Robust Data-Driven Battery State of Charge Estimation for Hybrid Electric Vehicles
Bibliographic record
Abstract
<div>This article presents a method to estimate the State of Charge (SOC) in Lithium-ion batteries of Hybrid Electric Vehicles (HEVs) with Artificial Neural Networks (ANNs). The inputs of the SOC estimation algorithm are the measured values of current, voltage, and temperature. In the article, two different battery packs are considered for a power-split full HEV. The training and validation datasets needed for developing the ANNs are generated exploiting a numerical model of two different configurations of an HEV performing real-world driving missions or the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) cycle, while the testing dataset is collected experimentally on battery cells. Specifically, the capacity values for the considered battery pack sizes are 1.82 kWh and 1.06 kWh. The proposed method uses a Nonlinear AutoRegressive with eXogenous input (NARX) recurrent ANN, which has been observed to have reasonable computational cost in prior research. The performance of the investigated technique is demonstrated by estimating the SOC with a low estimation error for both the considered battery sizes. Coulomb counting is used to compute the reference value of the SOC during the real charge/discharge cycles. An analysis of the robustness of the proposed estimation method to offset errors on the measured input current is also performed.</div>
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".