Deep Learning-Based Battery Voltage Forecasting Using Current as Input: A Multi-Stage Approach for Time Series Prediction
Bibliographic record
Abstract
This paper introduces a novel approach for forecasting the voltage of the battery by learning the deep neural networks, based on leveraging the concurrent battery current as an input variable. The methodology employs the Long Short-Term Memory model, named LSTM, which is known for its capability to handle long-range dependencies in sequential data. The approach begins with the data pre-processing, where an in-depth analysis of the battery's Equivalent Electric Circuit (EEC) parameters is performed. These parameters are crucial for accurately predicting the battery's voltage response under various conditions. Subsequently, the data undergoes normalization, feature importance determination, windowing, and is split into training and testing sets. The training subset is applied to develop the LSTM-based forecasting model. A key innovation of this approach is the use of a multi-stage forecasting technique, where the LSTM model is trained to predict one step at a time, and then feed the output back into the model for the next prediction. This approach allows the model to dynamically adjust its predictions based on the feedback received from previous steps, enabling more accurate and robust forecasting. The experimental framework evaluates the proposed multi-stage LSTM forecasting model against single-stage LSTM, and other neural networks including a dense layer model, as well as a dense layer model with activation function. The results demonstrate that the multi-stage LSTM approach outperforms other models in terms of Mean Absolute Error (MAE), indicating its superiority for forecasting the voltage of the battery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".