Modeling, optimization and environmental assessment of electrified marine vessels
Bibliographic record
Abstract
Electrified Vehicles (EVs), including Hybrid Electric Vehicles (HEVs) and Pure Electric Vehicles (PEVs), can provide substantial improvements in energy efficiency, emission reduction, and lifecycle cost over conventional vehicles solely powered by Internal Combustion Engines (ICE). Progress on electrification of marine vessels has been made, but the pace has been impacted by factors such as the different operational load profile of vessels, relatively small production levels and longer or varied lifetimes. In this dissertation, hybrid electric and pure electric propulsion system designs for fishing boats and passenger ferries are studied based on in-field acquired operational data. A new integrated marine propulsion system modeling and simulation method and a dedicated mobile data acquisition system have been introduced to analyze the energy efficiency, emission reduction, and lifecycle costs of new or retrofitted fishing boats and passenger ferries with hybrid electric and pure electric powertrains. Following the automotive industry Model Based Design (MBD) approach, modeling and simulation of electrified vessels using the acquired operation profile have been carried out using backward and forward-facing methods. Series hybrid electric and pure electric powertrain system designs with powertrain component models and rule-based system control, including a properly sized electric Energy Storage System (ESS) with a Supercapacitor (SC) or battery, have been studied. The total CO2 equivalent (CO2e) or Greenhouse Gas (GHG) emissions and lifecycle costs of various new, electrified vessel propulsion system designs have been evaluated. Clean propulsion system solutions for fishing boats and passenger ferries with detailed powertrain system and control system designs are given which provide a foundation for further research and development. This dissertation also addresses the environmental impact of Natural Gas (NG) as a transportation fuel, particularly for marine transportation use. A systematic evaluation of GHG emissions is provided for the upstream fuel supply chain of natural gas fuel in British Columbia (BC), Canada. The Liquefied Natural Gas (LNG) lifecycle GHG emissions produced in both the upstream supply chain and the downstream vessel propulsion are estimated quantitatively using manufacturer data and propulsion system models of marine vessels. Extensive data have been collected from oil and gas companies that have active operations in BC to determine the upstream supply chain GHG emissions of the NG fuel under three scenarios. The energy efficiency and emissions of natural gas engines are compared with traditional diesel fuel marine engines and generators. The results obtained indicate that LNG fuel can lower CO2e by 10% to 28% with reduced local air pollutants such as sulfur oxides and particulates, compared to conventional diesel fuel. However, engine methane slip during combustion should be monitored as it can have a significant impact on the GHG emissions and so offset the environmental benefits of LNG.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".