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Global energy trends are experiencing a profound transformation, and the future of transportation will boost sustainable development by controlling energy production and consumption while limiting vehicle emissions. Hence, Electric Vehicles (EVs) can substantially influence energy consumption trends by addressing potential environmental hazards. In the coming decades, Artificial Intelligence (AI) based systems will play a crucial role in future EVs' overall energy management systems. Advanced electric vehicle technology and intelligent modules will lead the automotive powertrain architecture. Numerous barriers, such as government support in certain regions, user compatibility, vehicle limitation, battery technology, and charging infrastructure, limit electric vehicle expansion. Therefore, the current state and emerging trends in this area are a matter of concern for increasing the expansion of electric vehicles. This study presents the existing charging technologies and compatible standards that may assist in its adaptability. It also examines the applications of artificial intelligence in electric vehicle development to render a collectively smarter package for EVs. The current level of development, in accordance with the paper's purpose, is to deduce the adaptability of EVs to certain obstacles. Hence, the categorization of methodologies and standards may be investigated and improved by scholars at a later time.

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power.