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基于改進(jìn)粒子濾波算法的醫療鋰電池PHM系統設計
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上海第二工業(yè)大學(xué) 智能制造與控制工程學(xué)院 副教授,上海第二工業(yè)大學(xué),上海第二工業(yè)大學(xué)

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TP206

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上海第二工業(yè)大學(xué)研究生項目基金(基金號:EGD18YJ0003);


Electromedical Lithium Battery PHM System Based on Improved Particle Filter Algorithm
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School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University,,Shanghai Polytechnic University

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    摘要:

    針對維持生命的醫療電子設備的鋰離子電池維修問(wèn)題,設計了一套故障預測與健康管理系統(Prognostics and Health Management-PHM),提出了PHM系統的實(shí)現框架。通過(guò)搭建一套電池控制應力水平實(shí)驗平臺并將故障注入鋰離子電池中,來(lái)進(jìn)行數據采集。建立基于阿列紐斯模型(Arrhenius Model)的醫療電子設備的鋰離子電池模型,通過(guò)無(wú)跡粒子濾波(Unscented Particle Filter-UPF)算法和粒子濾波(Particle Filter-PF)算法計算出實(shí)時(shí)故障的概率并給出剩余壽命預測以及健康管理維護方法。通過(guò)Matlab對比UPF和PF的預測剩余壽命的仿真結果與實(shí)驗所測數據的吻合度,選出UPF算法為最優(yōu)算法并及時(shí)診斷故障,為后續維護提供建議。

    Abstract:

    Aiming at the maintenance of Li-ion battery for life-supporting medical electronic equipment, a set of Prognostics and Health Management (PHM) was designed and the implementation framework of the PHM system was proposed. A data-acquisition process was conducted by constructing a battery-controlled stress level test platform and injecting faults into a lithium ion battery. A lithium-ion battery model based on the Arrhenius model of a medical electronic device was established. The probability of real-time failures was calculated by using the UPF and PF algorithms, and the remaining life prediction and health management maintenance methods was given. Comparing the simulation results of the predicted remaining life of Unscented Particle Filter and Particle Filter with the experimentally measured data through Matlab, the UPF algorithm was selected as the optimal algorithm and the faults were diagnosed in time to provide suggestions for subsequent maintenance.

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何成,劉長(cháng)春,武洋.基于改進(jìn)粒子濾波算法的醫療鋰電池PHM系統設計計算機測量與控制[J].,2018,26(11):63-67.

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  • 收稿日期:2018-04-23
  • 最后修改日期:2018-05-22
  • 錄用日期:2018-05-22
  • 在線(xiàn)發(fā)布日期: 2018-11-26
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