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

被引：0
作者：
Liu B. [1 ]
Yin J. [1 ]
Li R. [2 ]
机构：
[1] Institute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin
[2] Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2024年 / 52卷 / 09期
关键词：
Gaussian mixture model; Li-ion battery; particle filter; remaining useful life;
D O I：
10.19783/j.cnki.pspc.231034
中图分类号：
学科分类号：
摘要：
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. © 2024 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码：123 / 131
页数：8
相关论文
共 23 条
[1]  
ZHANG Chaolong, ZHAO Shaishai, HE Yigang, Remaining useful life prediction method for lithium-ion batteries based on ensemble empirical mode decomposition and ensemble machine learning, Power System Protection and Control, 51, 13, pp. 177-186, (2023)
[2]  
JIN Dianwei, GU Zeyu, ZHANG Zhihong, Lithium battery health degree and residual life prediction algorithm, Power System Protection and Control, 51, 1, pp. 122-130, (2023)
[3]  
CAI Tao, ZHANG Zhaocheng, YUAN Aote, Et al., Review of machine learning for safety management of li-ion battery energy storage, Power System Protection and Control, 50, 24, pp. 178-187, (2022)
[4]  
XIE Qilong, LIU Rongchuan, HUANG Jihao, Et al., Residual life prediction of lithium-ion batteries based on data preprocessing and a priori knowledge-assisted CNN-LSTM, Energy, 281, (2023)
[5]  
LU Guangzheng, ZHANG Heng, MIAO Qiang, Parallel state fusion LSTM-based early-cycle stage lithium-ion battery RUL prediction under Lebesgue sampling framework, Reliability Engineering & System Safety, 236, (2023)
[6]  
WANG Shunli, FERNANDEZ C, FAN Yongcun, Et al., A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle-adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries, Energy Science & Engineering, 8, pp. 1484-1500, (2020)
[7]  
WU Jiaju, KONG Linggang, CHENG Zheng, Et al., RUL prediction for lithium batteries using a novel ensemble learning method, Energy Reports, 8, pp. 313-326, (2022)
[8]  
XU Zhicheng, WANG Jun, LUND P D, Et al., Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model, Energy, 240, (2022)
[9]  
YANG Jufeng, CAI Yingfeng, MI C C., State-of-health estimation for lithium-ion batteries based on decoupled dynamic characteristic of constant-voltage charging current, IEEE Transactions on Transportation Electrification, 8, 2, pp. 2070-2079, (2022)
[10]  
LI Penghua, ZHANG Zijian, XIONG Qingyu, Et al., State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network, Journal of Power Sources, 459, (2020)
123