A state of health estimation method of lithium-ion batteries based on DT-IC-V health features extracted from partial charging segment

Tian, Aina; Yang, Chen Wei; Gao, Yang; Li, Taiyu; Wang, Lujun; Chang, Chun; Jiang, Jiuchun

doi:10.1080/15435075.2022.2136001

Cited by 12 publications

(2 citation statements)

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“…In summary, the charging capacity curve can be divided into N segments; each segment is shifted backward in turn by one step, where the value of 0.01 V is fixed [29], as shown in Equation (2).…”

Section: Healthy Feature Extractionmentioning

confidence: 99%

“…With the advantages of low self-discharge rate, high power density and long life [1], lithium-ion batteries have been widely used in the energy storage systems, electric vehicles, and other fields. In practical applications [2], improper handling of lithium ions can lead to deterioration of battery performance or even abnormal deterioration, which may increase battery safety risks. Accurate battery state of health (SOH) prediction is essential for battery management system (BMS) during battery operation to prevent these problems.…”

Section: Introductionmentioning

confidence: 99%

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Li-Ion Battery State of Health Estimation Based on Short Random Charging Segment and Improved Long Short-Term Memory

Tian,

Chen,

Pan

et al. 2023

IET Signal Processing

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Lithium-ion batteries have been used in a wide range of applications, including electrochemical energy storage and electrical transportation. In order to ensure safe and stable battery operation, the State of Health (SOH) needs to be accurately estimated. In recent years, model-based and data-driven methods have been widely used for SOH estimation, but due to the uncertainty of battery charging conditions in practice, it is difficult to obtain a fixed local segment. In this paper, the charging curve is first divided into several equal voltage difference segments based on charging segment voltage difference ΔV in order to solve the random charging segment problem. Time interval of equal charge voltage difference of the voltage curve, coefficient of variation and euclidean distance of the charging capacity difference curve are extracted as health features. The improved flow direction algorithmlong short term memory-based SOH assessment method is proposed and verified by the Oxford battery degradation dataset and experimental battery degradation dataset with a maximum error of 0.6%.

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Section: Healthy Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%