Routing EV Users Towards an Optimal Charging Plan

Abstract

In this work we address the efficient operation of public charging stations. Matching energy supply and demand requires an interdisciplinary understanding of both the mobility of electric vehicle (EV) users and the load balancing mechanisms. As a result of existing mobility studies, we propose in this work a routing service for searching and reserving public charging spots in the neighborhood of a given destination. When comparing the search results for direct drive with those for a multimodal route (using driving, walking and public transport) in an urban environment, we obtain for the latter significantly more charging options in particular at low e-mobility penetration levels, at a cost of slightly longer trip duration. Further contributions address the schedule optimization, that, due to the proposed distributed architecture, can be performed independently at each public charging station. We formulate an integer program for the controlled charging and compare results obtained both with the exact and with a greedy heuristic method.

Multimodal routing and energy scheduling for optimized charging of electric vehicles

“…The machine scheduling problem described in detail in (Bessler, Grønbaek, 2012) aims at shifting the actual charging intervals of all the plugged-in EVs such that the total consumption does not exceed the available power at any moment. The solution is relevant not only for public charging stations but also for private or corporate EV parking lot.…”

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

“…The test environment and the simulation infrastructure described in this article have been developed incrementally during several years of collaboration among heterogeneous academic research groups, working in close contact with industries in the context of European Projects. The timespan of the research ranges from 2008, when the techniques for the representation of machine interpretable information have been studied in the SOFIA 3 project, to the present when, in the European project Arrowhead, 4 the technology readiness level of the implemented systems is being incremented and the deployment of the simulated infrastructure in the real world is being actuated by using performing technologies to manage the service layer and the big data generated by the many heterogeneous interacting entities. Among the project under which the vision and the test prototype have been conceived and developed, it is relevant to cite also Internet of Energy (IoE) 5 and the Knowledge and Inno-vation Community of the European Institute of Innovation and Technology (EIT) ICT project of 2014 ''Planning Tool for EV Deployment and Related User Centric Services'', where the collaboration with industrial partners like ENEL, Siemens, Centro Ricerche Fiat (CRF) and Eurotech helped the academic groups to be grounded on the specific industrial needs and requirements.…”

“…With the advent and the diffusion of smart devices, such a solution has been largely fostered. In [3] a re-routing service has been introduced, largely using the concept of reservations and load balancing showing that, at the cost of a slightly longer trip, a correct vehicle scheduling results in an overall benefit. Compared with this approach, our proposal intended to completely yield the decision on which station to choose to the user.…”

A tour recommendation service for electric vehicles based on a hybrid orienteering model

“…Copyright 2013 ACM 978-1-4503-1656-9/13/03 ...$10.00. the visiting order affects total driving distance and waiting time [1]. Waiting time is the time interval drivers cannot do anything but wait for their EVs to be charged.…”