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Mobile Working Machines
Mobile Working Machines are defined by three characteristics. These machines have a cer-tain task of doing a working process, they are mobile, and they have a signifi cant energy share in their working functions. The machines should be as productive, efficient and of high quality as possible. All these machines in the fi eld of agriculture, forestry, construction, logistics, municipal sector, and in other special applications work in different applications. But, many technologies placed in the machines are the same, similar or comparable; therefore, different branches can learn from each other. Mobile Working Machines provides a wide and deep view into the technologies used in these machines. Appropriate for new engineers as well as those who wish to increase their knowledge in this field, this book brings together all the latest research and development into one place.
Organic Computing — A Paradigm Shift for Complex Systems
Organic Computing has emerged as a challenging vision for future information processing systems. Its basis is the insight that we will increasingly be surrounded by and depend on large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organising themselves in order to perform actions and services required by the users. These networks of intelligent systems surrounding us open fascinating ap-plication areas and at the same time bear the problem of their controllability. Hence, we have to construct such systems as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards...
Development of a CO2e quantification method and of solutions for reducing the greenhouse gas emissions of construction machines
This work focuses on the development of a quantification method for GHG (CO2e) emissions from construction machines. The method considers CO2e reduction potentials in the time past-present–future, through influencing factors from six pillars: Machine efficiency, process efficiency, energy source, operating efficiency, material efficiency and CCS. In addition, transformation solutions are proposed to reduce GHG emissions from construction machines like liquid methane, fuel cell drive or CCS.
Numerical prediction of curing and process-induced distortion of composite structures
Fiber-reinforced materials offer a huge potential for lightweight design of load-bearing structures. However, high-volume production of such parts is still a challenge in terms of cost efficiency and competitiveness. Numerical process simulation can be used to analyze underlying mechanisms and to find a suitable process design. In this study, the curing process of the resin is investigated with regard to its influence on RTM mold filling and process-induced distortion.
Experimental investigation and process simulation of the compression molding process of Sheet Molding Compound (SMC) with local reinforcements
In this book, a new three-dimensional approach for the process simulation of SMC is developed. This approach takes into account both, the core layer that is dominated by the extensional viscosity and the thin lubrication layer. In order to transfer the information from the process to the structure simulation, a CAE chain is further developed. In addition, a new rheological tool is developed to analyze flow behavior experimentally and to provide the required material parameters.
Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle
This work describes a method for weighted least squares approximation of an unbounded number of data points using a B-spline function. The method can shift the bounded B-spline function definition range during run-time. The approximation method is used for optimizing velocity trajectories for an electric vehicle with respect to travel time, comfort and energy consumption. The trajectory optimization method is extended to a driver assistance system for automated vehicle longitudinal control.
Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation
This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models.
Sustainable Automotive Technologies 2014
This volume collects the research papers presented at the 6th International Conference on Sustainable Automotive Technologies (ICSAT), Gothenburg, 2014. The topical focus lies on latest advances in vehicle technology related to sustainable mobility. ICSAT is the core and state-of-the-art conference in the field of new technologies for transportation. Research contributions from the US, Australia, Europe and Asia illustrate the pivotal role of the conference. The book provides an excellent overview of R&D activities at OEMs as well as in leading universities and laboratories.
