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    A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction

    Source: Journal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 006::page 061001-1
    Author:
    Hu, Xiaosong
    ,
    Yang, Xin
    ,
    Feng, Fei
    ,
    Liu, Kailong
    ,
    Lin, Xianke
    DOI: 10.1115/1.4049234
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can improve the durability, reliability, and maintainability of battery system operation in electric vehicles. To achieve high-accuracy RUL predictions, it is necessary to develop an effective method for long-term nonlinear degradation prediction and quantify the uncertainty of the prediction results. To this end, this paper proposes a hybrid approach for lithium-ion battery RUL prediction based on particle filter (PF) and long short-term memory (LSTM) neural network. First, based on the training set, the model parameters are iteratively updated using the PF algorithm. Second, the LSTM model parameters are obtained using the training set. The mean and standard deviation in the prediction stage are obtained through Monte Carlo (MC) dropout. Finally, the mean value predicted by MC-dropout is used as the measurement for the PF in the prediction phase, the standard deviation represents the uncertainty of the prediction result, and the mean and standard deviation are integrated into the measurement equation of the model. The experimental results show that the proposed hybrid approach has better prediction accuracy than the PF, LSTM algorithm, and two other types of hybrid approaches. The hybrid approach can obtain a narrower confidence interval.
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      A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4277110
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    contributor authorHu, Xiaosong
    contributor authorYang, Xin
    contributor authorFeng, Fei
    contributor authorLiu, Kailong
    contributor authorLin, Xianke
    date accessioned2022-02-05T22:12:01Z
    date available2022-02-05T22:12:01Z
    date copyright1/5/2021 12:00:00 AM
    date issued2021
    identifier issn0022-0434
    identifier otherds_143_06_061001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4277110
    description abstractAccurate prediction of the remaining useful life (RUL) of lithium-ion batteries can improve the durability, reliability, and maintainability of battery system operation in electric vehicles. To achieve high-accuracy RUL predictions, it is necessary to develop an effective method for long-term nonlinear degradation prediction and quantify the uncertainty of the prediction results. To this end, this paper proposes a hybrid approach for lithium-ion battery RUL prediction based on particle filter (PF) and long short-term memory (LSTM) neural network. First, based on the training set, the model parameters are iteratively updated using the PF algorithm. Second, the LSTM model parameters are obtained using the training set. The mean and standard deviation in the prediction stage are obtained through Monte Carlo (MC) dropout. Finally, the mean value predicted by MC-dropout is used as the measurement for the PF in the prediction phase, the standard deviation represents the uncertainty of the prediction result, and the mean and standard deviation are integrated into the measurement equation of the model. The experimental results show that the proposed hybrid approach has better prediction accuracy than the PF, LSTM algorithm, and two other types of hybrid approaches. The hybrid approach can obtain a narrower confidence interval.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction
    typeJournal Paper
    journal volume143
    journal issue6
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4049234
    journal fristpage061001-1
    journal lastpage061001-13
    page13
    treeJournal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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