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In this work we address the efficient operation of public charging stations. Matching energy supply and demand requires an interdisciplinary understanding of both the mobility of electric vehicle (EV) users and the load balancing mechanisms. As a result of existing mobility studies, we propose in this work a routing service for searching and reserving public charging spots in the neighborhood of a given destination. When comparing the search results for direct drive with those for a multimodal route (using driving, walking and public transport) in an urban environment, we obtain for the latter significantly more charging options in particular at low e-mobility penetration levels, at a cost of slightly longer trip duration. Further contributions address the schedule optimization, that, due to the proposed distributed architecture, can be performed independently at each public charging station. We formulate an integer program for the controlled charging and compare results obtained both with the exact and with a greedy heuristic method.

Abstract-In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies to improve resilience of end-node services. In this paper we present a framework, called ODDR (Observation, Diagnosis, Decision, Remediation), for improving resilience of network based services through integration of self-adaptive monitoring services, network diagnosis, decision actions, and finally execution (and monitoring) of remediation actions. We detail the motivations to the ODDR design, then we present its architecture, and finally we describe our current activities towards the realization and assessment of the framework services and the main results currently achieved.

Context-dependent decisions in safety-critical applications require careful consideration of accuracy and timeliness of the underlying context information. Relevant examples include location-dependent actions in mobile distributed systems. This paper considers localization functions for personalized warning systems for railway workers, where the safety aspects require timely and precise identification whether a worker is located in a dangerous (red) or safe (green) zone within the worksite. The paper proposes and analyzes a data fusion approach based on low-cost GPS receivers integrated on mobile devices, combined with electronic fences strategically placed in the adjacent boundaries between safe and unsafe geographic zones. An approach based on the combination of a Kalman Filter for GPS-based trajectory estimation and a Hidden Markov Model for inclusion of mobility constraints and fusion with information from the electronic fences is developedand analyzed. Different accuracy metrics are proposed and the benefit obtained from the fusion with electronic fences is quantitatively analyzed in the scenarios of a single mobile entity: By having fence information, the correct zone estimation can increase by 30%, while false alarms can be reduced one order of magnitude in the tested scenario.

In this work we describe the brokerage function between electric vehicle users searching for a charging spot and the charging stations providing the charging service. We propose a routing service for locating and reserving charging spots. Furthermore, we extend the search for charging stations to the case where the trip from the charging station to destination is made using public transportation.Further contributions address the load balancing functionality at the charging station and at the low voltage grid level. In order to realize the latter, we argue for the introduction of a bidirectional interface between the charging station and the DSO, and show how available power for charging stations can be dynamically calculated. Finally, we analyze in detail different charge control concepts for grid component protection. Multimodales Routing und Energieflusssteuerung f€ ur optimierte Ladevorg€ ange bei Elektrofahrzeugen.Diese Arbeit besch€ aftigt sich mit der Verkn€ upfung zwischen den suchenden Elektroautobenutzern und den Ladestationen. Ziel ist es, mittels Verkehrslenkungsdiensten die optimale Ladestation zu finden und im Vorhinein zu reservieren. Weiters wird in dieser Arbeit die Kombination mit dem € offentlichen Verkehr in Form mehrerer Szenarien detailliert analysiert. Neben allen wichtigen Informationen f€ ur den Benutzer soll ebenfalls die zur Verf€ ugung stehende Leistung an der Ladestation geliefert werden. Die Methodik der Berechnung dieser freien Kapazit€ aten im Niederspannungsnetz wird ebenfalls erl€ autert. Abschließend werden Ladesteuerungskonzepte, welche die Netzkomponenten sch€ utzen sollen, eingef€ uhrt und auf deren Funktion gepr€ uft. IntroductionThe problem of electric vehicle (EV) charging has to be analyzed in both spatial (geographic) and temporal dimensions. The geographical aspect is related to the mobility model: a solution approach is to search for available power in charging stations near the users activity places, or to search for less congested charging stations. We show that the latter problem which is relevant in urban regions can be solved by suggesting multimodal routes (drive, walk, use public transportation).The temporal aspects arise when the distribution network operator together with the charging station owner (a new actor in e-mobility) are trying to flatten the load peak and delay in this way investments in grid enhancement. The solutions investigated in this work are: controlled charging (that is an optimized scheduling of EVcharging tasks using time windows), the LV grid level computation of the available power at each charging station and charge control concepts to protect grid components.The work is being performed within the Austrian project KOFLA (KOFLA Project website), in the new mobility program "ways2go" and the results are currently tested in a lab environment.