Entropy-driven online open circuit voltage identification for precise state estimation in lithium-ion batteries.

Authors

Zhengyang Li, Cheng Chen, Ruixin Yang, Hailong Li, Rui Xiong

Published in

iScience. Volume 28. Issue 9. Pages 113290. Sep 19, 2025. Epub Aug 06, 2025.

Abstract

The open circuit voltage (OCV)-state of charge (SOC) curve of lithium-ion batteries is affected by battery inconsistency and degradation. Compared to lab methods, which are time-consuming, using operation data of electric vehicles (EVs) to identify OCV-SOC curve online attracts increasing attention. Considering that many operating conditions of EVs cannot sufficiently excite the dynamic voltage response of battery, leading to significant uncertainty in identification results, the Shannon entropy of measured signal and terminal voltage error calculated by the identified parameters are used to assess the accuracy of the identified OCV in this work. Then the identified OCV is used to interpolate the start point of ampere-hour counting in the constructed OCV-SOC segment, to guarantee the accuracy of SOC. Validation results show that the maximum deviation of the online constructed OCV-SOC curve is below 22 mV. When applied to SOC estimation, an error of less than 2.2% can be achieved.

PMID:
41054552
Bibliographic data and abstract were imported from PubMed on 07 Oct 2025.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.