A Novel Multi-Mode Adaptive Energy Consumption Minimization Strategy for P1-P2 Hybrid Electric Vehicle Architectures
Bibliographic record
Abstract
The presented study aims to propose a new method of driving behavior recognition using a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) in combination with an Energy Consumption Minimization Strategy (ECMS), resulting in a Multi-Mode Adaptive Energy Consumption Minimization Strategy (A-ECMS) for a P1-P2 series parallel Hybrid Electric Vehicle (HEV). Novelty is achieved by focusing on efficient driving mode switching instead of single mode optimization. Therefore, offline optimization was performed over different driving situations to gather different calibrations, which will be utilized in the hybrid propulsion system master controller with the purpose of determining the most fuel-efficient driving mode based on the current driving behavior. A LSTM RNN is used to classify the current driving behavior online based on vehicle speed, acceleration and distance per stop. This paper compares the effect of the proposed method in different driving conditions in order to investigate the benefits and applicability of such a control strategy. Simulations were performed representing a conventional engine-driven vehicle and a hybrid electric vehicle equipped with a P1-P2 series-parallel hybrid propulsion systems. Improvement in fuel consumption against a conventional vehicle of around 52% in average over all driving cycles can be achieved through this approach.
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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".