A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data

Wang, Jiaolong; Zhang, Fode; Zhang, Jianchuan; Li, Wen; Zhou, Kuang

doi:10.1016/j.ress.2022.108976

Cited by 13 publications

(1 citation statement)

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“…put forward a multistage LSTM that simultaneously utilizes clustering algorithms to enhance RUL prediction. Recognizing the significance of data features, a Poly‐Cell LSTM model was proposed 25 . Although data‐driven methods based on machine learning excel at solving nonlinear problems, their accuracy is still inadequate for fulfilling requirements across all lifecycle stages, and their effectiveness relies heavily on the size and quality of the dataset.…”

Section: Introductionmentioning

confidence: 99%

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

Tao,

Wu,

Zheng

2024

Quality & Reliability Eng

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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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Section: Introductionmentioning

confidence: 99%