Ensuring Compliance and Reliability in EV Charging Station Management Systems: A Novel Testing Tool for OCPP 1.6 Messages Conformance
Bibliographic record
Abstract
The Open Charge Point Protocol (OCPP) has been considered the de-facto standard for communication between charge points and the central management system of the charge points.This paper presents a novel messages testing tool based on OCPP 1.6 to evaluate the conformance of the central system to the field category definitions specified by OCPP.The authors propose the test method, discuss the test case scenarios (positive and negative) used by the messages testing tool, and describe how to set up a test case to examine the system under test.Additionally, this study highlights the difference between the messages testing tool and the OCPP 1.6 Compliance Testing Tool (OCTT) provided by the Open Charge Alliance (OCA).To test its reliability, the message testing tool has been applied to an open-source central system platform.The result shows that the system under test is able to pass 100% of the positive test case scenarios, but only 33% of the negative test case scenarios.It is worth noting that similar results may occur in other central system platforms as well.The study's findings underscore the significance of using comprehensive messages testing tools during the development and release of central system platforms.This ensures conformance to the OCPP field category definitions and promotes reliable interoperability between charge points and central management systems.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".