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Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data

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Title: Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data
Authors: Zhang, Huang, 1993, Su, Yang, Altaf, Faisal, 1982, Wik, Torsten, 1968, Gros, Sebastien, 1977
Source: Klassificering och optimal hantering av 2nd life xEV-batterier IEEE Transactions on Transportation Electrification. 9(2):2669-2682
Subject Terms: quantile regression forest, Lithium-ion battery, cycle life early prediction, prediction interval, interpretable machine learning.
Description: Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.
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  Data: Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Huang%22">Zhang, Huang</searchLink>, 1993<br /><searchLink fieldCode="AR" term="%22Su%2C+Yang%22">Su, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Altaf%2C+Faisal%22">Altaf, Faisal</searchLink>, 1982<br /><searchLink fieldCode="AR" term="%22Wik%2C+Torsten%22">Wik, Torsten</searchLink>, 1968<br /><searchLink fieldCode="AR" term="%22Gros%2C+Sebastien%22">Gros, Sebastien</searchLink>, 1977
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  Data: <i>Klassificering och optimal hantering av 2nd life xEV-batterier IEEE Transactions on Transportation Electrification</i>. 9(2):2669-2682
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  Data: <searchLink fieldCode="DE" term="%22quantile+regression+forest%22">quantile regression forest</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium-ion+battery%22">Lithium-ion battery</searchLink><br /><searchLink fieldCode="DE" term="%22cycle+life+early+prediction%22">cycle life early prediction</searchLink><br /><searchLink fieldCode="DE" term="%22prediction+interval%22">prediction interval</searchLink><br /><searchLink fieldCode="DE" term="%22interpretable+machine+learning%2E%22">interpretable machine learning.</searchLink>
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  Label: Description
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  Data: Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.
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        Value: 10.1109/TTE.2022.3226683
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      – Text: English
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      – SubjectFull: Lithium-ion battery
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      – SubjectFull: cycle life early prediction
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      – SubjectFull: prediction interval
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      – SubjectFull: interpretable machine learning.
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      – TitleFull: Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data
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            NameFull: Zhang, Huang
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            NameFull: Su, Yang
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              Y: 2023
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            – TitleFull: Klassificering och optimal hantering av 2nd life xEV-batterier IEEE Transactions on Transportation Electrification
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