A Review of the Current Status of Global Electric Vehicle Charging Infrastructure Development
Bibliographic record
Abstract
Our study reviews the current status of global electric vehicle (EV) charging infrastructure development, emphasizing policy drivers, market dynamics, and technological advancements in North America, Europe, Asia-Pacific, and other regions. By referencing recent research and survey data, the study identifies the primary challenges faced by the current charging infrastructure, including high construction costs, lack of standardization, grid load pressure, low usage efficiency, and insufficient policy support. Specifically, by the end of 2023, the global number of fast charging stations reached 50,000, with an annual growth rate of 50%; China's public charging stations exceeded 1.3 million, with a 44% annual growth rate; the United States had 150,000 public charging stations, while Canada had 30,000. In Europe, the number of public charging stations exceeded 400,000, with Germany having 120,000, the Netherlands having 150 stations per 100 square kilometers, and Norway having 20,000. The study also examines recent advancements in charging technology, such as fast charging stations above 350kW, wireless charging technology, the promotion of ISO 15118 charging standards, and the application of smart grids and energy management systems. Despite numerous challenges, the development of EV charging infrastructure is experiencing unprecedented opportunities. Moving forward, it is essential for governments, enterprises, and research institutions to enhance policy support, technological innovation, market incentives, and international cooperation to collectively improve charging infrastructure and advance the EV market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".