Development and analysis of the application of an adaptive measurement grid in obtaining the OCV(SOC) curve of lithium-ion batteries
Year: 2026
Type: article
Abstract: The paper addresses the problem of efficient identification of the Open Circuit Voltage versus State of Charge (OCV-SOC) dependency for lithium-ion batteries, which is a critical step for creating accurate Battery Management Systems (BMS). Traditional approaches using uniform discretization are characterized by increased time costs and low accuracy, since they do not take into account the shape of the OCV(SOC) curve. This article presents four adaptive discretization algorithms based on local function nonlinearity analysis: second derivative calculation, moving window regression analysis, collinearity analysis via triangle area, and tangent angle variance analysis. The theoretical basis of the developed methods is the principle of interpolation error equidistribution, linking the discretization step to the local function curvature. Numerical simulation was performed using a battery mathematical model with a second-order Thevenin equivalent circuit. Results demonstrate that the proposed algorithms provide approximation accuracy comparable to a fine uniform grid (2 % SOC step). The methods based on the second derivative and regression analysis showed the highest efficiency regarding the accuracy-to-time ratio. The developed approaches include smoothing mechanisms to ensure robustness against measurement noise and are recommended for optimizing battery characterization processes in research and industrial conditions.
Authors: I. S. Lyamin, A. V. Golubkov
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203603065