Recent advancements in Artificial Neural Network-based temperature prediction and management of lithium-ion batteries: A comprehensive review
Bibliographic record
Abstract
In this comprehensive review, we meticulously examine the role of Artificial Neural Networks (ANN) in predicting and understanding the thermal behavior of Lithium-ion batteries (LIBs), with a focus on battery temperature and thermal runaway (TR) prediction. Throughout this review, A bibliometric analysis of over 200 publications between 2010 and 2024 revealed a more than 5 × growth in ANN-based thermal modeling studies in the last five years. We quantitatively compare recent models including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Feedforward Neural Networks (FFNN), with reported root mean square errors (RMSE) ranging from as low as 0.055 °C (LSTM) to 1.3 °C (FFNN) in surface temperature prediction tasks. Moreover, we have identified an emerging trend in the design of hybrid models such as LSTM-CNN, which achieves TR detection of up to 27 min in advance. Therefore, emphasizing the advantage of hybrid modeling in battery thermal safety. In parallel, this review highlights the current state-of-the-art of Physics informed Machine Learning (PIML) that integrates domain knowledge and governing physical laws with neural networks and achieves average RMSE as low as 0.12 ° C in temperature prediction. Furthermore, PIML models reduce drift error by up to 40% under dynamic conditions, while reducing computation time by up to 250 times less than purely ML data-driven models. This highlights the transformative role PIML can provide in onboard and real-time BTMS. Despite this progress, a critical research gap remains, such as the underutilization of GRU based models, limited core temperature prediction, and a shortage in publicly available battery datasets with internal thermal measurement. This review concludes by providing a curated summary of benchmark datasets and model evaluation to serve as a valuable reference for researchers in the domain of next-generation BTMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".