The transportation industry is taking a giant leap forward towards a dependable, carbon-neutral future thanks to electric vehicles (EVs). The growing number of electric vehicles on the road is putting a strain on power networks and electrical grids caused by dispersed energy resources, particularly the EVI's demands for charging and discharging. Without proper coordination of charging and discharging, the already overburdened power distribution system would struggle to handle the large number of electric vehicles on the road. To get more people to use scheduling programs, electric vehicle owners can use dynamic pricing, a kind of demand response. Studying the timing of electric vehicle charging and discharging as well as its dynamic pricing model is, hence, crucial. There has been a lot of study on scheduling and demand forecasting models for electric vehicle charging that employ artificial intelligence, with many academics claiming that AI approaches outperform more traditional optimization techniques. The Diversified Grid Optimization Model (DGOM) is a novel method for optimizing charging facilities that aims to provide fast and efficient charging. To test its efficacy, it is cross-validated with the Dynamic Vehicle Charging Model (DVCM), a traditional charging scheme. The investigations pertaining to electric vehicle charging and discharging are categorized into scheduling, pricing, and forecasting in this paper's experimental analysis. This work finds the missing pieces of the puzzle when it comes to dynamic pricing models and EV discharge scheduling by establishing the connection between forecasting, scheduling, and pricing mechanisms. © 2025 Elsevier B.V., All rights reserved.