Machine Learning Driven Forecasting Of HNEV Markets: Challenges, Policies, and Pathways To Growth

The application of NEVs is essential to achieve
sustainable mobility, yet their widespread implementation is
subject to infrastructural constraints, economic considerations,
and government backing. This study seeks to present a
machine-learning-based framework to predict the future
growth of NEVs over the next five years. Using historical
information on vehicle production and refuelling
infrastructure, several machine learning algorithms were
implemented in this analysis, including AdaBoost, Gradient
Boosting, Random Forest, Ridge Regression, and XGBoost.
Based on the findings, the market for electric and hybrid
vehicles is expected to grow significantly in the coming years,
whereas the growth of hydrogen vehicles will be much slower.
Among the machine learning algorithms mentioned above,
XGBoost yielded more accurate predictions and growth trend