A survey on authentication protocols of dynamic wireless EV charging
Bibliographic record
Abstract
Electric Vehicles (EVs) are considered the predominant method of decreasing fossil fuels as well as greenhouse gas emissions. With the drastic growth of EVs, the future smart grid is expected to extensively incorporate dynamic wireless charging (DWC) systems, a significant advancement over traditional charging methods. DWC, offering the unique ability to charge vehicles in motion, introduces new infrastructures, complex network models and consequently, a massive attack surface. To accomplish the goal of such an enormous smart grid accompanying DWCs, the security of EV charging infrastructures has become a deciding factor. EV charging is vulnerable to cyberattacks as it has many attack vectors and many challenges to combat. Unlike the traditional charging services provided in a typical static charging station, the DWC has a complex network architecture which makes it vulnerable to many forms of cyberattacks. Authentication plays a crucial role in safeguarding the frontline security of this ecosystem. However, within the domain of DWC, the current academic landscape has seen limited attention dedicated to authentication protocols. This background signifies the necessity of a comprehensive survey to cover the authentication protocols of dynamic wireless EV charging environments. This review paper examines the security requirements and the network model of the DWC, providing comprehensive insights into existing authentication protocols by scrutinizing a proper classification. Furthermore, the paper addresses existing challenges in authentication schemes within DWC and explores potential future research tendencies aiming to strengthen the security framework of this emerging technology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".