The E-mobility Case Study

Abstract

Abstract. Electro-mobility (e-mobility) is one of the promising technologies being considered by automotive OEMs as an alternative to internal combustion engines as a means of propulsion. The e-mobility case study provides a novel example of a relevant industry application within the ASCENS framework. An overview of the system design is given which describes how e-mobility is conceptualized and then transformed using the ensemble development life cycle (EDLC) approach into a distributed autonomic (i.e self-aware, self-adaptive) component-based software system. The system requirements engineering is based on the state-of-theaffairs (SOTA) approach and the invariant refinement method (IRM) which are both revisited and applied. Regarding the implementation and deployment of the system, a dependable emergent ensembles of components (DEECo) approach is utilized. The DEECo components and ensembles are coded and deployed using the Java-based jDEECo runtime environment. The runtime environment integrates the multi-agent transport simulation tool (MATSim), which is used to predict the effects of the physical interactions of users, vehicles and infrastructure resources. jDEECo handles multiple MATSim instances to allow for different belief states between components and ensembles.

The SOTA approach to engineering collective adaptive systems

“…Let us now conceptualize the problem (deriving from a real-world case study in which we have applied SOTA [28]) in detail.…”

“…To exemplify how SOTA can be exploited and to show how it can provide the above claimed advantage, we will make reference to a case study in the area of autonomous vehicles. In particular, as we are entering the era of autonomous cars, many envision that future urban mobility will no longer be primarily supported by private vehicles, but rather by fleets of autonomous vehicles, either owned by private companies or by the municipality itself, and devoted to car or ride sharing [9,28], and to the delivery of merchandise [55]. Thus, properly organizing and managing such fleets will be of primary importance in future cities.…”

The Invariant Refinement Method

“…To illustrate the IRM approach, we use a scenario taken from the ASCENS e-mobility case study (Chapter IV.4 [12]). In this case study, a fleet of electric vehicles (e-vehicles) is used to distribute people to their places of interest (POIs) in a city.…”

Electric Vehicles in Jordan: Challenges and Limitations

“…It includes all vehicles (bikes, cars, buses, and trains) with electric powertrain technology. In the transportation system, EV infrastructure resources, such as charging stations, parking lots, and roads, represent the supply side that electric vehicle owners (as the demand side) will take advantage of [7]. There are many dynamic factors (location, time, and scale) that could affect EV infrastructure resources and restrict the travel demand by EV drivers [8].…”