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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".