A Comparison Study of Unidirectional and Bidirectional Recurrent Neural Network for Battery State of Charge Estimation
Bibliographic record
Abstract
An accurate state of charge (SOC) estimation is important for ensuring the safe operation of electric vehicles (EVs). Recurrent Neural Networks (RNNs), known for their time-sequence capabilities, offer advantages in battery SOC estimation. Bidirectional RNNs (BiRNN), in particular, have recently been introduced in this domain. Conventional testing methods for BiRNN often utilize the entire dataset, allowing the model to access future data for present SOC estimation, which is impractical for real-world applications. To address this issue, this paper proposes a real-time testing method that restricts the BiRNN to historical data by employing a sliding window. For comparison, the BiRNN is also tested with a same-sized sliding window containing both historical and future data. Furthermore, this paper provides a comparative evaluation of unidirectional RNNs and BiRNNs for SOC estimation. The results reveal that the performance of BiRNNs under the historical data scenario is similar to unidirectional RNNs, with a minor improvement of an average 0.08% mean absolute error (MAE) on the testing dataset. When exposed to future data, BiRNNs demonstrate an average MAE improvement of 0.25%.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".