基于蒙特卡洛和SH-AUKF算法的锂电池SOC估计*

doi:10.11985/2022.03.008

摘要/Abstract

摘要：

针对锂离子电池荷电状态(State of charge,SOC)估计精度低的问题,将Sage-Husa自适应算法与无迹卡尔曼滤波算法相结合,提出了一种可以对系统噪声进行不断更新和修正的自适应滤波新算法——SH-AUKF算法。在动态应力测试(Dynamic stress test,DST)工况下,采用无迹卡尔曼滤波(Unscented Kalman filter,UKF)、自适应无迹卡尔曼滤波(Adaptive unscented Kalman filter,AUKF)和SH-AUKF三种算法分别对SOC进行估计。结果表明,SH-AUKF算法估计SOC的误差最小,估计精度最高。与UKF相比,SH-AUKF算法的估计精度提高了45.4%;与AUKF相比,SH-AUKF算法的估计精度提高了14.3%。为了进一步降低噪声干扰的偶然性和突发性对SOC估计的影响,在估计过程中加入了蒙特卡洛采样方法。结果表明,融合了蒙特卡洛方法的SH-AUKF算法估计SOC时,估计误差区间仅为±1×10^-3,有效提高了估计精度。

关键词: 锂离子电池, 荷电状态, Sage-Husa, 蒙特卡洛

Abstract:

Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10^-3, which effectively improves the estimation accuracy.

Key words: Lithium-ion battery, state of charge, Sage-Husa, Monte Carlo

中图分类号:

TM911

巫春玲, 程琰清, 徐先峰, 孟锦豪, 谢美美. 基于蒙特卡洛和SH-AUKF算法的锂电池SOC估计^*[J]. 电气工程学报, 2022, 17(3): 66-75.

WU Chunling, CHENG Yanqing, XU Xianfeng, MENG Jinhao, XIE Meimei. SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm[J]. Journal of Electrical Engineering, 2022, 17(3): 66-75.

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参数	数值
容量/(mA·h)	2 000
直径/mm	18.33±0.07
长度/mm	64.85±0.15
额定电压/V	3.7
充电截止电压/V	4.2
放电截止电压/V	2.75

参数	数值
欧姆内阻R₀/Ω	0.102
极化内阻R_p/Ω	0.266
极化电容C_p/F	21.125
充电内阻R_c/Ω	0.023
放电内阻R_d/Ω	0.026

算法	最大误差值	MAE	RMSE
UKF	0.007 375	0.003 062	0.002 828
AUKF	0.006 432	0.002 780	0.002 779
SH-AUKF	0.004 311	0.000 927	0.000 050

算法	最大误差值	MAE	RMSE
UKF	0.004 666	0.002 972	0.002 711
AUKF	0.003 443	0.002 587	0.002 477
SH-AUKF	0.000 771	0.000 174	0.000 025

方案	误差区间
方案一	±3×10^-3
方案二	±1×10^-3