State of Charge Estimation of Lithium-Ion Batteries: Comparison of GRU, LSTM, and Temporal Convolutional Deep Neural Networks
Bibliographic record
Abstract
Accurate state of charge (SOC) estimation of lithium-ion batteries (LIBs) is essential to ensure safe and reliable battery operation. A simple way to obtain the SOC is by integrating the charge withdrawn or inserted into the battery. However, this coulomb counting method is often inaccurate due to accumulated error associated with current sensor offset or bias error. Hence, estimators are often used to report the SOC values by monitoring the battery's measured parameters. In this study, three deep neural network (DNN) -based SOC estimators are benchmarked, including a gated recurrent unit (GRU), long short-term memory layer (LSTM), and temporal convolutional neural network (TCN). The networks are trained to estimate the SOC of a prismatic battery at five ambient temperatures ranging from -20 to 40°C. The results show that the optimum configuration of the three DNN types estimates SOC with less than 2% root mean square and 60% maximum error. The GRU shows slightly higher error and lower computational resources than the LSTM and TCN, which was most evident for challenging cases such as drive cycles at -20°C. The TCN is shown to require around 250,000 learnable parameters and thus seven times higher execution time to achieve similar accuracy as an LSTM or GRU RNN with just 3,000 learnable parameters.
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 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 | 0.000 | 0.000 |
| 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".