Smart Energy Management System based on Reconfigurable AI Chip and Electrical Vehicles

Abstract

Almost every larger city in Europe has ambitious smart city projects. This is particularly true for Hamburg, a Hanseatic city in the north of Germany. Hamburg is the smartest city in Germany according to a Federal Association for Information Technology. Although there are no megacities in the European Union (the largest city in the European Union is Berlin with 3.7 million inhabitants), the increasing urbanization is apparent and produces problems to be solved. At the same time rural depopulation creates conjugated problems.One category of these problems is mobility. Mobility can be regarded as the need to move persons and freight. In densely populated cities an increasing amount of transport users have to share a decreasing amount of space with conflicting needs. At the same time in rural areas, a dwindling supply of local public transport makes the mobility of the remaining residents more difficult. The same applies to parcel delivery or the supply of goods. Autonomous systems have great potential to create a sustainable and livable environment. The author has initiated a publicly funded project to investigate technologies of autonomous mobile systems which interact with a smart city. The test area intelligent urban mobility (Testfeld intelligente Quartiersmobilitat) at the campus of Hamburgs University of Applied Sciences is created to do research on connected and autonomous mobile systems like multipurpose robots and other mobility users like pedestrians with a smartphone. A particular focus is on neighborhood mobility. This means that distances of less than 3 kilometers usually have to be covered. The special type of needs in neighborhood mobility has two important aspects that affect development of autonomous mobile systems: It is slow mobility and the transport users are especially vulnerable. The acceptance of the residents of autonomous systems is equally important, as is the protection of privacy when collecting environmental data. They are expected to make decisions on their own in complex environments. The real world usually differs from a simulation or an experimental setup in a laboratory - a problem commonly referred to as Sim-2-Real gap. Active and non-destructive exploration is expected from an autonomous system to solve unexpected problems. Machine learning methods come into play which in turn have their own pitfalls. The author has built a specialized laboratory to investigate machine learning technology applied to autonomous systems. In this laboratory miniature autonomous vehicles are developed. The general idea of this experimental setup allows research on new methodologies for autonomous systems in a very small scale