Optimisation de la recharge des véhicules électriques (VÉ) dans les sites hors réseau
Bibliographic record
Abstract
Ce mémoire porte sur l’optimisation de la recharge des véhicules électriques (VE) dans les sites hors réseau, notamment dans les pourvoiries québécoises, en exploitant les surplus d’énergie solaire. Face à l’augmentation de la demande de recharge de VE et à la dépendance persistante aux génératrices diesel dans les sites isolés, ce travail propose une approche intégrée et durable. La méthodologie s’appuie sur la modélisation technico-économique avec HOMER Pro pour simuler différentes configurations hybrides (photovoltaïque, onduleur, batterie, génératrice), combinée à une validation électrique avec ETAP. Trois pourvoiries types ont été analysées. Les résultats montrent que l’intégration de bornes de recharge dynamique, couplée à un surdimensionnement stratégique des systèmes photovoltaïques, permet de maximiser l’utilisation locale de l’énergie renouvelable, de réduire les coûts énergétiques, la consommation de carburant et les émissions de gaz à effet de serre. Pour deux des trois pourvoiries, un scénario optimal a pu être identifié, alliant performance technique, rentabilité économique et impact environnemental positif. En revanche, la troisième pourvoirie ne présentait pas de potentiel d’optimisation significatif, soulignant l’importance d’adapter chaque solution au profil énergétique spécifique du site. Ce mémoire propose ainsi une méthode reproductible et adaptable pour accompagner la transition énergétique des sites isolés, tout en favorisant l’électrification des transports en milieu rural. This thesis focuses on optimizing the charging of electric vehicles (EVs) in off-grid locations, particularly within Québec’s outfitter sites, by leveraging surplus solar energy. In response to the growing demand for EV charging and the continued reliance on diesel generators in remote areas, this work proposes an integrated and sustainable approach. The methodology combines techno-economic modeling using HOMER Pro to simulate various hybrid configurations (photovoltaics, inverter, battery, generator) with electrical validation through ETAP. Three representative outfitter sites were analyzed. The results show that integrating dynamic charging stations, coupled with a strategic oversizing of photovoltaic systems, maximizes local use of renewable energy, reduces energy costs, fuel consumption, and greenhouse gas emissions. For two of the three sites, an optimal scenario was identified, combining technical performance, economic viability, and positive environmental impact. In contrast, the third site showed no significant optimization potential, highlighting the need to tailor each solution to the site’s specific energy profile. This thesis thus proposes a reproducible and adaptable method to support the energy transition of remote locations while promoting the electrification of transportation in rural areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".