Energy Management Systems for Electrified Powertrains: State-of-the-Art Review and Future Trends
Bibliographic record
Abstract
Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electrified powertrain, are a much simpler version of the theoretically developed EMS. Such simplification is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simplification should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simplification must be effective to create a versatile and utilitarian EMS. Hence, it is comprised of rigorous analysis of results obtained from theoretical EMS under various driving scenarios. This review paper broadly categorizes most of the reported utilitarian EMSs into three major categories and discusses the processes of simplification associated with each category. The utilitarian EMSs are classified based on their dependence on either online computation or offline pre-computation or even both for spewing control decisions. The paper delineates the chronological steps of a utilitarian EMS development, starting from theoretical background, process of simplification, validation through model-in-the-loop, software-in-the-loop, hardware-in-the-loop simulation, dynamometer test, and on-road performance validation. Future trends of on-board EMSs are also discussed before the conclusion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".