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    Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

    Source: Journal of Electrochemical Energy Conversion and Storage:;2022:;volume( 019 ):;issue: 003::page 31004-1
    Author:
    He, Ning
    ,
    Qian, Cheng
    ,
    He, Lile
    DOI: 10.1115/1.4053141
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: As an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life (RUL) prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter (PF) are developed. First, the adaptive hybrid model is constructed, which is a combination of empirical model and long short-term memory (LSTM) neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search (BAS) based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.
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      Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4285281
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    • Journal of Electrochemical Energy Conversion and Storage

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    contributor authorHe, Ning
    contributor authorQian, Cheng
    contributor authorHe, Lile
    date accessioned2022-05-08T09:33:33Z
    date available2022-05-08T09:33:33Z
    date copyright2/4/2022 12:00:00 AM
    date issued2022
    identifier issn2381-6872
    identifier otherjeecs_19_3_031004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4285281
    description abstractAs an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life (RUL) prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter (PF) are developed. First, the adaptive hybrid model is constructed, which is a combination of empirical model and long short-term memory (LSTM) neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search (BAS) based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleShort-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter
    typeJournal Paper
    journal volume19
    journal issue3
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4053141
    journal fristpage31004-1
    journal lastpage31004-13
    page13
    treeJournal of Electrochemical Energy Conversion and Storage:;2022:;volume( 019 ):;issue: 003
    contenttypeFulltext
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