|Table of Contents|

State of charge estimation of lithium-ion batteries based on strong tracking adaptive cubature Kalman filter algorithm(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2025年2期
Page:
165-176
Research Field:
汽车与机械工程
Publishing date:

Info

Title:
State of charge estimation of lithium-ion batteries based on strong tracking adaptive cubature Kalman filter algorithm
Author(s):
WU Chun-ling1;  ZHAO Yu-bing1;  GENG Li-min1;  CHEN Hao1;  LIU Pan-zhi1;  ZHAO Xuan2
(1. School of Energy and Electrical Engineering, Chang'an University, Xi'an 710064,Shaanxi, China; 2. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
automotive engineering;  lithium-ion battery;  cubature Kalman filter;  strong tracking filter;  non-Gaussian noise
PACS:
TM912
DOI:
10.19721/j.cnki.1671-8879.2025.02.014
Abstract:
To address the issues of system noise statistical uncertainty and battery model inaccuracy in traditional Kalman filtering algorithms for state of charge estimation under non-Gaussian noise interference, a new algorithm combining strong tracking filtering and adaptive cubature Kalman filtering was proposed, namely the strong tracking adaptive cubature Kalman filter. The advantages of STF and CKF were integrated by utilizing the existing cubature Kalman filtering algorithm. Time-varying fading factors were introduced into the time update and measurement update equations to ensure the orthogonality of the output residual sequence and make the residuals conform to the characteristics of Gaussian white noise. By adaptively adjusting the gain matrix online, the algorithm effectively enhances the system's ability to track abrupt state changes. To verify the effectiveness of the proposed algorithm, it was compared with the strong tracking adaptive extended Kalman filter and the strong tracking cubature Kalman filter in estimating terminal voltage and SOC under both Gaussian noise and non-Gaussian interference. The research results show that under Gaussian noise interference, the SOC estimation accuracy of ST-ACKF improves by 49% and 16% compared to ST-AEKF and ST-CKF, respectively, with an increase in computation time of 1.259 1 s and 0.352 3 s. Under non-Gaussian noise interference, the SOC estimation accuracy improves by 62% and 18% compared to ST-AEKF and ST-CKF, respectively, with an increase in computation time of 1.195 5 s and 0.206 3 s. The proposed algorithm proves to be an effective SOC estimation method under non-Gaussian noise interference, offering high estimation accuracy, good numerical stability, and strong robustness.6 tabs, 15 figs, 27 refs.

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Last Update: 2025-04-01