The future of e-mobility will consist of a large number of connected electric vehicles, smart charging stations and information systems at the intersection of electricity and mobility sector. When engineering and integrating the multitude of systems into even more complex systems-of-systems for e-mobility, interoperability and complexity handling are vital. Model-based system architectures support the engineering process of information systems with the concepts of abstraction, reduction and separation of concerns. In this paper, we contribute to the research body, by extracting requirements for managing complexity and interoperability of these systems. Further, a comparative analysis of the state-of-the-art in existing architecture models and frameworks for e-mobility is conducted. Based on the identified gaps in existing research, we propose the E-Mobility Systems Architecture (EMSA) Model, a three-dimensional systems architecture model for the e-mobility sector. Its structure originates from the well-established Smart Grid Architecture Model. We further allocate all relevant entities from the e-mobility sector to the EMSA dimensions, including a harmonized role model, functional reference architecture, component and systems allocation, as well as a mapping of data standards and communication protocols. The model then is validated qualitatively and quantitatively against the requirements with a case study approach. Our evaluation shows that the EMSA Model fulfills all requirements regarding the management of complexity and ensuring interoperability. From the case study, we further identify gaps in current data model standardization for e-mobility.

Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA.

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