An outlook of electric vehicle daily use in the framework of an energy management system
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present a prospective study of sustainable mobility in the framework of a supporting energy management systems (EMS). Technological advances are still required, namely electric vehicles (EV) endowed with improved EMS in order to increase their performance by making the most of available energy storage technologies. As EVs may be seen as a special domestic load, EMS are proposed based on demand-sensitive pricing strategies such as the Energy Box discussed in this paper. Design/methodology/approach – The study presents an overview of electric mobility and an urban EV project, with special focus on the utilization of its energy sources and their relation with the energy demand of a typical urban driving cycle. Results based on the ECE 15 standard driving cycle for different free market electricity tariffs are presented. Findings – The analysis based on present Portuguese power and energy tariffs reveals that it is highly questionable whether the resulting profit will be enough to justify the potential inconveniences to the vehicle user, as well as those resulting from the increased use of batteries. Practical implications – The conclusions indicate that more studies on the trade-offs between grid to vehicle and vehicle to grid schemes and electricity pricing mechanisms are needed in order to understand how the utilization of EVs can become more attractive in the end-users’ and utilities’ perspectives. Originality/value – The paper proposes an approach for future electricity tariff behavior that could be applied to EVs in order to understand whether or not their grid integration in charge and discharge situations would be beneficial for end-users and utilities, in the framework of smart energy management technologies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".