基于维纳过程的锂离子电池剩余寿命预测

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李玥锌,刘淑杰,高斯博,胡娅维,张洪潮.基于维纳过程的锂离子电池剩余寿命预测[J].,2017,57(2):126-132

基于维纳过程的锂离子电池剩余寿命预测

Prediction of lithium-ion battery′s remaining useful life based on Wiener process

DOI：10.7511/dllgxb201702003

中文关键词:锂离子电池剩余寿命维纳过程参数估计粒子滤波

英文关键词:lithium-ion battery remaining useful life (RUL)Wiener process parameters estimation particle filter

基金项目:“九七三”国家重点基础研究发展计划资助项目(2011CB013401).

作者	单位
李玥锌,刘淑杰,高斯博,胡娅维,张洪潮

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中文摘要:

锂离子电池内部结构复杂，受外界影响大，使其容量退化过程具有不确定性因素而呈现随机性．对电池容量退化服从非线性维纳过程建立状态空间模型，并认为参数是服从共轭分布的随机变量，增加了模型不确定性使之更加符合锂离子电池容量的退化过程．利用自助法获得先验分布参数初始值，由共轭分布的性质可以得到后验分布的类型，由此得到简便的参数估计方法．粒子滤波可对每一时刻的参数及退化状态进行估计和更新，根据提前设定的状态阈值可以预测电池的剩余寿命．具体实例验证了方法的准确性，该方法对可靠性高、样本量少的电池的剩余寿命预测有借鉴意义．

英文摘要:

Lithium-ion battery has a complex internal structure and is easily affected by the external environment, which makes its capacity state degrade with uncertainties and randomness. State space model is used to describe the degradation process of battery capacity which obeys nonlinear Wiener process, and the parameters of state space model are subject to conjugate distributed random variables, which adds the uncertainties of the model and makes it more consistent with the degradation process of the lithium-ion batteries. Bootstrap method is used to obtain the initial parameters of the prior distribution. Besides, due to the property of conjugate distribution, the posterior distribution type is the same as the type of prior distribution, therefore, a simple parameter estimation method can be obtained. Particle filter (PF) contributes to estimate and update the parameters and degradation state at each time. According to the state threshold set in advance, remaining useful life (RUL) of the battery can be predicted. The accuracy of this method is verified by an example. It is shown that the proposed method can provide reference for remaining useful life prediction of batteries with high reliability and small sample applications.

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基于维纳过程的锂离子电池剩余寿命预测

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