Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability,...

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Bibliographic Details
Published in:International journal of energy research 2023-02, Vol.2023, p.1-13
Main Authors: Kim, Joonchul, Kim, Eunsong, Park, Jung-Hwan, Kim, Kyoung-Tak, Park, Joung-Hu, Kim, Taesic, Min, Kyoungmin
Format: Article
Language:English
Subjects:
Accuracy
Aluminium
Aluminum
Aluminum oxide
Batteries
Cathodes
Cell cycle
Charge transfer
Chemical elements
Cobalt
Cobalt compounds
Cycles
Electrode materials
Elements
Iron phosphates
Lithium
Lithium-ion batteries
Machine learning
Manganese
Manganese oxides
Methods
Nickel
Partitioning
Phosphates
Predictive maintenance
Quality assessment
Quality control
Stability analysis
Training
Useful life
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Summary:Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability, the charge–discharge information of cathode materials with different chemical elements was adopted from previous research, including lithium iron phosphate, lithium nickel cobalt aluminum oxide, and lithium nickel cobalt manganese oxide cells. Among the partitioning procedures, the method of adding predicted data from the surrogate model to the training set exhibited the best accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which only used certain cycles before the test set as the training set, exhibited the worst MAE value of 60 cycles. In conclusion, the proposed data partitioning method could be implemented to predict the RUL of batteries to develop next-generation cathode materials with improved performance and stability, shorten the quality assessment time, and achieve stable predictive maintenance.
ISSN:0363-907X
1099-114X
DOI:10.1155/2023/9305309