The global transition toward electric vehicles (EVs) has accelerated as a primary strategy for climate change mitigation and the achievement of net-zero emission targets. The transition to this new system encounters major technical obstacles which hinder efforts to reduce greenhouse gas emissions. The automotive industry requires rigorous research into market development and battery optimization as technology evolves. The process of electrification helps the environment but creates new challenges for power grid stability and battery safety management. EV technology has emerged as a critical field within Energy Informatics which uses data science to connect green transportation systems with economic systems. Machine Learning (ML) serves as the foundation for contemporary sales prediction which enables businesses to estimate market growth and consumer preferences with increased efficiency and lower costs. The public transit sector and personal transportation industry both adopt EV technology despite its high initial capital costs because it helps reduce global petroleum dependence. The current pace of technological development makes it hard for stakeholders to tell temporary market changes apart from long-term market growth which results in increased investor uncertainty and stock market fluctuations. The primary objective of this project is to conduct an accurate evaluation of EV market dimensions while identifying the essential factors driving expansion through data analysis. This study used predictive algorithms to analyze major worldwide datasets which included both socio-economic and socio-technical factors to determine adoption patterns. The research team created and tested regional models using data from the United States, China, and Europe to confirm their geographical applicability.
EV Market Analysis; Predictive Analytics; Machine Learning; Sustainable Transportation; Lithium-ion Battery Optimization; Data Science Perspective
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