Remaining useful life prediction for lithium-ion battery using dynamic fractional brownian motion degradation model with long-term dependence

Li, Xin; Ma, Yan

doi:10.1007/s43236-022-00507-w

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Ref. [ 29 ] proposes a nonlinear degradation model based on fractional Brownian motion with dynamic properties (FBM-D) to feature the long-range dependence of the lithium battery degradation data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Deng

Gao

Chen

et al. 2023

Entropy

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In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

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Section: Introductionmentioning

confidence: 99%

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Deng

Gao

Chen

et al. 2023

Entropy

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“…The temporal correlation for the longrange-dependent time series is strong, which is beneficial for RUL prediction [17]. Fractional Brownian motion (FBM) has been proposed for RUL prediction due to its non-Markovian characteristics [18]. The FBM model requires a Gaussian assumption for the vibration response, which is not always valid.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

Zheng,

Deng,

Song

et al. 2023

Fractal Fract

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The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

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“…Then, the PDF of the RUL at 10 different prediction starting points of the operation process is obtained by Monte Carlo simulation. The prediction results of the PDFs by different models are shown in Figure15[34][35][36][37].…”

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confidence: 99%

An Adaptive Generalized Cauchy Model for Remaining Useful Life Prediction of Wind Turbine Gearboxes with Long-Range Dependence

et al. 2022

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Remaining useful life (RUL) prediction is important for wind turbine operation and maintenance. The degradation process of gearboxes in wind turbines is a slowly and randomly changing process with long-range dependence (LRD). The degradation trend of the gearbox is constantly changing, and a single drift coefficient is not accurate enough to describe the degradation trend. This paper proposes an original adaptive generalized Cauchy (GC) model with LRD and randomness to predict the RUL of wind turbine gearboxes. The LRD is explained jointly by the fractal dimension and the Hurst exponent, and the randomness is explained by the diffusion term driven by the GC difference time sequence. The estimated value of the unknown parameter of adaptive GC model is deduced, and the specific expression of the RUL estimation is deduced. The adaptability is manifested in the time-varying drift coefficient of the GC model: by continuously updating the drift coefficient to adapt to the change in the degradation trend, the adaptive GC model offers high accuracy in the prediction of the degradation trend. The performance of the proposed model is analyzed using real wind turbine gearbox data.

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Remaining useful life prediction for lithium-ion battery using dynamic fractional brownian motion degradation model with long-term dependence

Cited by 9 publications

References 31 publications

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

An Adaptive Generalized Cauchy Model for Remaining Useful Life Prediction of Wind Turbine Gearboxes with Long-Range Dependence

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