The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines
Bibliographic record
Abstract
The current advances in the integration of devices through the internet of things (IoT) have encouraged researchers to focus on the applications of IoT in the automotive industry. Although different achievements in the in-vehicle network analysis and traffic management have been already reviewed, a comprehensive study to bring together the main applications of the IoT in the automotive industry is required. Internal combustion engines (ICEs) are established as the most common prime-mover for cars, however, with the depleting fossil-fuel resources, the interest in the usage of fuel cells and batteries has increased. In this regard, the main goal of the current study is to evaluate the application of IoT in batteries, fuel cells, and ICEs. This paper is also centralized on different types of IoT applications and combines them with empirical articles such as Random Location Detection, Vehicle Theft Prevention, Observation of vehicle performance, and industrial management of vehicles. As an output of this comprehensive review, different usages of the IoT in the automotive sector will be clarified. Also, this article can be considered as a basis for advancing the recent implementation of the IoT in the fuel cell, battery, and ICE domains.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".