Research data - Diagnosis of the open-circuit voltage curve of batteries with voltage-controlled models

This dataset contains the research data (MATLAB code, measurement data) of the journal article: Jonas A. Braun, Wolfgang G. Bessler, Diagnosis of the open-circuit voltage curve of batteries with voltage-controlled models, Journal of Power Sources 676 (2026) 239471, https://doi.org/10.1016/j.jpowsour.2026.239471. If you use this research data, please cite both the original publication and this Zenodo repository, in accordance with the CC BY-NC 4.0 license.

Abstract:

The open-circuit voltage curve (OCV curve) is a fundamental property of batteries. It is a signature of the battery chemistry and contains details on health and degradation modes. This article presents a novel method for operando diagnosis of the OCV curve using a voltage-controlled model (VCM). The method is based on a dynamic equivalent circuit model which uses measured voltage as input and simulates current as output. Theory and algorithm are developed for estimating the OCV curve from the difference between experimental and simulated current. The algorithm is demonstrated with four different lithium-ion battery cells that have different chemistries (metal-oxide and iron-phosphate positive electrodes), formats (pouch and prismatic), capacities (between 0.35 Ah and 180 Ah) and states of health (fresh and aged). Different operating protocols were used, spanning the range from two-hour single-cycle data up to 60-hour multi-rate data and including dynamic and partial cycles. In all cases, the algorithm was able to successfully estimate the OCV curve. Mean absolute errors between estimated and true OCV curves were below 20 mV for each protocol and 10.1 mV in average. The algorithm thus allows to identify battery chemistries and to differentiate between fresh and aged batteries, without requiring low-current pseudo-OCV measurements.

Copyright and IP information:

Copyright © 2026 by Wolfgang G. Bessler and Jonas A. Braun. All rights reserved. The MATLAB code and research data provided are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Please note that the algorithms themselves are subject to intellectual property rights, including, but not necessarily limited to, German patent DE102022129314 and international patent application WO2024/099513A1. Any use of the codes and algorithms presented here is subject to these property rights.

Quick start:

Download all files and save them in the same folder. Open and start "EstimateOCVCurve.m" in MATLAB. Adjust the number of iterations. If you set the number of iterations to 1, Figure 5 of the journal article will be reproduced. 

Description of the files:

MATLAB code (tested using versions R2023b):

EstimateOCVCurve.m: This script diagnoses the OCV curve of a lithium-ion battery cell.

calculateDeltaV0.m: This script uses a linear fit to calculate the difference between the simulated and experimental OCV (called deltaV0 in the journal article).

interpolateCurve.m: This script performs a linear interpolation of a curve with 1001 entries as function of SOC between 0 and 1.

simulateVCMSimple.m: This script simulates a voltage-controlled "simple" equivalent circuit model using the Euler time discretization method.

Experimental data:

ExperimentalData_CellA_Protocol1_Protocol9.csv: The CSV file contains measured data from a single cell operating in three modes: full cycles, partial cycles, and WLTP cycles. It includes current, voltage, and temperature measurements.  

OCV_vs_SOC_curve_CellA.csv: The CSV file contains the true OCV curve.

Other files:

readme.txt: Overview of files with a short description.