LEE, SUNG HAK

김정기

박형근

이학현

설재복

CHIN, KWANG GEUN

김형섭

Energies

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li$_{1}$(NiCoAl)$_{1}$O$_{2}$-Li$_{1}$(NiCoMn)$_{1}$O$_{2}$/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method

Fang, Q.

Wei, X.

Lu, T.

Dai, H.

Zhu, Jiangong

KITopen

English

A state of health estimation method for lithium-ion batteries based on voltage relaxation model

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the &#945;-based, &#946;-based, and parameter&#8211;fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the &#945;-based method to 1.8% under the parameter&#8211;fusion method

Qiaohua Fang

Xuezhe Wei

Tianyi Lu

Haifeng Dai

Jiangong Zhu

Directory of Open Access Journals

A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

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