A novel methodology to estimate the state-of-health and remaining-useful-life of a Li-ion battery using discrete Fourier transformation

Sangiri, Jeemut Bahan; Kulshreshtha, Tanmai; Ghosh, Sudipto; Maiti, Suman; Chakraborty, Chandan

doi:10.1016/j.est.2021.103849

Cited by 17 publications

(3 citation statements)

References 60 publications

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“…The RUL is one of the important indicators to measure the aging degree of lithiumion batteries, and it is closely correlated with the state of health (SOH) of lithium-ion batteries [6]. When the SOH of lithium-ion batteries falls below a certain threshold, their operational performance will inevitably deteriorate [7].…”

Section: Introductionmentioning

confidence: 99%

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Qin

Fan

et al. 2023

Applied Sciences

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Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed.

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Section: Introductionmentioning

confidence: 99%

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Qin

Fan

et al. 2023

Applied Sciences

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“…It includes several aspects such as battery state of charge (SOC), state of health (SOH), battery thermal management, and safety management. [9][10][11][12] Predicting battery aging is a key issue in battery management. The aging state of a battery can be expressed in terms of the battery SOH.…”

mentioning

confidence: 99%

Battery State of Health Model Estimation Based on Variable Projection Algorithm Using Steffensen Acceleration Method

Cheng

Chen

et al. 2023

J. Electrochem. Soc.

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The variable projection based Steffensen acceleration (VP-SA) algorithm is proposed for battery state of health estimation. The VP-SA algorithm exploits the separability of variables to reduce the dimensionality of the parameters, which can improve the estimation efficiency. In addition, based on the Steffensen acceleration method, the convergence rates of the VP algorithm can be increased. The simulation example shows the effectiveness of the proposed algorithm.

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“…[5][6][7][8] The current research on SOH includes battery degradation mechanism and aging factor analysis, battery health management, battery state estimation, and battery aging prediction. [9][10][11][12][13] SOH cannot be obtained by direct measurement but can be evaluated by models. 14,15 Recently, there exist two main battery SOH estimation models: electrochemical model and empirical model.…”

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confidence: 99%

Expectation Maximization Algorithm for a Battery State of Health Model with Missing Data

Han

2023

J. Electrochem. Soc.

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With the popularity of lithium-ion batteries, battery state of health (SOH) estimation has become one of the current research hotspots. Due to network congestion, collected data usually encounter time-delay or packet loss. In this paper, an expectation maximization (EM) algorithm is proposed for the SOH model which is approximated by a polynomial model. Based on the EM method, the missing data are computed in the E step, and the parameters are updated in the M step. Compared with the least square method, the proposed algorithm has more accurate estimation accuracy. The simulation example shows the effectiveness of the proposed algorithm.

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A novel methodology to estimate the state-of-health and remaining-useful-life of a Li-ion battery using discrete Fourier transformation

Cited by 17 publications

References 60 publications

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Battery State of Health Model Estimation Based on Variable Projection Algorithm Using Steffensen Acceleration Method

Expectation Maximization Algorithm for a Battery State of Health Model with Missing Data

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