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Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
This work contains the process of having implemented DART (Dynamic Animation and Robotics Toolkit), an open source physics engine, usually used mainly for robotics, into MABE (Modular Agent-Based Evolution platform), a tool to evolve and analyze digital brains, using C++. This added much desired complexity to the system which allows the Hintze Lab to further research evolution and added new ways in which they can do so. Furthermore, the resulting software package was used to run a series of experiments in this work, which will then be analyzed and compared.
Perception of Crowding
(2023)
One of the reasons for establishing digital visitor management systems can be to avoid overcrowding and crowding (Schmücker et al., 2022).
Objective data (counts) or subjective assessments by visitors, residents or businesses can be used to identify "overcrowding", "crowding", "crowdedness" or "crowding". This analysis is about the subjective perception of crowding by visitors in selected German destinations in summer 2022.
AI in Battery Storage
(2023)
Financial institutions have a direct or indirect impact on the environment throughout their core activities. However, in the case of financial institutions, the main source of Greenhouse Gas (GHG) emissions is not the emissions produced directly by financial institutions via operating their business processes or their energy consumption, but GHG emissions produced by other sectors that are financed by the financial institutions. These type of emissions are known as Financed Emissions. Therefore, systemic assessment of financed emissions is of significant importance for the financial sector in environmental risk management processes.
The Financed Emissions Tool is used to calculate financed emissions throughout the lending activities of commercial banks. The principle of the tool is based on international standards, namely, the methodology of The Partnership for Carbon Accounting Financials (PCAF). This methodology is used for different categories of assets, including assets as important for the banks as business loan portfolios. Capital provided as a loan by the bank is used by companies for business processes as well as for financing GHG emissions.
The tool is used to calculate financed emissions from the perspective of the business loan portfolio of a bank, as well as for a sector or individual customer. Moreover, it is a useful tool for comparative analysis.
The Financed Emissions Tool enables financial institutions to analyze their credit portfolio better, determine the share of financed emissions, identify climate change-related risks and opportunities, and improve business processes in the direction of environmental management while making better strategic decisions.
We study the action of the nonlinear mapping G[z] between real or complex Banach spaces in the vicinity of a given curve with respect to possible linearization, emerging patterns of level sets, as well as existing solutions of G[z]=0. The results represent local generalizations of the standard implicit or inverse function theorem and of Newton's Lemma, considering the order of approximation needed to obtain solutions of G[z]=0.
The main technical tool is given by Jordan chains with increasing rank, used to obtain an Ansatz, appropriate for transformation of the nonlinear system to its linear part. The family of linear mappings is restricted to the case of an isolated singularity.
Geometrically, the Jordan chains define a generalized cone around the given curve, composed of approximate solutions of order 2k with k denoting the maximal rank of Jordan chains needed to ensure k-surjectivity of the linear family. Along these lines, the zero set of G[z] in the cone is calculated immediately, agreeing up to the order of k−1 with the given approximation. Hence, the results may also be interpreted as a version of Tougeron's implicit function theorem or Hensel's Lemma in Banach spaces, essentially restricted to the arc case of a single variable.
Finally, by considering a left shift of the Jordan chains, the Ansatz can be modified in a systematic way to obtain a sequence of refined versions of linearization theorems and Newton Lemmas in Banach spaces.
The development of Automated Driving Systems (ADS) has the potential to revolutionize the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mitigate FM by the driver using the driving simulator is presented. FM by the human driver refers to potential driving scenarios where the driver misinterprets the intended functionality of ADS, leading to hazardous behavior. Safety of the Intended Functionality (SOTIF) focuses on ensuring the absence of unreasonable risk resulting from hazardous behaviors related to functional insufficiencies caused by FM and performance limitations of sensors and machine learning-based algorithms for ADS. The simulation-based application of SOTIF to mitigate FM in ADS entails determining potential misuse scenarios, conducting simulation-based testing, and evaluating the effectiveness of measures dedicated to preventing or mitigating FM. The major contribution includes defining (i) test requirements for performing simulation-based testing of a potential misuse scenario, (ii) evaluation criteria in accordance with SOTIF requirements for implementing measures dedicated to preventing or mitigating FM, and (iii) approach to evaluate the effectiveness of the measures dedicated to preventing or mitigating FM. In conclusion, an exemplary case study incorporating driver-vehicle interface and driver interactions with ADS forming the basis for understanding the factors and causes contributing to FM is investigated. Furthermore, the test procedure for evaluating the effectiveness of the measures dedicated to preventing or mitigating FM by the driver is developed in this work.
The Asymmetric Public Goods Game (APGG) C++ framework offers an easy to use environment to study game theoretical questions. Specifically, it is designed to address questions in the domain of asymmetric public goods games.
We hereby publish version 1.1.2 of our software APGG (Release for DOI via Zenodo for paper in JOSS, the Journal of Open Source Software).