Plug-in Electric Vehicles (PEVs) provide new opportunities to reduce fuel consumption and exhaust
emission. PEVs need to draw and store energy from an electrical grid to supply propulsive energy for
the vehicle. As a result, it is important to know when PEVs batteries are available for charging and
discharging. Furthermore, battery energy management and control is imperative for PEVs as the vehicle
operation and even the safety of passengers depend on the battery system. Thus, scheduling the grid
power electricity with parking lots would be needed for efficient charging and discharging of PEV
batteries. This paper aims to propose a new intelligent battery energy management and control scheduling
service charging that utilize Cloud computing networks. The proposed intelligent vehicle-to-grid
scheduling service offers the computational scalability required to make decisions necessary to allow
PEVs battery energy management systems to operate efficiently when the number of PEVs and charging
devices are large. Experimental analyses of the proposed scheduling service as compared to a traditional
scheduling service are conducted through simulations. The results show that the proposed intelligent
battery energy management scheduling service substantially reduces the required number of interactions
of PEV with parking lots and grid as well as predicting the load demand calculated in advance with
regards to their limitations. Also it shows that the intelligent scheduling service charging using Cloud
computing network is more efficient than the traditional scheduling service network for battery energy
management and control.