Multifeature‐based online remaining useful life prediction of lithium‐ion batteries in stages using cascaded data‐driven algorithm

Accurately predicting the remaining useful life (RUL) is crucial for the safety and stability of battery systems. Considering the inherent challenges in directly measuring the capacity of lithium‐ion batteries during operation, this paper proposes an online hybrid cascaded data‐driven prediction alg...

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Bibliographic Details
Published in:Quality and reliability engineering international Vol. 40; no. 5; pp. 2527 - 2546
Main Authors: Tao, Liujun, Wu, Huaiyu, Zheng, Xiujuan
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.07.2024
Subjects:
Algorithms
Artificial neural networks
cascaded hybrid method
Correlation analysis
Errors
health indicators
Life prediction
Lithium
Lithium-ion batteries
lithium‐ion battery
Machine learning
Prediction models
Principal components analysis
Redundancy
remaining useful life
Search algorithms
Useful life
ISSN:0748-8017, 1099-1638
Online Access:Get full text
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Summary:Accurately predicting the remaining useful life (RUL) is crucial for the safety and stability of battery systems. Considering the inherent challenges in directly measuring the capacity of lithium‐ion batteries during operation, this paper proposes an online hybrid cascaded data‐driven prediction algorithm for RUL. Health indicators (HIs) are extracted from the charge–discharge voltage and incremental capacity curves, following which gray correlation analysis is employed to quantitatively assess the relevance between the HIs and batteries' capacities. Redundancy of HIs is eliminated through kernel principal component analysis, which enhancing the efficiency of subsequent analysis. The proposed framework incorporates the sparrow search algorithm‐based kernel extreme learning machine (SSA‐KELM) as the first‐level prediction model, establishing the relationship between HIs and capacities. The bidirectional long short‐term memory (BiLSTM) is utilized as the secondary‐level model, which integrates the preliminary capacity predictions of SSA‐KELM. Finally, experimental validation using battery datasets from NASA and Oxford showed that the method has remarkable generalization ability and superior prediction accuracy. Quantitatively, the RMSE and MAPE of NASA batteries are within 0.03, while the errors of Oxford batteries are within 0.003. The RUL prediction errors of all lithium‐ion batteries are within two cycles.
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ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3524