Open Access

From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.

This paper focuses on data-driven remaining useful life prediction using ensemble methods for prognostics and health management. An important factor for the performance of an ensemble method is the diversity within the ensemble. An effective neural network ensemble method that emphasizes the generation of diversity is negative correlation learning. It is argued that for both diagnosis and prognosis, the consideration of uncertainties has a substantial added benefit over a simple point estimate. For this reason, a prediction interval is derived for the ensemble method negative correlation learning using the delta method. In the delta method, the neural network is treated as a nonlinear regression model, which is approximated by a Taylor series. A look at the derived formula of the prediction interval, emphasizes that negative correlation learning behaves inversely to a regularization. Furthermore, the formula for a diversity parameter of zero is equal to the prediction interval of the regular multilayer perceptron.

The quality of a repairable system can be described using its availability. Typically, a high degree of availability is demanded by the customer. To analyze the availability of a repairable system, the specification of reliability and maintainability are needed. Usually, they are demonstrated based on limited sample sizes, e.g., by analyzing the failure times of a life time test. The evaluation of the test results yields a mean-value distribution of reliability and maintainability as well as its confidence interval. Consequently, the calculated availability based on these inputs also need to be expressed including a confidence interval. In this paper, firstly several approaches to calculate the confidence interval of steady-state availability based on reliability and maintainability are presented. Afterwards, a procedure to investigate and evaluate the quality and accuracy of the confidence intervals calculated with the presented methods is shown. Therefore, the coverage probability as the most common indicator is used. Based on an exemplary parameter study which is performed, the accuracy of the confidence intervals determined with the methods is investigated and evaluated in the case of exponentially distributed failure and repair times. Finally, several hints for an effective availability calculation with confidence intervals are given.

Moderne Visualisierungsmethoden wie bspw. Mixed Reality Methoden in Kombination mit Digitalen Zwillingen eröffnen neue Formen der Mensch-Roboter-Interaktion bei der Offline-Programmierung von Industrierobotern. Durch die Verschmelzung von realen und virtuellen Inhalten ist eine intuitivere dreidimensionale Interaktion zwischen Mensch und Digitalem Zwilling möglich. Bislang verhindern jedoch die plattform- und endgeräteabhängige Entwicklung der Anwendungen sowie die fehlenden Schnittstellen zwischen modernen Endgeräten, Digitalen Zwillingen und der industriellen Steuerungstechnik den breiten Einsatz dieser Technologien. Mit diesen Herausforderungen beschäftigt sich das Forschungslabor Virtual Automation Lab (VAL) der Fakultät Maschinenbau an der Hochschule Esslingen. Kernkompetenz des VAL ist die Erforschung und der Einsatz von Mixed Reality Methoden im Maschinenbau. Ergänzend wird im Rahmen der vom Land BW finanzierten Transferinitiative „Transferplattform BW Industrie 4.0“ Forschungstransfer für kleine und mittelständische Unternehmen (KMU) geleistet. Das in Kooperation mit den Hochschulen Aalen und Reutlingen und der Steinbeis-Stiftung durchgeführte Transferprojekt soll einen niederschwelligen Zugang für KMU zu Themen wie servicebasierter Einsatz von Digitalen Zwillingen im industriellen Umfeld, webbasiertes 3D-Maschinenmonitoring und Mixed Reality Anwendungen ermöglichen. Im Rahmen dieses Beitrags wird die am VAL entwickelte Digital Twin as a Service – Plattform, die Interaktionsabstraktion und -modellierung zur eingabegeräteunabhängigen intuitiven Mensch-Roboter-Interaktion sowie ein auf Basis dieser Plattform entwickelter Digitaler Zwilling der Maschinenbaulabore der Hochschule Esslingen mit Anbindung an Offline-Programmiersysteme von Roboterherstellern, vorgestellt.

Negotiations are a relevant and highly complex business skill. Therefore, extensive training is required to become a good negotiator. Such training is offered by universities for their students and by companies for their employees. The present paper designs gamified feedback features in electronic negotiation training and evaluates their potential and their effects. Following a design science research method, feedback mechanisms in electronic negotiation training are derived from literature. An assessment regarding their relevance for e-negotiation training shows a preparation quiz, set and track goals and expert reviews to be the most useful gamified feedback mechanisms. Dedicated mock-ups implementing these feedback mechanisms are designed and evaluated in semi-structured interviews showing their capability to improve relevant negotiation skills, as well as motivation and competence of the learners. Out of the three mock-ups, the interviewees prefer the feedback mechanisms “expert review” and “set and track goals”; both mechanisms provide a competence-confirming learning experience and an autonomous learning experience.