Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries

Pastor-Flores, Pablo; Martin-del-Brio, B.; Bono-Nuez, Antonio; Sanz-Gorrachategui, Iván; Bernal-Ruíz, Carlos

doi:10.3390/electronics10182294

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…A SOM is employed to identify aging conditions in LIBs [72]. The methodology can be employed to depict the aging process in batteries intended for the second-life market, even if their past uses are unknown.…”

Section: ) Self-organizing Mapsmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review paper analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.INDEX TERMS Deep learning, health and life-cycle analysis, Lithium-ion battery management system, prognostics and health management, remaining useful life prediction, state of charge estimation. Nomenclature

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Section: ) Self-organizing Mapsmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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“…Unsupervised learning can be used to detect abnormal conditions in battery operation, such as excessive temperature, abnormal voltage, capacity drop, etc. By modeling the normal behavior of the battery, the state of the battery can be monitored in real time, and potential problems can be detected in time so that early action can be taken to avoid battery failure [25]. Ensemble learning algorithms combine multiple learners and have better learning performance.…”

Section: Introductionmentioning

confidence: 99%

State of Health Prediction for Lithium-Ion Batteries through Curve Compression and CatBoost

Yin

Zhang

Feng

2023

WEVJ

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This study proposes a novel approach for predicting the State of Health (SoH) and Remaining Useful Life (RUL) of lithium-ion batteries. The low accuracy of SoH and RUL is due to the challenges of establishing effective feature engineering for battery attributes. To address this issue, a SoH and RUL prediction model based on curve compression and CatBoost is proposed. Firstly, an improved threshold selection method based on curvature analysis is introduced to enhance the compression performance of battery attributes under different cycles. Secondly, to ensure that the extracted feature sequences have the same length, spline interpolation and local anomaly factor detection techniques are utilized to fill or eliminate feature points for feature length normalization. Finally, a dynamic time regularization algorithm is applied to calculate the shortest distance between the feature sequence and the original curve to determine the optimal feature length for input into the CatBoost prediction model. The experimental results demonstrate that the proposed approach outperforms other prediction models in the research object dataset, achieving R2 values higher than 0.98 and MSE values around 1 × 10−5. The proposed approach also achieves better prediction results in the validation object dataset, indicating its strong generalization capability. Additionally, the proposed model shows significant robustness by accurately predicting SoH and RUL under noisy environmental conditions. Overall, the proposed model shows significant potential to accurately predict SoH and RUL by efficiently addressing the challenges associated with feature engineering for battery attributes, reducing the impact of background noise on prediction results, and exhibiting strong robustness.

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Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information

Sanz-Gorrachategui,

Wang,

Guillén-Asensio

et al. 2024

IEEE Access

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Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries

Cited by 4 publications

References 36 publications

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

State of Health Prediction for Lithium-Ion Batteries through Curve Compression and CatBoost

Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information

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