While today's agent oriented software engineering facilitates the development of complex, distributed systems, fundamental problems remain. One of the difficulties is that one can see only part of the system, or one side of the business, but not the whole context. BPMN is considered a remedy here, but while suitable for modelling some aspects of agenthood, there are others for which BPMN does not go very well. In this paper, we show how BPMN can be embedded in a broader methodology so that its strengths can be exploited while avoiding its weaknesses.
Due to the fact that electric vehicles have not broadly entered the vehicle market there are many attempts to convince producers to integrate technologies that utilise embedded batteries for purposes different from driving. The vehicle-to-grid technology, for instance, literally turns electric vehicles into a mobile battery, enabling new areas of applications (e.g., to provide regulatory energy, to do grid-load balancing, or to buffer surpluses of energy) and business perspectives. Utilising a vehicle's battery, however is not without a price-in this case: the driver's mobility. Given this dependency, it is interesting that most available works consider the application of electric vehicles for energy and grid-related problems in isolation, that is, detached from mobility-related issues. The distributed artificial intelligence laboratory, or DAI-Lab, is a thirdparty funded research lab at Technische Universität Berlin and integrates the chair for agent technologies in business applications and telecommunication. The DAI-Lab has engaged in a large number of both, past and upcoming projects concerned with two aspects of managing electric vehicles, namely: energy and mobility. This article aims to summarise experiences that were collected during the last years and to present developed solutions which consider energy and mobility-related problems jointly.
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