A Neural Network Energy Management Controller Applied to a Hybrid Energy Storage System using Multi-Source Inverter
Bibliographic record
Abstract
In this paper, a Neural Network Energy Management Controller (NN-EMC) is designed and applied to a Hybrid Energy Storage System (HESS) using the Multi-Source Inverter (MSI). Its aim is to manage the current sharing between a Li-ion battery and an Ultracapacitor by actively controlling the operating modes of the MSI. A discharge duty cycle that biases the use of one source over another is used as the control variable. To limit the battery wear and the input source power loss, an optimized solution is obtained with Dynamic Programming (DP). The NN-EMC is designed with an artificial neural network and trained with the optimized duty cycle obtained by DP. The DP/NN-EMC solution was compared to the battery-only Energy Storage System (ESS) and the HESS-MSI with 50% discharge duty cycle. Both the battery RMS current and peak battery current have been found to be reduced by 50% using the NN-EMC compared to the battery-only ESS for the New York City drive cycle.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".