Reconstructing long-time voltage relaxation from short pulse tests for faster battery testing and modeling

The galvanostatic intermittent titration technique (GITT) is a widely used method for obtaining open-circuit voltage (OCV) profiles and kinetic information in rechargeable batteries. However, its practical use is limited by the long relaxation period required after each current pulse. This study proposes a physics-consistent short-window extrapolation method to reconstruct long-time relaxation behavior from only minutes of post-pulse data. Based on this framework, two pulse-wise OCV extraction strategies, namely threshold-based convergence and time-based extrapolation, are evaluated. The utility of reconstructed relaxation trajectories for downstream equivalent-circuit-model (ECM)-based dynamic battery modeling is also assessed. The method was evaluated using more than 5.7 k pulses across 8 different rest-duration protocols. Using 10 min of measured relaxation data, the threshold-based convergence yielded RMSE values of 4.35 mV during charge and 8.98 mV during discharge relative to the measured 3 h relaxation, reducing the required relaxation time from 180 min to 10 min. For downstream modified 2-RC ECM analysis, the time-based extrapolation-assisted framework reduced the mean absolute error by 78% relative to direct identification from 10 min data, and achieved accuracy comparable to direct identification from 1 h relaxation data while using only 1/6 of the measured relaxation time. Independent validation on five additional commercial cells confirmed that reconstruction-assisted ECM consistently reduces the error relative to direct ECM identification. The reconstruction was especially consistent over the mid-SOC range (20–80% SOC), with relatively larger deviations observed near the voltage cut-offs.

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Publication Details

Journal
Journal of Energy Storage
Published
2026-10-09
DOI
https://doi.org/10.1016/j.est.2026.125004
Primary Topic
Advanced Battery Technologies Research
Type
article
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article

Reconstructing long-time voltage relaxation from short pulse tests for faster battery testing and modeling

Qiaomin Ke, Bojing Zhang, Leah Nuss, Jun Yuan et al.
Journal of Energy Storage
Advanced Battery Technologies Research
article

Reconstructing long-time voltage relaxation from short pulse tests for faster battery testing and modeling

Qiaomin Ke, Bojing Zhang, Leah Nuss, Jun Yuan, Helge Sören Stein, Shanling Ji, Leon Merker
article en

Abstract

The galvanostatic intermittent titration technique (GITT) is a widely used method for obtaining open-circuit voltage (OCV) profiles and kinetic information in rechargeable batteries. However, its practical use is limited by the long relaxation period required after each current pulse. This study proposes a physics-consistent short-window extrapolation method to reconstruct long-time relaxation behavior from only minutes of post-pulse data. Based on this framework, two pulse-wise OCV extraction strategies, namely threshold-based convergence and time-based extrapolation, are evaluated. The utility of reconstructed relaxation trajectories for downstream equivalent-circuit-model (ECM)-based dynamic battery modeling is also assessed. The method was evaluated using more than 5.7 k pulses across 8 different rest-duration protocols. Using 10 min of measured relaxation data, the threshold-based convergence yielded RMSE values of 4.35 mV during charge and 8.98 mV during discharge relative to the measured 3 h relaxation, reducing the required relaxation time from 180 min to 10 min. For downstream modified 2-RC ECM analysis, the time-based extrapolation-assisted framework reduced the mean absolute error by 78% relative to direct identification from 10 min data, and achieved accuracy comparable to direct identification from 1 h relaxation data while using only 1/6 of the measured relaxation time. Independent validation on five additional commercial cells confirmed that reconstruction-assisted ECM consistently reduces the error relative to direct ECM identification. The reconstruction was especially consistent over the mid-SOC range (20–80% SOC), with relatively larger deviations observed near the voltage cut-offs.

Journal of Energy StorageVol. 182
Munich Center for Machine Learning, Technical University of Munich (DE), Southeast University (CN)
Openalex Percentile: Top 21%
Advanced Battery Technologies Research
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