TY - GEN
T1 - Electric Vehicle Growth Analysis and Forecasting Using Hybrid Machine Learning and Time-Series Models
AU - Sudhanva Kalkura, S.
AU - Panwar, Arav
AU - Kishore Raj, N. G.
AU - Vibha, null
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Electric vehicle (EV) adoption has accelerated globally due to environmental concerns, policy incentives, and advances in battery technology. This study proposes a hybrid forecasting framework that integrates machine learning and time-series models to predict EV registrations. Baseline models including XGBoost, ARIMA, and ARIMAX are complemented by Prophet and Vector AutoRegression (VAR) to capture long-term trends and multivariate dependencies. Granger causality analysis is used to examine the influence of external factors such as fuel prices and charging infrastructure. The results indicate that VAR achieves the lowest prediction error, while Prophet effectively captures long-term adoption trends, supporting data-driven policy and infrastructure planning.
AB - Electric vehicle (EV) adoption has accelerated globally due to environmental concerns, policy incentives, and advances in battery technology. This study proposes a hybrid forecasting framework that integrates machine learning and time-series models to predict EV registrations. Baseline models including XGBoost, ARIMA, and ARIMAX are complemented by Prophet and Vector AutoRegression (VAR) to capture long-term trends and multivariate dependencies. Granger causality analysis is used to examine the influence of external factors such as fuel prices and charging infrastructure. The results indicate that VAR achieves the lowest prediction error, while Prophet effectively captures long-term adoption trends, supporting data-driven policy and infrastructure planning.
UR - https://www.scopus.com/pages/publications/105042323954
UR - https://www.scopus.com/pages/publications/105042323954#tab=citedBy
U2 - 10.1109/AIDE69088.2026.11544508
DO - 10.1109/AIDE69088.2026.11544508
M3 - Conference contribution
AN - SCOPUS:105042323954
T3 - 2026 International Conference on Artificial Intelligence and Data Engineering, AIDE 2026 - Proceedings
SP - 392
EP - 397
BT - 2026 International Conference on Artificial Intelligence and Data Engineering, AIDE 2026 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2026 International Conference on Artificial Intelligence and Data Engineering, AIDE 2026
Y2 - 5 February 2026 through 7 February 2026
ER -