Optimization of energy management in heavy-duty fuel cell hybrid electric vehicle conversion: enhancing lifetime and efficiency with fuzzy logic-based strategies
Bibliographic record
Abstract
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".