Hybrid Control Scheme for More Efficient Charging of Two Electric Vehicle Batteries Simultaneously
Bibliographic record
Abstract
The paper proposes a hybrid control scheme for more efficient charging of two electric vehicles (EV) simultaneously. In this work, a triple active bridge (TAB) is controlled using a hybrid scheme comprising of proportional-integral (PI) and deep reinforcement learning (DRL) algorithm to improve the charging efficiency of two electric vehicle batteries. The DRL part uses the Deep Q-Network (DQN) algorithm to balance the effects of varying parameters between the two EV batteries. The control model is trained following the Markov Decision Process (MDP). Battery parameters namely voltage, current, temperature, state of charge (SoC) and state of health (SoH) form the state-space (St). Phase shift ratios, duty cycles and the switching frequency associated with the active bridges of the converter form the action space (at). Charging efficiency and voltage stability form the reward function (rt). Simulation results indicate a slight increase in power efficiency of battery charging using the proposed method. The study contributes to the advancement of EV charging technologies, emphasizing cost savings, space savings, efficiency gains, and showing versatility with multiport charging.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".