Entropy-induced temperature variation as a new indicator for state of health estimation of lithium-ion cells

Wu, Yao; Jossen, Andreas

doi:10.1016/j.electacta.2018.04.203

Cited by 33 publications

(17 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison, the data (voltage, current, surface temperature, etc.) required by the SOH estimation method based on the surface temperature variation rate curve 18 and the requirements for the batteries working mode are relatively wide, so it is more suitable for application SOH estimation on vehicle batteries. Therefore, this paper takes the SOH estimation method based on the surface temperature variation rate curve as a representative to introduce the basic ideas, steps, and limitations of the external indicator function SOH estimation method.…”

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

“…Wu and Yosen 18 pointed out through a large number of experiments that there are two valleys in the surface temperature variation rate curve caused by entropy varying during the batteries constant current (CC) charging, and the distance between the two valleys has a functional relationship with SOH. Therefore, the batteries surface temperature variation rate easily obtained during constant current charging can be used as an external indicator of SOH.…”

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

“…In the surface temperature variation rate curve, the corresponding time is the distance between the two valleys caused by entropy changing. Wu and Yosen 18 define it as t min and t min can be used as an external indicator of batteries SOH estimation. According to the external indicator value t min , the corresponding function relationship between it and SOH is established as follows:

S_{soh} = g ((), t_{\min})

…”

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

“…According to the Ref. 18, the fitting function relationship is given as shown in Equation ):

S_{soh} = g ((), t_{\min}) = a * t_{\min} + b

where a and b represent fitting coefficients. In this paper, the functional relationship in Equation ) is used as the basis for online determination of the initial SOH value of the same material vehicle power batteries.…”

Section: Soh Online Estimation Framework For Discharging Process Based On External Indicator Functionmentioning

confidence: 99%

“…However, the measurement of batteries surface stress variations requires more precise instruments, so it is not suitable for batteries SOH estimation problems under complex working conditions. Further, Wu and Yosen in reference 18 propose an SOH estimation method based on the surface temperature variation rate curve only based on the easily available data (surface temperature, voltage, current) of the vehicle batteries, the surface temperature variation characteristic curve method, which obtained by differential surface temperature and time ( dT / dt ), and establish the function relationship between the corresponding time t min of the surface temperature variation rate curve's valley and the SOH. Compared with the two types of incremental curve methods, it is not only applicable to SOH estimation under high‐rate current, but also data are easier to obtain.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online estimation of lithium‐ion batteries state of health during discharge

Liu

Yanpeng

et al. 2021

Int J Energy Res

View full text Add to dashboard Cite

Summary In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed‐loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium‐ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship.

show abstract

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

S_{soh} = g ((), t_{\min})

…”

Section: Soh Estimation Based On External Indicator Function Methodsmentioning

confidence: 99%

“…According to the Ref. 18, the fitting function relationship is given as shown in Equation ):

S_{soh} = g ((), t_{\min}) = a * t_{\min} + b

Section: Soh Online Estimation Framework For Discharging Process Based On External Indicator Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online estimation of lithium‐ion batteries state of health during discharge

Liu

Yanpeng

et al. 2021

Int J Energy Res

View full text Add to dashboard Cite

show abstract

State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy

Guo

Wang

et al. 2022

Energy Tech

View full text Add to dashboard Cite

Accurate prediction of the state of health (SOH) is essential to ensure the safety and reliability of battery operation. The thermal factor is an important indicator of SOH and many methods based on temperature are sensitive to measurement noise. To improve SOH estimation precision, a new health indicator (HI) directly extracted from the temperature curve is developed and an integrated multi-Gaussian process regression (MGPR) model is proposed. First, based on the trend analysis of the charging temperature curves with battery degradation, three features that can reflect thermal characteristics are extracted as the HI. Second, considering that the model generated by machine learning is influenced by the training dataset and the inherent inconsistencies in batteries, MGPR model is proposed to improve the model fitness. The training data is reformed and multiple GPR models are established. The multiple models are weighed by taking into account the prediction uncertainty to get the final SOH estimation result. Finally, two types of open-source data relative to different ambient temperatures and operating profiles are used to verify the performance. Experiment results show that the HI developed can characterize the battery degradation well and the MGPR model has high robustness and can obtain high-precision estimation results.

show abstract

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

View full text Add to dashboard Cite

Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

show abstract

Entropy-induced temperature variation as a new indicator for state of health estimation of lithium-ion cells

Cited by 33 publications

References 37 publications

Online estimation of lithium‐ion batteries state of health during discharge

Online estimation of lithium‐ion batteries state of health during discharge

State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Contact Info

Product

Resources

About