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

Area of Science:

• * Electrochemical energy storage systems
• * Battery management systems
• * Lithium-ion battery characterization

Background:

• * The open circuit voltage (OCV)-state of charge (SOC) curve is crucial for accurate battery management but is affected by battery degradation and inconsistency.
• * Traditional lab-based OCV-SOC curve identification is time-consuming, prompting interest in online methods using electric vehicle (EV) operational data.
• * Identifying the OCV-SOC curve online faces challenges due to insufficient dynamic voltage excitation under many EV operating conditions, leading to uncertainty.

Purpose of the Study:

• * To develop an accurate online method for identifying the OCV-SOC curve of lithium-ion batteries using EV operational data.
• * To address the uncertainty in online OCV identification caused by limited dynamic voltage responses.
• * To improve the accuracy of state of charge (SOC) estimation by enhancing the OCV-SOC curve construction.

Main Methods:

• * Utilized Shannon entropy of measured signals to assess the accuracy of identified OCV.
• * Employed terminal voltage error, calculated from identified parameters, as a metric for OCV accuracy.
• * Interpolated the starting point for ampere-hour counting within the constructed OCV-SOC segment using the identified OCV to ensure SOC accuracy.

Main Results:

• * The online constructed OCV-SOC curve exhibited a maximum deviation below 22 mV.
• * The proposed method achieved less than 2.2% error when applied to SOC estimation.
• * The integration of Shannon entropy and voltage error effectively quantified the accuracy of the identified OCV.

Conclusions:

• * The developed online OCV-SOC curve identification method is accurate and reliable for lithium-ion batteries in EVs.
• * The approach successfully mitigates uncertainties associated with limited dynamic voltage data during EV operation.
• * Accurate OCV-SOC curve identification directly translates to improved SOC estimation accuracy for EV battery management systems.