Enhanced Performance of Bidirectional DC-DC Converters for EV Charging: Impact on Energy Efficiency and Grid Load
Bibliographic record
Abstract
A surge in the adoption of EVs is posing numerous challenges with respect to energy efficiency and stability of the grid. The existing bidirectional DC-DC converters for Vehicle to Grid (V2G) and Grid to Vehicle (G2V) operations have several drawbacks, namely loss of efficiency, uncontrolled power flow, and grid oscillations. This research proposes a control framework using Deep Reinforcement Learning (DRL) to optimize bidirectional DC-DC converters for making EV charging energy efficient and balancing the grid load. Unlike traditional Proportional-Integral-Derivative (PID) and rulebased control methods, the proposed DRL algorithm adjusts the duty cycle and switching frequency dynamically according to real-time grid conditions for optimal control of power transfer. The architecture of the proposed control model includes Deep Q-Network (DQN), where the state variables voltage, power demand, and Battery State of Charge (SoC) are employed in making charging decisions. Experimental results demonstrate that DRL optimization improves the converter's efficiency to$\mathbf{9 8. 5 \%}$, reduces switching losses by$\mathbf{6 0 \%}$, stabilizes the grid frequency, and declines peak demand by 18.3 % when compared with conventional methods. Moreover, energy consumption is reduced by 22.1 %, thus resulting in a lower carbon footprint. These results consolidate that the Artificial Intelligence (AI) power management enhances the efficiency of Electric Vehicle (EV) charging and stability of grids and hence, soars as a potential alternative towards sustainable and intelligent energy distribution in modern power systems.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".