Optimizing Low Pass Filter Cut-off Frequency for Energy Management in Electric Vehicles with Hybrid Energy Storage Systems
Bibliographic record
Abstract
Mitigating pollution in the transportation sector necessitates the deployment of zeroemission solutions, such as electric vehicles (EVs).One significant challenge with EVs is the limited lifespan of the battery, a key and costly component.To circumvent this issue, a potential solution lies in the integration of batteries with supercapacitors to create a Hybrid Energy Storage System (HESS).This combination can notably decrease the peak current of the battery, thereby prolonging its lifespan, and ultimately, contributing to the long-term cost-effectiveness of EVs.A critical component of the HESS is the Energy Management Strategy (EMS), tasked with optimizing energy distribution.A Low-Pass Filter (LPF) serves as an uncomplicated, real-time EMS.The current study introduces a novel approach for determining the optimal cut-off frequency of the LPF, termed the Ragone Plot with Fine Tuning (RPFT).The Ragone plot provides a general cut-off frequency for the battery and drive cycle, while fine-tuning is employed to optimize it.Simulation results reveal that the RPFT method outperforms the Fast Fourier Transform (FFT) method, thereby proving its efficacy.The application of RPFT resulted in a reduction of battery peak current and battery current root mean square (BCRMS) by up to 29.80% and 9.99%, respectively.This study offers valuable insights for improving energy management in electric vehicles and underscores the potential of the RPFT method in extending battery lifespan and enhancing the costeffectiveness of EVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".