State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach
Bibliographic record
Abstract
The traditional machine learning approach requires substantial computational resources which are scarce in the embedded devices. Recently, the confluence of Edge computing with IoT has enabled resource constrained embedded devices to implement machine learning algorithms. TinyML, with its emphasis on integrating machine learning into embedded systems, seeks to move the end users away from high-performance machines, towards devices with limited resources and power. The present work investigates the estimation of state of charge (SoC) of Lithium Ion (Li-ion) batteries focussing on data-driven methodologies developed during the last five years. This paper mainly focusses on the relationship between dataset characteristics and data stationarity, exploring battery behaviour prediction and related dataset comprehension techniques. A Long Short-Term Memory (LSTM) network, a variant of Recursive Neural Networks (RNN), is utilised for SoC estimation. A 1C rating standard is implemented to comprehend the charge and discharge properties of a Li-ion battery. This study includes hardware evaluation as well as Monte Carlo simulation analysis in circuit component design. The experimental results provide a Mean Absolute Error (MAE) of 0.0630, a Mean Squared Error (MSE) of 0.0107, and a Root Mean Squared Error (RMSE) of 0.1033, confirming the efficacy of the proposed methodology. These results illustrate the accuracy and reliability of SoC estimation model. In conclusion, the proposed data-driven technique can lead to improved data security, reduced latency and cost in the estimation of SoC for Li-ion batteries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".