Prediction of Remaining Useful Life of Lithium-Ion Battery Packs of Autonomous Vehicle with Incremental LSTM Neural Networks
Bibliographic record
Abstract
Predicting the Remaining Useful Life (RUL) is critical in lithium-ion batteries for efficiency and timely replacement. There are several approaches for RUL estimation in lithium-ion batteries, such as model-based, data-driven, and hybrid approaches. Data-driven methods have gained popularity due to their lower complexity and adaptability. This study investigates Long Short-term Memory (LSTM) networks for RUL estimation in lithium-ion batteries. The first part of the study examines the effect of different parameter changes, such as hidden nodes and window size, in LSTM networks. The investigation reveals that increasing the number of hidden nodes before encountering overfitting improves prediction accuracy and lowers the Root Mean Square Error (RMSE) from 0.2 to 0.03. This study also involves selecting an optimal window size to enhance the overall effectiveness of the LSTM model. Moreover, this paper proposes an incremental LSTM model for time series forecasting that incorporates newly available data at each time step and updates itself to make better predictions of future capacity values. The proposed incremental LSTM model improves the RMSE by 17.66% compared to the baseline LSTM model. This technique is valuable for RUL estimation in lithium-ion batteries and is crucial for autonomous vehicles.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".