Abstract

Plug-in Hybrid Electric Vehicles (PHEV) have been promoted by providing Vehicle-to-Grid (V2G) infrastructure as a possible solution to reduce greenhouse gas (GHG) and other emissions by utilizing energy instead of oil for effective environmental management. The promising solution for reducing air pollution in cities is commonly regarded as electric vehicles, which helps to optimize the environment management more effectively, as a key to future low carbon mobility. However, their environmental benefits rely on the temporal and spatial sense of real use, and challenges such as limited range complicated for the rollout of an Electric Vehicle (EVs). This paper investigates the environmental carbon pollution in cities and control preventions using Plug-in Hybrid Electric Vehicles (PHEV). Further, the Artificial intelligence model has been introduced, which defines optimal automobile designs and the assignment of vehicles to drivers across a variety of scenarios, including minimum net life cycle expense, GHG emissions, and oil usage for effective environmental management. By designing overspent vehicle power for corresponding output, weight, and cost impact, the life cycle costs and the emission of GHG are reduced utilizing high battery swinging and replacing batteries as needed. Moreover, energy consumption (EC) and pollution have been greatly influenced by the use of energy sources in the environment. The significant energy consumption and pollution variables resulted in a large proportion of coal-fired energy. The results show that the PHEV can achieve better fuel economy by combining the proposed model with an allowable deviation from the state of the charge.

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