Hybrid Energy Storage System State-Of-Charge Estimation Using Artificial Neural Network For Micro-Hybrid Applications
Bibliographic record
Abstract
Using experimental data from a hybrid energy storage system (HESS) composed of two 12V batteries in parallel 60Ah Lead acid (LA) and 8Ah Lithium Iron Phosphate (LFP)–a machine learning approach known as feedforward backpropagation artificial neural network (BPNN) was developed to estimate the state-of-charge (SOC) of both batteries using only one neural network structure. In order to minimize the SOC estimation error the BPNN was trained using a set of five different homologation automotive drive cycles and tested on a sixth drive cycle known as worldwide harmonized light vehicles test cycle (WLTC) to compute the estimation accuracy. A rootmean- squared error (RMSE) of 0.33% and 0.84% is obtained over the test cycle for the LFP and the LA batteries, respectively. The results were then compared to the estimation obtained from a commercially available battery management system (BMS) showing better performance for the proposed approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".