基于AUKF算法的锂离子电池SOC估计
SOC estimation of Li-ion battery based on AUKF algorithm
杜涵 1马雁 1王非 2周永年 3辛业春4
作者信息
- 1. 郑州电力高等专科学校电力工程学院,河南 郑州 450000
- 2. 国家电投集团河南电力有限公司,河南 郑州 450000
- 3. 中国科学院上海高等研究院,上海 201210
- 4. 东北电力大学电气工程学院,吉林 吉林 132012
- 折叠
摘要
准确的荷电状态(SOC)估计有助于电池管理系统延长电池寿命并确保电池安全.由于协方差矩阵的伪正定性和噪声统计误差的累积,使用卡尔曼滤波算法对锂离子电池的SOC进行估计通常不准确.为此,提出基于自适应无迹卡尔曼滤波(AUKF)的锂离子电池SOC估计方法.该方法由无迹卡尔曼滤波(UKF)和自适应算法组成.与UKF比较,验证所提方法在不同老化程度下SOC估计的准确性.所提方法显示出高SOC估计精度,误差在 0.5%以内.
Abstract
Accurate state of charge(SOC)estimation helps battery management systems extend battery life and ensure battery safety.The estimation of SOC of Li-ion battery using the Kalman filtering algorithm is usually inaccurate due to the pseudo-positive characterization of the covariance matrix and the accumulation of noise statistical errors.Therefore,a SOC estimation method for Li-ion battery based on an adaptive unscented Kalman filter(AUKF)is proposed.The method consists of unscented Kalman filter(UKF)and adaptive algorithm.The accuracy of the proposed method for SOC estimation at different aging levels is verified by comparing with UKF.The proposed algorithm shows high SOC estimation accuracy with error within 0.5%.
关键词
磷酸铁锂/锂离子电池/自适应无迹卡尔曼滤波(AUKF)/荷电状态(SOC)Key words
lithium iron phosphate/Li-ion battery/adaptive unscented Kalman filter(AUKF)/state of charge(SOC)引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据