Deep Reinforcement Learning for Intelligent Energy Management Systems of Hybrid-Electric Powertrains: Recent Advances, Open Issues, and Prospects
Bibliographic record
Abstract
The hybrid-electric powertrain presents an immediate solution to energy and environmental challenges encountered within the realm of transportation. Targeting the optimization of hybrid-electric powertrains, deep reinforcement learning (DRL) has been intensively and increasingly investigated to develop intelligent energy management systems in the context of augmented vehicular and traffic information. After a brief introduction to the Markov Decision Process and DRL, this paper presents a comprehensive survey of the recent advancements in DRL-based energy management. The survey categorizes the progress based on the various roles that DRL plays in energy management systems, highlighting the flexibility and advantages of integrating DRL for achieving energy efficiency, safety, and reliable performance. Furthermore, the study concludes with an analysis of open issues and future prospects, including the learning and application of DRL-based energy management strategies, development of novel DRL algorithms, and integration of DRL-based energy management in intelligent and sustainable transportation contexts.
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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 | 0.001 |
| 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 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".