A Study of Cybersecurity and Resilience of Dual Active Bridge Converters in Electric Vehicle Fast Charging Systems
Bibliographic record
Abstract
The rapid adoption of electric vehicles (EVs) and the growing need for high-power EV fast charging infrastructure have led to the widespread use of advanced power electronic topologies, notably the Dual Active Bridge (DAB) converter. While DAB-based DC fast chargers offer efficient bidirectional power flow and galvanic isolation, their integration into cyber-physical systems introduces new cybersecurity vulnerabilities. Existing research primarily focuses on communication protocols (e.g., OCPP, ISO 15118), backend networks, or electric vehicle supply equipment (EVSE) firmware security, often overlooking the converter level where actual power processing occurs as a critical attack surface. This study investigates recent literature on cyber threats specific to DAB-based fast charging systems and reviews resilience strategies that enhance system robustness. Emphasis is placed on the unique attack surfaces of the DAB architecture, potential impacts on the control loop and power flow, and the integration of real-time monitoring, secure communication protocols, and anomaly detection systems. The objective is to provide the EV charging and smart grid research communities with an understanding of emerging threats at the converter and its controller level and to propose recommendations for designing cyber-resilient EV fast charging systems. The paper concludes by outlining key research gaps and offering directions for future work to support the development of secure and reliable DAB-based EV fast charging infrastructure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".