present a data-driven optimization environment that enables to identify the ecological potential of different concepts for different scenarios. The parametrization of each powertrain concept is dedicatedly optimized to minimize the ecological impact, which allows for an unbiased and reliable comparison on an uniform evaluation basis. To exploit the potential of each single powertrain parametrization, the operating strategy of the powertrain is adapted. Naturalistic driving profiles, including the speed, acceleration and road-slope information are depicted by multidimensional and representative driving cycles, allowing for an efficient search of the real-driving-optimal powertrain parametrizations within the optimization. In this study, we investigate long-range capable vehicles for a scenario in the reference year 2030 in Germany. Conventional vehicles, battery electric vehicles, fuel cell electric vehicles and plug-in hybrid electric vehicles are examined. Finally, the results are compared to an evaluation of the CO2 emissions according to the Worldwide harmonized Light vehicles Test Procedure (WLTP)

Anticipatory Longitudinal Vehicle Control using a LSTM Prediction Model

In this paper, an approach for longitudinal vehicle control is proposed that integrates a Machine Learning model for the speed prediction of the leading vehicle in order to improve the energy efficiency of the control in a city driving environment. The prediction is employed in an additional control mode that extends a conventional state-of-the-art speed and headway control. The approach aims to reduce the vehicle consumption through a more anticipatory driving style. The prediction model uses an encoder-decoder LSTM network with additional information from Vehicle-2-X communication and is trained through supervised learning with training data generated from simulation. A co-simulation environment comprising of an ego vehicle simulation and a microscopic traffic simulation is used for the generation of training data and the assessment of the control approach. The proposed control is compared to a benchmark Adaptive Cruise Control scheme for three battery electric vehicles with different powertrain specifications on multiple routes in a simulated city-driving environment of Darmstadt, Germany. The results show that the consumption of the vehicles can be reduced for all vehicles by 3–5 % while still maintaining similar mean speeds on different routes through the city

A survey of sequential adaptive sampling strategy for transmission power loss measurement

Laboratory experiments for characterizing the power loss behavior in transmissions causes time and energy costs when performing the traditional factorial design strategy. This is because the space filling strategy may locate redundant samples with low information regarding the measurement targets. This article proposes a sequential adaptive sampling methodology for performing efficient and informative power loss measurements. The presented sampling methodology associates surrogate modeling based on a Gaussian Process approach with Subset Simulation. Gaussian Process Regression creates a statistical model with the estimation targets and the expression of uncertainty. Subset Simulation is applied to efficiently identify the regions where an objective function reaches a predefined critical threshold. The proposed adaptive sampling method is implemented for the real-time measurement of a 7-speed DCT on a drivetrain test bench. Compared to the traditional factorial design, the iterative adaption of the proposed method ensures an informative and effective measurement and an automatic termination with reduced time and energy cost

Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach

Teoría de la educación : educación y cultura en la sociedad de la información

Monográfico con el título: 'La Alfabetización Tecnológica y el desarrollo regional'. Resumen basado en el de la publicaciónSe exponen los últimos progresos en las actividades de investigación aplicadas en el departamento de Folklore y Ethnology del University College Cork con particular referencia a las implicaciones del uso de las tecnologías informáticas y digitales en ambientes de trabajo de campo. Destacando el caso del establecimiento de un centro Multimedia para la Etnología Urbana y Regional como un archivo virtual, se explora la dimensión pedagógica relativa a los contextos educativos adultos y no tradicionales. Se presta especial atención a la política de financiación gubernamental, especialmente su aplicación al desarrollo interdisciplinario de ayudas de autoaprendizaje a través del uso del irlandés como lengua minoritaria. Además, sitúa la discusión dentro de las perspectivas teóricas más amplias de la cultura popular y los medios de comunicación para explorar las potencialidades y los límites existentes e inesperados relativos a la emergencia de la sociedad de la información y la comunicación.Castilla y LeónES

The Sensitivity in Consumption of Different Vehicle Drivetrain Concepts Under Varying Operating Conditions: A Simulative Data Driven Approach

As an important aspect of today’s efforts to reduce greenhouse gas emissions, the energy demand of passenger cars is a subject of research. Different drivetrain concepts like plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) are introduced into the market in addition to conventional internal combustion engine vehicles (ICEV) to address this issue. However, the consumption highly depends on individual usage profiles and external operating conditions, especially when considering secondary energy demands like heating, ventilation and air conditioning (HVAC). The approach presented in this work aims to estimate vehicle consumptions based on real world driving profiles and weather data under consideration of secondary demands. For this purpose, a primary and a secondary consumption model are developed that interact with each other to estimate realistic vehicle consumptions for different drivetrain concepts. The models are parametrized by referring to state of the art contributions and the results are made plausible by comparison to literature. The sensitivities of the consumptions are then analysed as a function of trip distance and ambient temperature to assess the influence of the operating conditions on the consumption. The results show that especially in the case of the BEV and PHEV, the trip distance and the ambient temperature are a first-order influencing factor on the total vehicle energy demand. Thus, it is not sufficient to evaluate new vehicle concepts solely on one-dimensional driving cycles to assess their energy demand. Instead, the external conditions must be taken into account for a proper assessment of the vehicle’s real world consumption