Enabling EV Roaming Through Cascading WebSockets in OCPP 1.6
Bibliographic record
Abstract
Currently, there are four major protocols for EV roaming that support electric vehicle drivers to access charge points from different networks with a single user registration through roaming agreements between charge point operators (CPOs) and mobility service providers (MSPs): the Open Clearing House Protocol (OCHP), the Open InterCharge Protocol (OICP), the eMobility Inter-operation Protocol (eMIP), and the Open Charge Point Interface (OCPI).These protocols facilitate data exchange between CPOs and MSPs using a roaming hub or peer-to-peer connections.On the other hand, the Open Charge Point Protocol (OCPP) is the standard protocol widely used for communication between the charge point and the central system within the CPO's internal system.OCPP has been integrated into many charge point products today, where OCPP 1.6 supports Simple Object Access Protocol (SOAP) and JavaScript Object Notation (JSON) data format over the WebSocket, with the charge point acting as the WebSocket client and the central system as the WebSocket server.The aim of this study is to further enhance the functionality of OCPP by integrating the role of the WebSocket client into the central system to support EV roaming for EV drivers.This new approach describes the architecture that includes the actors and their roles, which are the Charge Point that delivers energy to electric vehicles, the Central System that manages the Charge Point and requests for EV roaming, and the National Access Point that acts as a roaming hub in the proposed EV roaming system.Additionally, three simulation models have been created, each representing an actor and their role in the proposed system.The feasibility and effectiveness of the proposed EV roaming system are evaluated through experiments during high traffic load conditions of a network using the simulation models and actual charge point products.The experiment scenarios specifically focus on cases related to user authorization and billing.This study only concern on the time consumption for user authorization.The results confirm that the proposed EV roaming system can be implemented based on the enhanced functionality of the OCPP, with the average time for user authorization over five attempts range between 3 ms for the simplest scenario one to 2200 ms for the most complex scenario four, which can be considered quite impressive.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".