An EV user must be assured that power will be available when needed especially in an unfamiliar area. A route optimization approach based on Machine Learning needs to be considered to ensure that vehicle drivers are led to the nearest or their preferred e-Station.
The E-Charging Operators (equivalent of today’s petrol pump operators) need a Big Data approach to ensure their customers (end-users) satisfaction and optimum utilization of the e-Charging stations. The operator may have fast or slow charging infrastructure with their own payment terms and timings. Based on a data-centric approach the operator can offer dynamic pricing at stipulated times and manage the peak demand accordingly.
A Smart city will have various elements like power, water, etc monitored through a central control room. The Control Center will be the single source of information for administrators and decision makers. The new set of challenges faced by a Smart Grid, e-Charging network and e-Vehicle users culminate in the Smart City Control room as an indicator of traffic movement/congestion, power demand and various other related parameters. The solution need to be integrated into the Smart city control room
Over the time, cities have built the power distribution asset in phases with varied product technologies. At the time of faults in the physical infrastructure, the distribution utilities take huge time to identify and pin point the fault in the physical infrastructure (especially the cable faults, which is mix of overhead and underground infra). Also, faults in networks are catered to only after they occur. A low cost prediction solution will help in better planning for ad-hoc maintenance.