Method and system for estimating health state of lithium ion battery

By extracting eight health indicators of lithium-ion batteries using the DRT method and combining them with temperature and SOC, the problems of low SOH estimation accuracy and poor robustness in existing technologies are solved, and high-precision estimation is achieved under different working conditions.

CN120652335APending Publication Date: 2025-09-16HEFEI UNIV OF TECH
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Patent Information

Application Number
CN202511003956.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-21
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing lithium-ion battery SOH estimation methods have problems such as low accuracy, difficulty in feature extraction, and poor robustness under different operating conditions.

Method used

The DRT method was used to analyze the EIS data and extract eight health indicators (peak amplitude, time constant corresponding to the peak, peak area, full width at half maximum, weighted average time constant, weighted standard deviation time constant, time constant skewness, and time constant kurtosis). These indicators were combined with the current temperature and SOC and input into the trained SOH estimation model for estimation.

Benefits of technology

The accuracy and robustness of SOH estimation are improved, and it can maintain a low error level under a wide range of operating environments and effectively capture key information related to battery aging.

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Abstract

The invention provides a lithium ion battery health state estimation method, and belongs to the field of battery management, and the method comprises the steps: obtaining the EIS data, the current temperature and the current SOC of a to-be-detected lithium ion battery; the EIS data are analyzed through a DRT method, a DRT curve is obtained, health indexes are extracted from the DRT curve, and the health indexes comprise the peak amplitude, the time constant corresponding to the peak value, the peak area, the full width at half maximum, the weighted average time constant, the weighted standard deviation time constant, the time constant skewness and the time constant kurtosis; combining the health index, the current temperature and the current SOC to obtain an input feature vector, inputting the input feature vector into the trained SOH estimation model, and estimating the SOH of the lithium ion battery to be measured; the invention further provides an estimation system. The eight health indexes are closely related to aging mechanisms such as SEI membrane growth, active substance loss and impedance increase of the battery, high-quality input is provided for the model, and SOH estimation precision is improved; the temperature and the SOC serve as key input, and the influence of working condition changes on EIS measurement is effectively compensated.
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