Battery state of charge estimation using an Artificial Neural Network
Bibliographic record
Abstract
The automotive industry is currently experiencing a paradigm shift from conventional, diesel and gasoline-propelled vehicles into the second generation hybrid and electric vehicles. Since the battery pack represents the most important and expensive component in the electric vehicle powertrain, extensive monitoring and control is required. Therefore, extensive research is being conducted in the field of electric vehicle battery condition monitoring and control. In this paper, an Artificial Neural Network (ANN) is used for Lithium-Ion (Li-Ion) battery state-of-charge (SOC) estimation. When properly trained using the random current profile described in this paper, a single-layered Neural Network is capable of capturing the non-linear characteristics of a battery. The ANN is able to estimate a non-measurable parameter such as battery SOC level based on battery measurable parameters such as voltage and current. The ANN in this paper is trained using experimental data generated from an experimental battery using a R-RC model with SOC/OCV relationship. The SOC/OCV relationship was derived from a commercial 3.6V 3.4Ah Li-Ion battery cell. The network is trained using current, and voltage as inputs and SOC as the output. The trained network is tested using benchmark driving cycles to be capable of estimating the battery SOC with a relatively high degree of accuracy.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".