Transformer-Based Deep Learning Strategies for Lithium-Ion Batteries SOX Estimation Using Regular and Inverted Embedding
Bibliographic record
Abstract
The accurate estimation of Li-ion battery (LIB) states such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP) plays a pivotal role in the efficient operation of Electric Vehicles (EVs). These parameters can impact the battery’s health, driving range, and overall vehicle performance. Transformer-based artificial neural networks have shown impressive results in natural language processing (NLP) and estimation problems of many other domains. This paper presents an intensive study on the capabilities of various Transformer-based models in estimating the SOC and SOH of LIBs, the SOP is obtained based on the estimated SOC. This paper provides the following key original contributions: 1) the application of the Informer and Reformer variants of the Transformer model for the first time for SOH estimation of LIBs in EVs, 2) studying the effect of inverted embedding of iTransformers, a modified architecture of the transformers, on SOC and SOH estimation, inversion is performed on the Informer and Reformer as well; 3) applying a simple feature extraction method using partial discharge cycles for SOH estimation with Transformer-based models; 4) a new robust method is proposed for SOC estimation based on a 2-Encoder-Transformer with a one-dimensional convolutional neural network (1D-CNN) architecture; 5) the various architectures are trained, validated and tested on two real-world datasets comprising various driving scenarios and battery conditions. Comparative analysis with various deep learning architectures show impressive accuracy for estimating the SOC and SOH, leading to better SOP calculation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".