Remaining useful life prediction with probability distribution for lithium-ion batteries based on edge and cloud collaborative computation

Yong, Zhou; Gu, Huanghui; Su, Teng; Han, Xiao; Lu, Languang; Zheng, Yuejiu

doi:10.1016/j.est.2021.103342

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…PSO algorithm has the advantages of simple structure and fast convergence speed. It is widely used in parameter optimization and pattern recognition (He et al, 2017; Zhou et al, 2021). The process of PSO algorithm to optimize model parameters is as follows:…”

Section: Soft Measurement Model Construction For F-cao Contentmentioning

confidence: 99%

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Zhang

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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The content of free calcium oxide (f-CaO) in cement clinker is a key indicator for testing the quality of cement clinker. To address the problem that the content of f-CaO cannot be detected online, a multi-model fusion soft measurement method based on K-means++ clustering, empirical modal decomposition combined with multi-kernel relevance vector machines (EMD-MKRVM) is proposed to predict f-CaO content under different operating conditions. First, time-series analysis and matching of input variables with f-CaO content were performed, based on which a combination of empirical modal decomposition (EMD) and sample entropy (SE) denoising method was used to filter out high- frequency noise from the original data and extract effective signal information for reconstruction. Second, the K-means++ algorithm was used to cluster the processed training sample data, and multi-kernel relevance vector machine (MKRVM) sub-models were established by training the sample data of each sub-class and then the affiliation between the test samples and each sub-class was calculated as the weights of the sub-model output values, and the final model prediction output was obtained by multi-model fusion. Finally, the real data from cement plants were used for validation. The results show that compared with the single MKRVM model, multi–relevance vector machine (RVM) model, multi–support vector machine (SVM) model, and multi-MKRVM model using only EMD denoising method, the mean absolute error (MAE) of the multi-MKRVM model proposed in this paper was reduced by 42%, 7%, 14%, and 35%; root mean square error (RMSE) is reduced by 28%, 10%, 12%, and 21%; squares due to error (SSE) is reduced by 51%, 24%, 27%, and 41%; Theils inequality coefficient (TIC) is reduced by 27%, 17%, 21%, and 19%; [Formula: see text] is improved by 64%, 18%, 39%, and 91%; and Index of agreement (IA) is improved by 28%, 9%, 22%, and 13%. The multi-MKRVM model proposed in this paper has higher accuracy, better generalization ability and stability, and provides an effective method for f-CaO content prediction under complex multiple working conditions.

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Section: Soft Measurement Model Construction For F-cao Contentmentioning

confidence: 99%

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Zhang

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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“…Although lithium batteries have advantages, such as high energy density, a wide operating temperature range, and high charging efficiency [3], the degradation of the remaining capacity for lithium batteries may accelerate the aging of connected batteries, reduce the endurance of devices, influence the security, and may cause loss of property [4,5]. In order to determine the possible remaining usage time for batteries before their remaining capacity reaches the failure threshold so that they can be replaced in time, the prediction of the remaining useful life (RUL) for lithium batteries is essential [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

Gu,

Liu

2024

Applied Sciences

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With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.

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“…Integrating cloud computation and extensive data resources into real‐time vehicle battery management is realized by establishing a novel cloud‐edge battery management system (CEBMS). Zhou et al 16 propose the architecture of the combination of the BMS and the cloud big data platform. Cloud terminals' multithread online computing capability addresses the issues with data‐driven methods for estimating SOC.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

Feng,

Cai,

et al. 2023

Energy Science & Engineering

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In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities.

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Remaining useful life prediction with probability distribution for lithium-ion batteries based on edge and cloud collaborative computation

Cited by 8 publications

References 26 publications

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

A multi-model fusion soft measurement method for cement clinker f-CaO content based on K-means ++ and EMD-MKRVM

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

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