Energy Management Systems for Electrified Powertrains: State-of-the-Art Review and Future Trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it