Feedforward and NARX Neural Network Battery State of Charge Estimation with Robustness to Current Sensor Error
Bibliographic record
Abstract
State of charge (SOC), the remaining usable charge of the battery divided by its nominal capacity, is one of the most important parameters for managing Li-ion battery packs. This work investigates two types of artificial neural network-based SOC estimators: a feedforward neural network (FNN) and a nonlinear autoregressive exogenous model (NARX) network. These networks are trained and tested with battery drive cycle and charging data for a Tesla Model 3 electric vehicle. Measured temperature, along with different combinations of filtered and unfiltered voltage and current, are used as model inputs. The NARX, which benefits from having SOC from the prior time step as an input, is shown to have substantially less error than the FNN, even when there is a significant current sensor offset error which prevents the NARX from simply functioning as a coulomb counter. Overall, the NARX is shown to be accurate for the most difficult highway drive cycles with steep grades and to be robust against large current sensor errors.
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 | 0.001 |
| 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".