A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles
Bibliographic record
Abstract
Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".