Enhancing the Lithium-ion battery life predictability using a hybrid method

Li, Lingling; Liu, Zhi Feng; Tseng, Ming‐Lang; Chiu, Anthony S.F.

doi:10.1016/j.asoc.2018.10.014

Cited by 71 publications

(29 citation statements)

References 27 publications

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“…Besides, the

RMSE

value of the QPSO‐SVR method is 0.02, whereas the proposed method is two times smaller than the QPSO‐SVR method (it should be noted that the “ / ” represents the reference is not describe the

RMSE

value). For battery B6, the

RMSE

value of the proposed method is two times smaller than that of the IBSA‐LSSVM method in Ref . Moreover, the

E_{r}

value of the proposed method is smaller than other two methods.…”

Section: Rul Prediction Of Lib Based On Ialo‐svrmentioning

confidence: 75%

“…Table shows that the test functions. And the parameter settings of the four algorithms are shown in Table . The population size (

Ps

) is 50, and the maximum number of iterations (

M a x_i t e r

) is 500 for each algorithm.…”

Section: Parameter Identification Using Svr Optimized By Ialomentioning

confidence: 99%

“…Gao et al proposed a multikernel SVM (MSVM) on the basis of polynomial kernel and radial basis kernel (RBF) function, and the parameters of the MSVM method are obtained by the PSO algorithm. Li et al proposed the improved bird swarm algorithm (IBSA) to optimize least squares SVM (LSSVM) and improve the RUL prediction accuracy. However, a problem is how to select the optimal parameters based on SVR method and provide high prediction accuracy and prediction stability when used to predict the RUL of LIBs.…”

Section: Introductionmentioning

confidence: 99%

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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

Wang

Li³

et al. 2019

Energy Science & Engineering

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Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis.

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“…Besides, the

RMSE

value of the QPSO‐SVR method is 0.02, whereas the proposed method is two times smaller than the QPSO‐SVR method (it should be noted that the “ / ” represents the reference is not describe the

RMSE

value). For battery B6, the

RMSE

value of the proposed method is two times smaller than that of the IBSA‐LSSVM method in Ref . Moreover, the

E_{r}

value of the proposed method is smaller than other two methods.…”

Section: Rul Prediction Of Lib Based On Ialo‐svrmentioning

confidence: 75%

“…Table shows that the test functions. And the parameter settings of the four algorithms are shown in Table . The population size (

Ps

) is 50, and the maximum number of iterations (

M a x_i t e r

) is 500 for each algorithm.…”

Section: Parameter Identification Using Svr Optimized By Ialomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wang

Li³

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…While the nonparametric model method overcomes these weaknesses, and it can adapt to electrical load forecasting with nonlinearity, uncertainty and time-varying nature [6]. e methods based on nonparametric models mainly include wavelet analysis method [7,8], grey model method [9], support vector machine (SVM) [10] and artificial neural network method [11][12][13], etc. ese methods could consider the nonlinearity of the electrical load, and the law of the electrical load could be found through fitting and approximating of the original data.…”

Section: Introductionmentioning

confidence: 99%

“…It can establish a decision function based on a small number of support vectors, effectively avoiding dimensionality disasters. In literature [10], the improved bird swarm algorithm (IBSA) is used to optimize the parameters of the LS-SVM, and then conduct the prediction using the optimized model. However, SVM is limited to small cluster samples.…”

Section: Introductionmentioning

confidence: 99%

Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Zhang

Liu

Li³

et al. 2020

Complexity

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Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

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Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

Zheng,

Wu,

Tao

2024

Quality & Reliability Eng

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As one of the key technologies in battery management system, accurate remaining useful life (RUL) prediction is critical to guarantee the reliability and safety for electrical equipment. However, the generalization and robustness of a single method are limited. A novel fusion data‐driven RUL prediction method CSSA‐ELM‐LSSVR based on charging‐discharging health features extraction is proposed in this paper, which fusions chaotic sparrow search algorithm (CSSA), extreme learning machine (ELM), and least squares support vector regression (LSSVR). First, four health indicators (HIs) are extracted from the charging‐discharging process, which can reflect the battery degradation phenomenon from multiple perspectives. Then, pearson correlation coefficient is used to numerically analyze the correlation between HIs and battery aging capacities. Second, the extracted HIs are used as the inputs for ELM and LSSVR to predict the degradation trend of battery, where CSSA is used for hyperparameters optimization in ELM. Finally, considering that CSSA‐ELM can capture the general trend of degradation curves, while LSSVR can trace the detail changes, a fusion framework based on CSSA‐ELM and LSSVR is proposed for RUL prediction. Two weighting schemes, namely precision‐based weighting (PW) and random forest regressor‐based weighting (RFRW) are put forward to fix the weights of CSSA‐ELM and LSSVR algorithms. Two publicly available datasets from National Aeronautics and Space Administration (NASA) and MIT are adopted to verify the feasibility and effectiveness of the proposed method. The results indicate that the proposed method with any weighting scheme has an overall superior prediction performance for different kinds of batteries compared with CSSA‐ELM, LSSVR, convolution neural network and long short term memory. Moreover, the RFRW scheme has better overall performance. Specifically, the maximum root mean square error of the predicted method is 2.5126%, the mean absolute percentage error is 12.9167%, the mean absolute error is 1.8376%, the predicted RUL errors are within one cycle, and the determination coefficient R2 is above 0.97.

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Enhancing the Lithium-ion battery life predictability using a hybrid method

Cited by 71 publications

References 27 publications

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

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

Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

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