A Federated Byzantine Agreement Model to Operate Offline Electric Vehicle Supply Equipment
Bibliographic record
Abstract
Providing equitable and resilient electric vehicle supply equipment (EVSE) to remote locations with limited access to the Internet infrastructure is one of the paradoxes in the decarbonization plan. In this paper, we develop an electric vehicle (EV) charging management system comprised of offline EVSEs based on a Federated Byzantine Agreement (FBA) system. To enable offline peer-to-peer (P2P) authentication between the user’s device (UD) and EVSE stations, we use a distributed certificate management based on Shamir’s algorithm and blind signing approach using the ring signature. We introduce extended FBA for charge point operator (FBA-CPO) systems and include explicit operational functions to ensure the ledger dissemination and scalability of the network. We specifically employ a pre-authorized mechanism for deferral vouchers using a pre-signed and weighted signing method. In the FBA-CPO network, each charger records all transactions on a local ledger and publicly publishes ledgers through random UD nodes. Under a generalization of the property of the federated quorum system, the proposed approach could provide a reliable network centrality with or without offline EVSEs. Eventually, we examined the performance of the proposed model using a system of real devices and their digital twins. Results show the FBA-CPO satisfies all functional and cross-cutting requirements for a consistent transnational mechanism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".