Optimized Power Control in Grid-Connected Electric Vehicles Using Feed-Forward Neural Networks
Bibliographic record
Abstract
In this paper, a feedforward neural network (FFNN)-based controller is designed for bidirectional voltage source inverters (VSI) in grid-connected electric vehicles (EVs) and presented. The suggested method addresses the inherent nonlinearities in EV charging systems while efficiently managing active and reactive power. Eliminating the dependency on system parameters and intricate mathematical modelling simplifies the control system and facilitates its implementation. High adaptability to nonlinearities and changing operating conditions is made possible by the FFNN-based controller's capacity to learn directly from input-output data. It addresses the issues of the tuning process and separate tuning for different sizes of batteries for conventional controllers that usually use the PI controller to generate reference voltage for pulse width modulation for inverters. The proposed controller is simulated and verified in various operating conditions using a 100 kW battery energy storage system, and the results show that the FFNN controller can track the active and reactive power properly while maintaining the voltage and frequency stable and reaching the steady state value in 0.4 seconds. The steady-state error for power is less than 0.05%, showing that the proposed controller performs precisely.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".