Optimization of energy management in heavy-duty fuel cell hybrid electric vehicle conversion: enhancing lifetime and efficiency with fuzzy logic-based strategies
Notice bibliographique
Résumé
Transportation is a major contributor to global CO2 emissions, responsible for about 20% of the total, with energy consumption in this sector accounting for nearly a quarter of all emissions. The International Energy Agency (IEA) highlights that road transport is the largest emitter within the sector, accounting for 75% of transportation emissions in 2018, divided between passenger vehicles (45.1%) and freight trucks (29.4%). This signifies that road travel alone is responsible for roughly 15% of global CO2 emissions. The adverse effects of greenhouse gases include climate change, which leads to extreme weather, disruptions in food supply, and increased wildfires, alongside health issues such as respiratory illnesses due to air pollution. Given these significant environmental and health challenges posed by conventional vehicles, the shift towards sustainable transport options is critical. Fuel Cell Electric Vehicles (FCEVs) stand out as a viable solution, offering a clean alternative by emitting only water vapor. Highlighted by the Massachusetts Institute of Technology, FCEVs have the potential to drastically cut greenhouse gas emissions and reduce petroleum dependence without changing current driving habits. They also offer advantages over Battery Electric Vehicles (BEVs), including longer ranges and faster refueling times, making them an appealing option for a broad spectrum of uses, from heavy-duty trucks to long-distance travel(Nunez (2019)InternationalRenewableEnergy Laboratory (2011)Camacho (2022)). This thesis aims to transform a conventional Class 8 C10 Caterpillar Kenworth 2002 truck with an internal combustion engine into an electrified vehicle by replacing the gasoline engine with an Electric Motor (EM), thereby eliminating emissions. The conversion utilizes a mix of hydrogen and batteries to fuel an electrochemical process in a fuel cell, generating electricity to power the EM without harmful emissions, only producing water and heat as by-products. The system design is bifurcated into the Traction Subsystem (TS) and the Energy Storage Subsystem (ESS), with each being validated separately. The control architecture comprises local controllers for the TS and ESS, focusing on vehicle speed and fuel cell current, respectively, and a global Energy Management Strategy (EMS). Designed and simulated in MATLAB-Simulink with an Energetic Macroscopic Representation (EMR), this approach illustrates the intricate system interactions and control complexities. The EMS for the ESS is explored through three scenarios: continuous fuel cell operation, a rule-based strategy, and a fuzzy logic-based method, assessing their performance against the system’s objectives and constraints. The final part of this thesis focuses on achieving specific system objectives: reducing the vehicle’s overall weight, minimizing hydrogen consumption, and extending the battery pack’s lifetime. The Energy Storage System (ESS) is designed so that the battery pack delivers the maximum current demanded by the Electric Motor (EM) at any moment, independent of battery capacity. This design, optimized through fuel cell operation during the New European Driving Cycle (NEDC), allows for a significant reduction in the number of battery modules, halving the weight by approximately 414kg. The effectiveness of the ESS design was evaluated across three scenarios. In the first scenario, with the fuel cell (FC) continuously operating, the battery’s State of Charge (SOC) exceeded 0.7, failing to meet the objective of maximizing battery life, which requires maintaining an SOC between 0.4 and 0.7. This scenario also led to unnecessary hydrogen consumption. The second scenario implemented a simple rule-based strategy for FC current control, turning the FC on at an SOC of 0.4 and off at 0.7. However, during the NEDC, the SOC dropped to 0.27 at times, indicating a risk to battery longevity, despite reduced hydrogen use. The third scenario, employing a fuzzy-logic strategy for the EMS, successfully maintained the SOC within the optimal range of 0.4 to 0.7, thereby aligning with all system objectives, including reduced hydrogen consumption. This scenario demonstrated the superiority of the fuzzy-logic approach in optimizing system performance and achieving the intended environmental and operational benefits.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».