Research on Safety Evaluation Methods in the Maintenance of Hybrid Vehicles
Bibliographic record
Abstract
With increasing emphasis on environmental problems, hybrid vehicles have become an important development direction of the automobile industry, and the safety assessment in the maintenance process has been particularly valued. This study establishes a systematic hybrid vehicle maintenance safety evaluation method, focusing on three key technologies: battery management system (BMS) fault diagnosis, high-voltage system isolation, and power battery safety detection. The multisource data fusion technology is used to carry out real-time monitoring and fault warning of BMS; adopt the design concept based on high-voltage interlock to realize the effective isolation of the automotive high-voltage system; and reconstruct the battery state through the simulation model to comprehensively evaluate the health of the power battery. In the experimental part, by randomly selecting the models of the main hybrid vehicles on the market, a database was built that covers more than 100 typical faults and a number of fault diagnosis algorithms were compared and tested on the actual car. The research results show that the evaluation method has reached the industry-leading level in terms of fault detection rate, fault location accuracy, and response time. The diagnostic precision rate has increased by an average of 15% and the response time has been reduced by 30%. The research not only provides a scientific and systematic safety evaluation tool for the maintenance of hybrid vehicles but also lays a theoretical foundation for the formulation and upgrading of training standards for the automotive maintenance industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".