A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

Wang, Yingzhou; Ni, Yulong; Li, Na; Lü, Shan; Zhang, Shude; Feng, Zhongbao; Wang, Jianguo

doi:10.1002/ese3.460

Cited by 36 publications

(10 citation statements)

References 36 publications

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“…Support vector machine (SVM) algorithm is a machine learning method, which is more suitable for the problems of small sample, nonlinearity, over-tting and dimension disaster when compared with others, emphasizing simultaneously on minimizing empirical and expected risks. [21][22][23] The main idea of SVM is to map input space to highdimensional feature space using kernel function, and obtains the non-linear relationship between input and output variables. The generalization ability of the model can be improved by minimizing the structure risk, and obtain good statistical results in the case of fewer input samples.…”

Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pan

Zhou

et al. 2020

RSC Adv.

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“…Levy flight is a random walk strategy with non‐Gaussian distribution. During the walk, Levy flight is accompanied by frequent short walks and occasional long walks, so it effectively balances the local development and global exploration capabilities of the algorithm 50,51 . Its position update formula is as follows:

x_{i}^{t + 1} = x_{i}^{t} + α \oplus italicLevy ((), s),

where

x_{i}^{t}

is the current position of the ant, α is the random step size, ⊕ is the dot product, and the step length s can be denoted as follows:

s = \frac{μ}{{(||, ν)}^{\frac{1}{β}}},

where ν and μ parameters in the above equation have Gaussian distribution and are obtained as follows:

ν \sim N ((),, 0, σ_{ν}^{2}), μ \sim N ((),, 0, σ_{μ}^{2}),

σ_{u} = {(\frac{normalΓ ((), 1 + β) \times \sin \frac{πβ}{2}}{normalΓ ((), \frac{1 + β}{2}) \times β \times 2^{((), β - 1) / 2}})}^{\frac{1}{β}}, σ_{ν} = 1, β \in ((], 0, 2) .

…”

Section: Predictive Methods Based On Ialo‐svrmentioning

confidence: 99%

“…During the walk, Levy flight is accompanied by frequent short walks and occasional long walks, so it effectively balances the local development and global exploration capabilities of the algorithm. 50,51 Its position update formula is as follows:…”

Section: Parameter Optimization Of Alo Algorithmmentioning

confidence: 99%

Fault early warning of wind turbine gearbox based on multi‐input support vector regression and improved ant lion optimization

et al. 2021

Self Cite

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Gearbox oil temperature is one of the important indicators for gearbox condition monitoring and faults early warning. Accurately predicting the gearbox oil temperature change trend can maintain the gearbox in advance and ensure the safety and reliability of the wind turbine gearbox. The purpose of this article is to analyze the supervisory control and data acquisition (SCADA) data in wind turbines.A method based on multi-input improved ant lion optimization and support vector regression (M-IALO-SVR) proposed, which can accurately predict the gearbox oil temperature. The prediction method is compared with back propagation neural network (BPNN) and ALO-SVR methods to verify the effectiveness of the M-IALO-SVR method. To further analyze the prediction results, the 95% confidence interval processing is performed on the residuals of the prediction model, and then the trends of the mean and standard deviation of the moving window residuals are calculated. Testing SCADA data from a wind farm in northeast China, the test results show that when the gearbox is operating normally, the predicted value of the gearbox oil temperature follows the measured value very well. When the gearbox operates abnormally, its temperature deviates from the normal range, and the statistical characteristics of the residuals also change. According to the trend of the residuals statistical characteristics, the abnormal state of the gearbox can be found in time.

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“…It is suitable for nonlinear small sample problems. Least squares support vector machine (LSSVM) transforms the problem into solving linear equations and the convergence speed is faster [37][38][39][40][41][42]. Next, a brief description of its principle follows.…”

Section: Phase Space Reconstruction Theorymentioning

confidence: 99%

State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

et al. 2020

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Generally, the State-of-Health (SOH) monitoring and Remaining Useful Life (RUL) prediction and assessment of lithium-ion (Li-ion) batteries need to use sensors to obtain the degradation test data of the same type of batteries and establish the degradation model for reference. However, when the battery type is unknown, a usable reference model cannot be obtained, so its prediction and evaluation may be relatively inconvenient. In this paper, the State of-Health prediction for lithium-ion batteries based on a novel hybrid scheme is proposed. Firstly, historical charge/discharge time series and capacity series are extracted to analyze and construct Health Indicators, then using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the Health Indicator series into the trend and non-trend terms. Among them, the relatively smooth trend item data series uses the Autoregressive Integrated Moving Average model (ARIMA) for prediction; when dealing with the data series of non-trend items which are obviously non-smooth and seemingly random, the residuals predicted by ARIMA and the non-trend items obtained by CEEMDAN decomposition are combined into new non-trend items; then the least square support vector machine (LSSVM) is introduced to build a nonlinear prediction model and make predictions. Finally, combining the prediction results of the trend item data series and the non-trend item data series as a reference for the assessment of the state of health and remaining useful life. The 13 experimental results of 3 batteries verify the effectiveness of the scheme.

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A method based on improved ant lion optimization and support vector regression for remaining useful life estimation of lithium‐ion batteries

Cited by 36 publications

References 36 publications

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Pharmacokinetic–pharmacodynamic modeling for Moutan Cortex/Moutan Cortex charcoal and the contributions of the chemical component using support vector regression with particle swarm optimization

Fault early warning of wind turbine gearbox based on multi‐input support vector regression and improved ant lion optimization

State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

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