Abstract: The rapid growth of electric vehicles (EVs) globally and in Malaysia has raised significant concerns regarding the adequacy and spatial imbalance of charging infrastructure. Despite government incentives and policy support, Malaysia’s charging network remains insufficient and unevenly distributed, with major urban centers having better access than rural and highway regions. This paper proposes a data-driven approach to optimize EV infrastructure planning by employing a hybrid CEEMDAN-XGBoost model for accurate EV ownership fore-casting and GIS-based spatial optimization for strategic charger deployment. The model achieved superior performance compared to baseline models, with the lowest prediction errors (RMSE: 120; MAE:38;MAPE: 5.6%). Spatial analysis revealed significant infrastructure gaps in underserved regions, guiding equitable and demand-aligned station placement. The results provide valuable insights into future EV distribution and inform policy recommendations for scalable, data-driven planning across Malaysia.

Ouyang Mutian, Guo Maobo, Yu Tianzhou, Liu Haotian and Yang Hanlin. “Big Data-Driven Charging Network Optimization: Forecasting Electric Vehicle Distribution in Malaysia to Enhance Infrastructure Planning”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.4 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160499

@article{Mutian2025,
title = {Big Data-Driven Charging Network Optimization: Forecasting Electric Vehicle Distribution in Malaysia to Enhance Infrastructure Planning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160499},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160499},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Ouyang Mutian and Guo Maobo and Yu Tianzhou and Liu Haotian and Yang Hanlin}
}