Electric vehicle trip planning integrating range constraints and charging facilities

Abstract

This paper presents a strategy for multi-modal\ud trip planning integrating the management of fully electric\ud vehicle range and charging services along the trip. The network\ud graph is modelled as the superposition of layers representing\ud different transportation means and charging infrastructure,\ud putting in evidence the interaction between the transportation\ud and electricity distribution grids. The presence of energy\ud constraints on the network nodes implies to formalize the trip\ud planning problem as a resource constrained shortest path\ud problem, and solve it through an ad-hoc decomposition\ud strategy. The proposed approach is validated through the\ud simulation of realistic test cases, showing its effectiveness and\ud potential in satisfying complex user preferences, mitigating\ud drivers perception about limited vehicle range and availability\ud of charging infrastructure, smoothing the impact of massive\ud fully electric vehicle charging on distribution grids

A Future Internet interface to control programmable networks

“…OFNIC has been published as an open source Generic Enabler instance of the FIWARE catalogue [21]. As a matter of fact one promising direction for exploiting this new technology is the application to the Smart Grid sector [26] where cloud and network resources are expected to be a key factors for the massive deployment of internet based control strategies enabling active demand services [27][28] [29].…”

On the many-to-many carpooling problem in the context of multi-modal trip planning

“…However, if accurate discrete-time measurements of the network state were available, thanks to the employment of suitably-designed widespread sensing technologies, and if such measurements were updated with a sampling period of amplitude , then the polylines and the associated quantities could be rewritten as otherwise, the solution procedure could be readily extended to the dynamic case, thus enabling also event-driven re-planning. In this respect, according to the BONVOYAGE platform architecture presented in [15] [16], the authors are focused on ensuring (i) the real-time monitoring of the dynamic present context (accounting for the multi-modal transportation network dynamics by means of a suitable multi-layer graph [15] [17]) and (ii) the exploitation of the user feedbacks provided in real-time by the users themselves (e.g., the Quality of Experience level perceived by each user during a given travel), in order to enable re-planning any time the occurrence of an event in the considered transportation network seriously compromises the feasibility of the currently selected travel solution. Indeed, the exploitation of user feedbacks and the real-time monitoring of the dynamic present context enable the closed-loop control mechanism that, by relying on similar methods as those used in [18]- [35], represents one of the most innovative features of the BONVOYAGE platform.…”

A Future Internet oriented user centric extended intelligent transportation system

“…From the conceptual point of view, ATIS consists of two main modules [3]: (a) Pre-Trip Traveller Information System (PTTIS) and (b) En-Route (also known as On-Trip) Traveller Information System (OTTIS). In applications of practical interest, ATIS can include the Multimodal Trip Planner (MTP) module (see [4]- [6]). In order to provide suitable real-time information about travel, usually data coming from several, heterogeneous sensors are gathered and subsequently processed into the Advanced Transportation Management System (ATMS) [1].…”