Interacting multiple model particle filter for prognostics of lithium-ion batteries
Interacting multiple model particle filter for prognostics of lithium-ion batteries
复制标题
DOI：
10.1016/j.microrel.2017.02.003
复制
发表时间：
2017-03-01
影响因子：
1.6
通讯作者：
Ye, Zhe
中科院分区：
文献类型：
--
作者：
Su, Xiaohong;Wang, Shuai;Ye, Zhe
提出了一种基于相互作用多模型粒子滤波（IMMPF）的数据驱动预测方法，用于确定锂离子电池的剩余使用寿命（RUL）和相关不确定性的概率分布函数（PDF）。该方法适用于不同的状态方程。建立电池容量退化模型对于预测锂离子电池的RUL是非常重要的。在本研究中，改进了各种锂离子电池容量模型（即多项式模型、指数模型和Verhulst模型）。在此基础上，建立了3种不同的单步状态转换方程，并应用impf方法对锂离子电池的RUL进行了估计。通过结合每个单独模型获得的PDF来获得预测规则的PDF。我们进行了四个案例研究来验证所提出的方法。结果表明：(1)3种改进模型所需参数比原模型少；(2)所提出的预测方法稳定，预测精度高；(3)所提出的方法缩小了锂离子电池RUL预测的不确定性PDF。(C) 2017 Elsevier Ltd.版权所有。
We propose a new data-driven prognostic method based on the interacting multiple model particle filter (IMMPF) for determining the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the associated uncertainty. The method applies the IMMPF to different state equations. Modeling the battery capacity degradation is very important for predicting the RUL of Li-ion batteries. In this study, improvements are made on various Li-ion battery capacity models (i.e., polynomial, exponential, and Verhulst models). Further, three different one-step state transition equations are developed, and the IMMPF method is applied to estimate the RUL of Li-ion batteries with the use of the three improved models. The PDF of the predicted RUL is obtained by combining the PDFs obtained with each individual model. We conduct four case studies to validate the proposed method. The results are as follows: (1) the three improved models require fewer parameters than the original models, (2) the proposed prognostic method shows stable and high prediction accuracy, and (3) the proposed method narrows the uncertainty PDF of the predicted RUL of Li-ion batteries. (C) 2017 Elsevier Ltd. All rights reserved.