Early prediction of cycle life for lithium-ion batteries based on evolutionary computation and machine learning

Gong, Dongliang; Gao, Ying; Kou, Yalin; Wang, Yurang

doi:10.1016/j.est.2022.104376

“…The predicted results closely match lithium-ion batteries' real capacity value and exhibit fluctuations similar to the actual capacity value. RMSE and RMSE in references [10] and [11] are in a good range.…”

Section: Rul Prediction Results Of Pf-bpmentioning

confidence: 81%

Life prediction of lithium battery based on particle filter and BP neural network

Zhou,

Huang,

Li

et al. 2024

J. Phys.: Conf. Ser.

0

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This paper focuses on using Particle Filter (PF) and Back Propagation (BP) neural networks for RUL prediction. This fusion strategy uses particle filtering (PF) for battery system state estimation and prediction while considering various types of noise and uncertainties to assess the system state comprehensively. Furthermore, the strategy employs a BP neural network to learn and combine historical data patterns with particle filtering estimation, improving prediction accuracy and reliability. This study validates the proposed fusion method by comparing it with the original PF and BP prediction methods using Toyota Motor Corporation and Stanford University datasets. The experimental results demonstrate its superior performance and higher prediction accuracy.

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“…They also developed and compared various machine learning methods, which can be regarded as the benchmark of more advanced related studies. 179,180 Some other researchers have conducted methods to improve the lifetime prediction accuracy based on the work of Severson et al 119 whether using more manually extracted features with the optimal feature selection method 181–183 or improved hybrid machine learning algorithms. 184,185…”

Section: Battery Health Prognosticsmentioning

confidence: 99%

“…They also developed and compared various machine learning methods, which can be regarded as the benchmark of more advanced related studies. 179,180 Some other researchers have conducted methods to improve the lifetime prediction accuracy based on the work of Severson et al 119 whether using more manually extracted features with the optimal feature selection method [181][182][183] or improved hybrid machine learning algorithms. 184,185 In addition to the feature-based lifetime prediction reviewed above, Paulson 186 evaluated the feature-based lifetime prediction on their own data set, which includes 300 pouch batteries with six different cathode chemistries.…”

Section: Eol Point Predictionmentioning

confidence: 99%