Particle Swarm Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles
Bibliographic record
Abstract
Electric vehicles (EVs) experience frequent current variations which cause current spikes and voltage drops during acceleration and braking operations. This could further escalate to excessive heat generation which degrades the battery. A hybrid energy storage system (HESS), such as a battery-ultracapacitor, can alleviate the current stress on the battery. However, an energy management strategy (EMS) is necessary to optimize the use of the HESS. This thesis proposes a particle swarm optimized fuzzy logic energy management of a battery-ultracapacitor for the General Motor (GM) EV1 drivetrain. The proposed EMS manages the power flow between the battery and ultracapacitor, with the aim of reducing battery degradation and improving energy efficiency. The proposed EMS outperforms an unoptimized FLC in terms battery stress reduction, battery degradation minimization, and lifespan, making it a promising candidate for electric cars, buses, and trucks. Its benefits include extending the battery’s lifespan and reducing replacement or maintenance costs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".