The Analysis and Prospect of CNPC's EV Charging Business Based on Machine Learning
Year: 2025
Type: conference-paper
Abstract: As electric vehicles (EVs) reshape China's energy landscape, China National Petroleum Corporation (CNPC) has entered the EV charging sector under the Kunlun i-Charge brand. This study investigates CNPC's development strategy, spatial deployment patterns, and future prospects in this emerging field. To assess long-term potential, we apply a mixed-methods approach combining spatial analysis with machine learning. A Random Forest regression model and Partial Dependence Plot (PDP) analysis are used to evaluate how charging infrastructure deployment affects projected market share in 2030. Findings show that while Kunlun i-Charge currently holds a small share (1.5%) of China's charging market, it demonstrates the highest growth rate and focuses on fast-charging stations in high-demand zones. Spatially, stations are concentrated along urban cores and transportation corridors. Machine learning results confirm that mid-2020s investments have the greatest marginal impact on future scale, while early-stage deployment provides foundational effects. The study highlights CNPC's unique structural advantages—such as land access, capital, and energy integration—positioning it for long-term impact. It offers data-driven insights into how traditional energy enterprises can transition toward integrated low-carbon infrastructure.
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7127586166