Towards Cognitive EV Charging Stations Enabled by Digital Twin and Parallel Intelligence

Abstract

Smart autonomous systems are expected to be cognisant, taskable, adaptive and ethical. Digital twin (DT) and parallel intelligence (PI) techniques are ideal candidates to further advance the smart autonomous systems to the next level. The DTPI techniques are under fast deployments in both the energy and transportation sectors due to the inevitable transition from fossil fuel vehicles to electric vehicles (EVs). The dramatic increase in EV charging demand yields a huge power supply gap. Currently, the low coverage and outdated management system of charging infrastructure have led to poor user experience and increased user range anxiety. To this end, we propose a cognitive charging station architecture for future charging infrastructure, which consists of power generation network, energy storage network, and charging network. DTPI techniques enable the cognitive charging stations to provide smart functions such as energy management, energy storage system health management, load management, intelligent maintenance, and smart user services.