Modelling Future Growth of Electric Vehicles: An Analytical Study of Sales Trends and Adoption Patterns using Artificial Intelligence

This paper analyzes the use of electric vehicles (EV) through predictive sales
models to predict market trends and future growth. The study uses a wideranging dataset from 2014 to 2023, and it applies more sophisticated techniques
such as time-series forecasting and machine learning (ML) in order to forecast
the sales of EV for the year 2024. The research has been able to cover essential
factors that affect EV sales, including inter-state, inter-class, inter-category, and
inter-type variations, thereby understanding market dynamics at a micro-level. It
has also taken into account temporal patterns and regional variations in its
findings. The results of this study are to inform the policymakers,
manufacturers, and other stakeholders in optimizing their production planning,
resource allocation, and investments in infrastructure. In addition, the analysis
highlights the need for data-driven approaches toward gaining insight into
changing consumer preferences and overcoming problems related to EV
adoption. The outputs not only enhance predictive sales but are also a strategic
guide for ushering in the sustainable growth of the EV sector while integrating it
with the best-fit, efficient energy systems.