Investigation of Adaptive Neuro-Fuzzy Inference System for Battery Degradation on a Charge-Sustaining Hybrid Electric Vehicle
Bibliographic record
Abstract
In this study, adaptive neuro-fuzzy inference system is investigated for battery degradation. Ten widely used drive cycles are selected to obtain a dataset for six different initial state-of-charge levels between 30%-80%. Torque demand, state-of-charge and battery capacity fade are used as the inputs of adaptive neuro-fuzzy inference system and internal combustion engine torque is selected as the output. Equivalent consumption minimization strategy is used as the dataset algorithm. Dataset size is increased by aging the battery to investigate the algorithm in further aging conditions. Four different datasets are used to investigate the algorithm for different aging conditions. Initial dataset size is increased by running each drive cycle in the dataset for 25, 50 and 100 times respectively. Results are obtained for Worldwide Harmonised Light Vehicle Test Procedure (WLTP) and New European Driving Cycle (NEDC). The WLTP case results for 100 cycles indicate 19.71% capacity fade reduction with 1.42% increase of fuel consumption. The NEDC case results for 100 cycles reveal a 23.31% reduction in capacity fade and 12.48% increase in fuel consumption.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".