Quantum assimilation-based data augmentation for state of health prediction of lithium-ion batteries with peculiar degradation paths

Gao, Haotian; Lin, Kaiqi; Cui, Yuxuan; Chen, Yunxia

doi:10.1016/j.asoc.2022.109515

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…In response to these issues, ML technology can utilize its powerful data processing and pattern recognition capabilities to improve the accuracy and efficiency of battery fault diagnosis through the real-time monitoring and analysis of batteries. For the condition with few experimental fault data samples, ML can use feature selection, data enhancement, TL, semi-supervised learning, and other methods to improve the accuracy and reliability of diagnosis [64][65][66]. In addition, ML can also optimize battery management strategies, thereby further improving the safety and reliability of battery systems.…”

Section: Machine Learning Is Applied To Libs Fault Diagnosismentioning

confidence: 99%

Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

Chen,

Qi,

Wang

2023

Energies

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Lithium-ion batteries, as a typical energy storage device, have broad application prospects. However, developing lithium-ion batteries with high energy density, high power density, long lifespan, and safety and reliability remains a huge challenge. Machine learning, as an emerging artificial intelligence technology, has successfully solved many problems in academic research on business, financial management, and high-dimensional complex problems. It has great potential for mining and revealing valuable information from experimental and theoretical datasets. Therefore, quantitative “structure function” correlations can be established to predict battery health status. Machine learning also shows significant advantages in strategy optimization such as energy optimization management strategy. For lithium-ion batteries, their performance and safety are closely related to the material structure, battery health, fault analysis, and diagnosis. This article reviews the application of machine learning in lithium-ion battery material research, battery health estimation, fault analysis, and diagnosis, and analyzes its application in aviation batteries in conjunction with the development of green aviation technology. By exploring the practical applications of machine learning algorithms and the advantages and disadvantages of different applications, this article summarizes and prospects the application of machine learning in lithium batteries, which is conducive to further understanding and development in this direction.

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Section: Machine Learning Is Applied To Libs Fault Diagnosismentioning

confidence: 99%

Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

Chen,

Qi,

Wang

2023

Energies

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“…The original degradation 𝑥 1∶𝑘−1 and the generated degradation dataset x1∶𝑘−1 that were measured from the beginning to the 𝑘 − 1 distance were fed into the GRU network. The forward propagation expression of GRU is shown in (9).…”

Section: 22mentioning

confidence: 99%

“…At present, many scholars have been studying degradation modeling and prediction. Such as modeling methods based on degradation paths, 8,9 based on the stochastic processes, [10][11][12] and based on the machine learning. 13,14 However, when the external environment becomes complex, the degradation process cannot be accurately described based on stochastic processes and degradation paths.…”

Section: Introductionmentioning

confidence: 99%

SVAE‐GRU‐based degradation generation and prediction for small samples

Shangguan

Feng

et al. 2023

Quality & Reliability Eng

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The degradation process analysis is the basis of the system reliability assessment. Too few degradation samples will affect the accuracy of the reliability analysis. So, it is necessary to expand the degradation data and construct the degradation prediction model. This paper proposes an SVAE‐GRU‐based degradation generation and prediction model to handle the issues. New degradation data are generated by combining the Stacked Variational Autoencoder (SVAE) and Gated Recurrent Units (GRU) in each time step, which can improve the learning effectiveness of the degradation features. The GRU and the Multi‐layer perceptron calculate the mean and the variance of the degradation distribution for each timestamp, the deeply heterogeneity and temporal feature of the original degraded samples are learned. Then the degradation amount of different samples are predicted. The optimal hyper‐parameters of the SVAE‐GRU are obtained by the grid search method. To verify the effectiveness of the proposed method, three datasets and four methods are used for experiment comparison in this paper. The actual example indicate that the SVAE‐GRU method has the best effect on the degradation generation and prediction.

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A Data Augmentation Method for Lithium‐Ion Battery Capacity Estimation Based on Wassertein Time Generative Adversarial Network

Soo,

Wang,

Xiang

et al. 2024

Energy Tech

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Accurate capacity estimation of lithium‐ion battery packs plays an important role in determining the battery performance degradation. However, performing comprehensive experiments for the whole battery pack to collect sufficient data is expensive and tedious. To eliminate the need for repetitive experiments this article proposes a pack battery capacity estimation model based on the incremental capacity analysis method and virtual battery generation. The proposed method achieved precise capacity estimation for pack batteries even when data availability is limited. A modified wassertein time generative adversarial network‐based approach for virtual battery generation is proposed and evaluated. A total of 12 virtual batteries are generated and trained with long short‐term memory. The proposed method is compared with alternative approaches, including those that do not employ data augmentation, as well as the original generative adversarial network (TimeGAN). The proposed method achieves better accuracy for each battery 1# and 2#, for mean squared error (MSE) reduced by 40% and 59%, mean absolute error reduced by 61% and 82%, and root mean squared error by 38% and 58%. The experimental results show the better the performance of generated virtual batteries added into the model training process, the greater the improvement for the model.

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Quantum assimilation-based data augmentation for state of health prediction of lithium-ion batteries with peculiar degradation paths

Cited by 7 publications

References 28 publications

Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

SVAE‐GRU‐based degradation generation and prediction for small samples

A Data Augmentation Method for Lithium‐Ion Battery Capacity Estimation Based on Wassertein Time Generative Adversarial Network

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