Piecewise Linear Approximation of Battery Open-Circuit Voltage Characteristics Using Dynamic Programming
Bibliographic record
Abstract
An open-circuit voltage (OCV) model, which represents OCV as a function of state of charge (SOC), is essential for estimating the state of a battery. Typically, the OCV-SOC characteristic is approximated using empirical functions, such as polynomials or lookup tables, and stored in a battery management system (BMS). However, polynomial approximations have several limitations: they are sensitive to floating-point errors, can be noninvertible, and require a high-precision computing system for numerical inverse lookups (determining the SOC for a given OCV). Moreover, many OCV models fail to maintain the monotonicity of the OCV-SOC relationship, especially in resource-constrained environments. While table-based (or piecewise linear) approaches have been explored, existing methods often fall short of preserving the accuracy of the OCV-SOC curve with a minimal number of breakpoints. This article introduces a novel dynamic programming approach to model the OCV-SOC characteristic as piecewise line segments. Compared to existing piecewise models, the proposed approach is more resource-efficient in systematically reducing the required table breakpoints while maintaining optimal accuracy. Experimental validation across various lithium-ion chemistries at four ambient temperatures consistently demonstrated a root mean square error below 5 mV. Additionally, differential feature extraction and segmented correlation analysis highlight the model’s effectiveness in estimating the battery’s state of health (SOH).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".