Anticipatory coordination of electric vehicle allocation to fast charging infrastructure

The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%. The limited range of electric vehicles (EVs) in combination with the limited capacity of current fast charging infrastructure are both causes for a limited adoption of EVs. In order to reduce the general inconvenience that EV users experience when having to wait for available fast charging stations and to lessen the danger of damaging the infra- structure by overloading it, an efficient coordination strategy is needed. This paper proposes an anticipatory, decentralised coordination strategy for on-route charging of EVs during lengthy trips in a fast-charging infra- structure. This strategy is compared to a reference strategy that uses global real-time knowledge of charging station occupation. Simulation results using a realistic scenario with real-world traffic data demonstrate that the anticipatory strategy is able to reduce the waiting times for EV users by up to 50% while at the same time decreasing the peak loads of the electricity grid caused by charging EVs by 21%.

ISBN:9783319075501

Publication year:2014

Accessibility:Open