Real-Time Prediction of Anode Potential in Li-Ion Batteries Using Long Short-Term Neural Networks for Lithium Plating Prevention
Bibliographic record
Abstract
The fast charging technology is urgently needed for wide acceptance of electric vehicles. And the most severe issue in fast charging is the lithium plating due to the low anode potential. In order to prevent lithium plating, it is crucial to monitor the anode potential at different operating conditions. This paper proposes a long short-term memory (LSTM) neural network that predicts the anode potential by using the most commonly measured signals including battery current, voltage, state of charge, and surface temperature. The proposed LSTM neural network is fitted to the training data generated using an experimentally validated battery model. The predictions achieve high accuracy, only 3.84 mV for the maximum Root Mean Square Error (RMSE) on the driving cycles, and 3.73 mV for the RMSE on the constant charging profiles. The results demonstrate the effectiveness of the LSTM neural network in predicting the anode potential. Unlike the existing mathematical models, the data-driven approach used in this paper does not involve complicated mathematical formulation, tedious parameter tuning, or a deep understanding of the electrochemistry. Accurate estimation is accomplished by fitting the neural network to the training dataset. The trained LSTM model is also quite computationally efficient, which enables the real-time estimation.
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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 | 0.001 |
| Open science | 0.000 | 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".