An Improved ECM-Based State-of-Charge Estimation for SLA and LFP Batteries Used in Low-Cost Agricultural Mobile Robots
Bibliographic record
Abstract
Batteries are crucial in transitioning from fossil fuels to clean-powered mobility, for several applications such as Electric Vehicles and Agricultural Mobile Robots (AMRs). However, the adoption of AMRs is limited by several challenges related to battery management, including restricted operation time, long recharge periods, and safe operation. The State of Charge (SOC) provides information about the remaining energy in the battery and is essential for battery management. Therefore, an accurate SOC estimation is crucial to ensure safe and reliable operation, which is needed to overcome the aforementioned challenges. This paper proposes, implements, and validates an SOC estimation system for low-cost AMRs. The accuracy of the SOC estimation is improved by adding information about the battery’s Open Circuit Voltage (OCV) to the Equivalent Circuit Models (ECM). Two SOC estimation methods based on ECM were implemented and validated for a Lithium Iron Phosphate battery (LFP) and a Sealed Lead Acid (SLA) battery powering an AMR. Finally, the results indicate that adding the OCV information to the models improves the estimation accuracy for both chemistries, being particularly interesting for LFP batteries, whose OCV vs. SOC has a flat area in almost the entire useful region of the SOC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".