Efficiency for Large Fleets: An Analysis of How Industrial Customers Use Electric and Hybrid Vehicles
Bibliographic record
Abstract
This paper presents methods and results which allow an analysis of relevant driving parameters of hybrid and electric vehicles. In order to gain crucial insights into how industrial customers use hybrid and electric vehicles, the paper investigates the following parameters: information about odometer, charging processes and battery charging levels. The data used for this purpose was provided by the Canadian fleet management company Geotab Inc. They were evaluated by means of ‘Google BigQuery’ and the statistics programme ‘IBM SPSS Statistics’. It turned out that correlations between ‘charging time’ and ‘battery charging level’ exist, as well as between ‘battery level’ and ‘distance per day’. One of the main questions in the present study asks whether long charging times of car batteries lead to decreased average battery charging levels. As a result of this study, the longer a hybrid vehicle is charged per day, the lower sink its average battery charging level. The findings of this research help managers of car fleets to enhance their existing fleet management for establishing more efficient fleets with respect to ecological and economical aspects. Our research is particularly significant as this will save money across such fleets worldwide, and at the same time, preserve the environment as much as possible. Keywords: fleet management, electric and hybrid vehicles, big data, battery charging DOI : 10.7176/ISDE/10-3-03 Publication date :March 31 st 2019
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".