Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process

Kesen Fan, Yiming Wan, Zhuo Wang and Kai Jiang

Energy, 2023, vol. 268, issue C

Abstract: For lithium-ion batteries, the functional dependence of open circuit voltage (OCV) on state of charge (SOC) varies with temperature and aging, which plays a significant role in accurate SOC estimation and state of health monitoring. To identify the OCV-SOC curve at a given condition, OCVs usually need to be either measured by a time-consuming OCV test, or estimated with inevitable errors that eventually propagate into the identified OCV-SOC curve. In this paper, we investigate time-efficient identification of temperature-dependent OCV-SOC curve from current–voltage data, without measuring or estimating OCVs. In particular, we identify the complete OCV-SOC curve from data over a partial SOC range at a given temperature, by fusing available OCV-SOC curve data at other temperatures. In the proposed approach, a multi-output Gaussian process (MOGP) model is first built to capture correlations among OCV-SOC curves at different temperatures, and then used to construct the OCV-SOC curve at the given temperature. Using experimental datasets, our proposed approach reduces the root mean square error (RMSE) of OCV predictions by at least 29.4% compared to three existing methods. Besides, with the updated OCV-SOC curve, the RMSE of SOC estimates is reduced by at least 14.0%, compared to using a non-updated OCV-SOC curve.

Keywords: Lithium-ion battery; Equivalent circuit model; Open circuit voltage; State of charge; Multi-output Gaussian process (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:268:y:2023:i:c:s0360544223001184

DOI: 10.1016/j.energy.2023.126724

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