[1]巫春玲,赵玉冰,耿莉敏,等.基于强跟踪自适应容积卡尔曼滤波算法的锂离子电池SOC估计[J].长安大学学报(自然科学版),2025,45(2):165-176.[doi:10.19721/j.cnki.1671-8879.2025.02.014]
　WU Chun-ling,ZHAO Yu-bing,GENG Li-min,et al.State of charge estimation of lithium-ion batteries based on strong tracking adaptive cubature Kalman filter algorithm[J].Journal of Chang’an University (Natural Science Edition),2025,45(2):165-176.[doi:10.19721/j.cnki.1671-8879.2025.02.014]
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基于强跟踪自适应容积卡尔曼滤波算法的锂离子电池SOC估计()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第45卷
期数:
2025年2期
页码:
165-176
栏目:
汽车与机械工程
出版日期:
2025-03-31

文章信息/Info

Title:
State of charge estimation of lithium-ion batteries based on strong tracking adaptive cubature Kalman filter algorithm
文章编号:
1671-8879(2025)02-0165-12
作者:
巫春玲1; 赵玉冰1; 耿莉敏1; 陈 昊1; 刘盼芝1; 赵 轩2
(1. 长安大学 能源与电气工程学院,陕西 西安 710064; 2. 长安大学 汽车学院,陕西 西安 710064)
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)
关键词:
汽车工程;  锂离子电池;  容积卡尔曼滤波;  强跟踪滤波器;  非高斯噪声
Keywords:
automotive engineering;  lithium-ion battery;  cubature Kalman filter;  strong tracking filter;  non-Gaussian noise
分类号:
TM912
DOI:
10.19721/j.cnki.1671-8879.2025.02.014
文献标志码:
A
摘要:
针对非高斯噪声干扰下传统卡尔曼滤波算法对电池荷电状态(SOC)估计存在系统噪声统计不确定性和电池模型不准确的问题,提出一种将强跟踪滤波和自适应容积卡尔曼滤波算法相结合的新算法,即强跟踪自适应容积卡尔曼滤波算法(ST-ACKF)。该算法兼有STF和CKF的优点,即利用现有的容积卡尔曼滤波算法,在时间更新和测量更新方程中引入时变渐消因子,确保输出残差序列正交,并使残差符合高斯白噪声特性。通过在线调整增益矩阵,该算法可有效提升系统对突变状态的跟踪能力。在ST-ACKF的基础上应用Sage-Husa噪声估值器对噪声统计特性进行在线估计,通过建立自适应协方差矩阵对过程噪声方差进行更新来进一步减小荷电状态估计误差,增强算法对噪声统计特性变化的自适应能力。为验证该算法的有效性,将提出算法与强跟踪自适应扩展卡尔曼滤波算法(ST-AEKF)和强跟踪容积卡尔曼滤波算法(ST-CKF)分别在高斯噪声和非高斯干扰下进行端电压和SOC的估计对比。研究结果表明:在高斯噪声干扰下,与ST-AEKF和ST-CKF相比,ST-ACKF的SOC估计精度分别提高了49%、16%,运行时间增加了1.259 1、0.352 3 s; 在非高斯噪声干扰下,与ST-AEKF和ST-CKF相比,ST-ACKF的SOC估计精度分别提高了62%、18%,运行时间增加了1.195 5、0.206 3 s; 提出算法在非高斯噪声干扰下是一种估计精度高、数值稳定性好且鲁棒性强的有效SOC估计方法。
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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备注/Memo

备注/Memo:
收稿日期:2024-09-15
基金项目:国家重点研发计划项目(2021YFB2601304); 陕西省重点研发计划项目(2022GY-193); 陕西省教育厅服务地方专项科学研究计划项目(23JE021); 中央高校基本科研业务费专项资金项目(300102383203); 陕西省创新能力支撑计划项目(2021TD-28,2022KXJ-144)
作者简介:巫春玲(1978-),女,宁夏中卫人,副教授,硕士研究生导师,E-mail:wuchl@chd.edu.cn。
通信作者:刘盼芝(1981-),女,河北廊坊人,副教授,工学博士,E-mail:liupz@chd.edu.cn。
更新日期/Last Update: 2025-04-01