Driving Mode Predictor-Based Real-Time Energy Management for Dual-Source Electric Vehicle
Bibliographic record
Abstract
To minimize battery aging of electric vehicles (EVs), it is paramount to manage efficiently their energy consumption. An energy management strategy (EMS) has recently been developed, where batteries and supercapacitors (SCs) are coordinated as a function of the driving mode which is determined manually by the driver. In the present article, we improve the EMS by developing a driving mode predictor (DMP) that determines automatically in real-time the driving mode simply from the speed history of the vehicle. The DMP is designed using supervised learning (SL), a branch of machine learning (ML). A strength of our approach is that it is applicable to predict the driving modes of any EV. To predict the driving modes during a trip made by a given EV, the DMP needs to follow the speed evolution of the EV and know its maximum reachable speed. The integration of the DMP into the EMS results in an enhanced EMS called DMP-based EMS that determines automatically in real-time the power to take from each source of energy of the EV as a function of its speed history. The results obtained from real driving cycles confirm the prediction quality of the DMP and the energy efficiency of the proposed DMP-based EMS.
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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) | 0.000 | 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) | 0.001 | 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".