ECoMobility – Connected E-Mobility. Vernetzte Elektromobilität am Beispiel der Technischen Universität Chemnitz

Das Forschungsprojekt ECoMobility – Vernetzte Elektromobilität am Beispiel der Technischen Universität Chemnitz untersuchte in unterschiedlichen Schwerpunktbereichen u.a. die Entwicklung eines vernetzten multimodalen Sharingsystems mit Elektrofahrzeugen, den Aufbau und die Steuerung der Ladeinfrastruktur, die Fahrstilklassifikation der Fahrer und dessen energieeffiziente Optimierung durch Anreize, die Entwicklung und Evaluation eines energieeffizienten Routingsystems für Elektrofahrzeuge sowie die Wirtschaftlichkeitsbetrachtung des Gesamtsystems und Geschäftsmodellentwicklung für vergleichbare Ansätze.The research project ECoMobility - Connected electromobility at Chemnitz University of Technology examined the development of a connected multimodal sharing system with electric vehicles, the implementation of the charging infrastructure, the driving style classification of the drivers and its energy-efficient optimization by incentives, the development and evaluation of an energy-efficient routing system for electric vehicles as well as the economic analysis of the overall system and business model development for comparable approaches

Meereisdrift-Kompensation zur multitemporalen Klassifizierung von Meereis aus satellitenbasierten SAR-Aufnahmen

Synthetic Aperture Radar Satelliten ermöglichen großflächige Beobachtungen des Meereises. In unserer vorangegangenen Arbeit haben wir eine Methode zur automatischen Bestimmung hochaufgelöster Meereisdriftfelder aus TerraSAR-X Aufnahmereihen entwickelt. Signifikante Strukturen im Meereis werden dabei mittels Phasenkorrelation über mehrere Aufnahmen hinweg erkannt und verfolgt. In der hier vorliegenden Arbeit dienen die erzeugten Driftvektoren als Grundlage für eine neuartige, multitemporale Analyse des Meereises hinsichtlich der vorherrschenden Eisklasse

Multiparametric Sea State from Spaceborne Synthetic Aperture Radar for Near Real Time Services

Spaceborne synthetic aperture radar (SAR) is a powerful tool for monitoring seas. The ability to work independently of sun illumination, cloud coverage and atmospheric conditions, as well as the capability of delivering spatial information, makes SAR one of the most perceptive instruments. The newest methods for processing SAR data with increased precision allow sea state fields to be estimated with local variabilities. For large areas in oceans where no in-situ measurements and only forecast predictions are available, this information is indispensable for global shipping and over human activity. Due to newest developments, the derived meteo-marine parameters can be transferred to weather services and to a ship’s bridge several minutes after acquisition, where the ship route can be optimized. The study presents a method and application for estimating series of integrated sea state parameters from satellite-borne SAR, allow processing of data from different satellites and modes in near real time (NRT). The developed Sea State Processor (SSP) estimates total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, mean wave period and period of wind sea. The algorithm was applied for the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WM) Level-1 (L1) products and also extended to the Xband TerraSAR-X (TSX) StripMap (SM) mode. The scenes are processed in raster and result in continuous sea state fields with the exception of S1 WV. Each 20 km × 20 km WV imagette, acquired every 100 km along the orbit, presents averaged values for each sea state parameter. The SSP was tuned and validated using two independent global wave models WAVEWATCH-3 (NOAA) and CMEMS (Copernicus) and NDBC buoys. The accuracy of Hs reaches an RMSE of 0.25 m by comparison with models (S1 WV); comparisons to NDBC worldwide buoys result into an RMSE of 0.3 m. Due to implemented parallelization, a fine rater step for scene processing can be practical applied: for example, S1 IW scene with coverage of 200 km × 250 km can be processed using raster step of 1 km (corresponds to ~50.000 subscenes) during minutes. The DLR Ground Station “Neustrelitz” applies SSP as part of a near real-time demonstrator service that involves a fully automated daily provision of surface wind and sea state parameters estimates from S1 IW for the North and Baltic Sea. All results and the presented methods are novel and provide a wide field for applications and implementations in prediction systems