Consensus cubature filtering based on Gaussian process for distributed sensor network with model uncertainty

Li, Song; Jing, Zhongliang; Dong, Peng; Zou, Ruping; Chen, Shichao

doi:10.1016/j.sigpro.2021.108347

Cited by 11 publications

(1 citation statement)

References 23 publications

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“…Besides, the uncertainties should be quantified to improve the performance of confidence estimation (Liu et al, 2015). From our experiment and the research on Song et al (2022), the measurement error could be regarded as Gaussian distribution. Moreover, the deviation based on the measurement interval could also be regarded as Gaussian distribution.…”

Section: Methodsmentioning

confidence: 97%

State of health confidence estimation for lithium-ion battery based on probabilistic ensemble learning

Wang,

Song,

Chen

et al. 2023

Transactions of the Institute of Measurement and Control

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Uncertainties in a battery would result in unreliable state of health (SOH) estimation. Considering the greater risk after reaching the end of life (EOL), designing a suitable ensemble learning to provide early warning before reaching EOL with uncertainty measurement is desirable for confidence estimation. In this paper, a novel probabilistic ensemble learning method-Gaussian process-based neural networks is proposed for the SOH confidence estimation by describing the uncertainties in probabilistic form. First, different neural networks are built based on health features. Second, battery data are classified under the recovery of capacity and normal operation conditions to characterize the uncertainties of the data under different operation conditions. Besides, the Gaussian process-based neural networks method is constructed based on the data from different conditions for neural networks weighted ensemble with the probabilistic form of Gaussian distribution. Therefore, the uncertainties are measured in the probabilistic form considering different operation conditions which is different from other methods. With the probabilistic form, the confidence interval could be determined to ensure the real SOH within the confidence interval, which improves the estimation performance of the proposed method because of the early warning near the EOL. Finally, the effectiveness is validated by NASA data sets and our experiment with the commercial 18650 lithium-ion battery. From the results, the mean error is less than 1% and real SOH is within the confidence interval.

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Section: Methodsmentioning

confidence: 97%