Real-Time Optimal Energy Management of Multimode Hybrid Electric Powertrain With Online Trainable Asynchronous Advantage Actor–Critic Algorithm
Bibliographic record
Abstract
An online updating framework of an energy management system (EMS) for a multimode hybrid electric powertrain is proposed via cooperation between the asynchronous advantage actor–critic (A3C)-based deep reinforcement learning (DRL) agent and the Markov chain model (MCM). In the overall framework, the DRL agent periodically updates the energy management policy. The MCM expedites the policy update process by generating plenty of probable future drive cycles using recent historical driving data and supplying them to the training process. Assisted with the MCM, the proposed A3C-based energy management framework can yield near-optimal policy for any type of unknown drive cycle in the recent future. Two types of unknown drive cycles are chosen to demonstrate the efficacy of the proposed framework. Type I unknown drive cycle is also generated from the same recent historical driving data but was not included in the training dataset. Type II drive cycle is neither known to the framework nor generated from the same historical data. In type I unknown drive cycle, the trained A3C-based EMS achieves 99% of the fuel economy obtained by the global-optimal EMS and 0.12% deviation from charge sustainability. The trained A3C-based EMS consumes 6%–12% more fuel than the global-optimal EMS for type II unknown drive cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".