Intelligent Energy Management System of Electric Vehicles using Botox Optimization based Fuzzy Logic Algorithm
Bibliographic record
Abstract
Electric vehicles (EVs) are attracting and growing up in many countries worldwide. Only battery storage system is the main power source in the EVs. An ultracapacitor is also used along with battery storage system. Hence, the energy management system is a very crucial and highly priority task in any EV for their best performance and life time. Many converters are included in EV for various applications. Hence, Fuzzy logic controllers based control methods are developed in this paper. However, conventional fuzzy logic controllers are having their limits, hence Botox optimization algorithm (BOA) is developed to train the fuzzy logic controllers. These strategies predominantly depend on the specialized knowledge and experience of professionals in the field. By leveraging fuzzy logic, these control methods can effectively handle the uncertainties and complexities associated with energy distribution and storage in EVs, leading to improved performance and reliability. This advanced control mechanism is designed to enhance various performance metrics including energy consumption, lithium battery output current, and peak power. Various results validate the flexibility of the BOA-based fuzzy energy management system in effectively distributing power across different driving conditions. Comparative analyses are performed regarding the power-sharing capabilities among proposed BOA-fuzzy (BOA-F), IWO-F, and WIO-F strategies. The primary goal of this research is to extend the lifespan of the battery by reducing both the output power and overall energy consumption. The BOA-F method enhances the battery’s SoC to $\mathbf{1 5. 6 \%}$ in comparison to the other methods. This improvement reduces the battery’s charging and discharging rates, thereby prolonging the lifespan of the battery while complying with the SoC limitations of the ultracapacitor.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".