Reinforcement Learning-Based Approaches to Energy Management of Hybrid Energy Storage Systems in Electric Vehicles
Bibliographic record
Abstract
An effective and efficient management of energy storage systems (ESSs) is critical for the successful deployment of electric vehicles (EVs). Hybrid energy storage systems (HESS), consisting of two or more energy storage devices, have been proposed as a solution to improve the performance of EV batteries and extend their lifespan. Various methods have been proposed to manage the HESS, including rule-based approaches and optimization algorithms. In spite of the advantages of these methods, they have limitations when it comes to handling the complex and dynamic energy management problems of HESS in real time. Therefore, reinforcement learning (RL) algorithms have been proposed as a promising solution to address these challenges. This paper summarizes the recent research on the application of RL to the energy management of HESSs in EVs. As a result of the review, we have been able to highlight how this method is capable of improving the efficiency and performance of HESSs. This paper discusses different aspects of RL-based EMSs, including the selection of state and action spaces, reward functions, and different algorithms used.
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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 | 0.000 | 0.001 |
| 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".