基于改进粒子滤波的锂离子电池剩余寿命预测  

Improved particle filter algorithm for remaining useful life prediction of lithium-ion batteries

作　　者：刘博 尹杰 李然[2] LIU Bo;YIN Jie;LI Ran(Institute of Sensor and Reliability Engineering,Harbin University of Science and Technology,Harbin 150080,China;Automotive Electronic Drive Control and System Integration Engineering Research Center,)

机构地区：[1]哈尔滨理工大学传感器与可靠性工程研究所,黑龙江哈尔滨150080 [2]汽车电子驱动控制与系统集成教育部工程研究中心,黑龙江哈尔滨150080

出　　处：《电力系统保护与控制》2024年第9期123-131,共9页Power System Protection and Control

基　　金：黑龙江省自然科学基金项目资助(LH2022E088);教育部联合发展基金项目资助(8091B022133)。

摘　　要：针对锂离子电池剩余寿命预测精度低、泛化能力差等问题,提出基于改进粒子滤波的预测方案。首先,提出双高斯模型作为退化经验模型,拟合锂离子电池的容量退化过程。然后,通过先验知识设置退化模型的初始参数,并利用粒子滤波方法进行参数更新。针对预测过程中出现的粒子退化问题,提出高斯混合方法进行粒子重采样,拟合重采样过程中粒子复杂的非线性分布和长尾分布,保证预测结果的概率密度分布状况均匀且集中。最后在不同的数据集上进行了实验验证,结果表明所提出的改进粒子滤波方案具有较高的精度和较强的鲁棒性。A prediction method based on improved particle filtering is proposed to improve the low accuracy and poor generalizability of the remaining life prediction of lithium-ion batteries.First,a double Gaussian model is taken as a degradation empirical model to fit the capacity degradation process of lithium-ion batteries.Then the initial parameters of the degradation model are set by using a priori knowledge,and the particle filtering method is used to update the parameters.The Gaussian mixture method for particle resampling is proposed to solve the particle degradation problem.This fits the complex nonlinear distribution and long-tailed distribution of particles in the resampling process,and ensures that the probability density distribution status of the prediction results is uniform and concentrated.Finally,experimental validation is carried out on different datasets,and the results show that the improved particle filtering method proposed has high accuracy and strong robustness.

关 键 词：锂离子电池 剩余寿命预测 粒子滤波 高斯混合模型 

分 类 号：TM912[电气工程—电力电子与电力传动] TN713[电子电信—电路与系统]