基于IGWO-PF算法的无人机锂电池SOC估计

doi:10.19799/j.cnki.2095-4239.2021.0489

摘要/Abstract

摘要：

无人机复杂飞行姿态下，其锂电池的放电电流大小会发生很大变化，难以准确估算无人机剩余电量等状态参数。针对这一问题，提出一种改进的灰狼算法优化粒子滤波算法（IGWO-PF），通过粒子分布进行SOC估计。该方法中用灰狼表征粒子，通过设置狼群位置更新机制，使粒子不断接近真实的概率分布。首先用二阶戴维南等效电路构建无人机锂电池模型，计算出观测方程和状态方程。其次在无人机动态工况下，通过基于遗忘因子的最小二乘法在线完成参数辨识。然后利用IGWO-PF算法进行无人机SOC估算。最后使用MATLAB分别对IGWO-PF算法、PF算法和UKF算法在3种不同工况下进行仿真验证。结果表明：在3种工况下，IGWO-PF算法不仅在SOC估计结果上能很好地收敛到实际值，而且对于SOC估算误差能控制在1%之内比传统算法误差更准确稳定。

关键词: 参数辨识, 剩余电量估计, 灰狼算法, 粒子滤波

Abstract:

The discharge current of the UAV's lithium battery will change greatly under the complex flight attitude of the UAV, making it difficult to accurately estimate the remaining power and other state parameters of the UAV. In response to this problem, this paper proposes an improved gray wolf algorithm (IGWO) optimized particle filter (PF) algorithm, which estimates the SOC through the distribution of particles. Particles are characterized using gray wolves in this method. By setting the wolf pack location update mechanism, the particles are continuously approaching the true posterior probability distribution, thereby accurately estimating the remaining power of the UAV lithium battery. First, the complexity and accuracy are comprehensively considered. The second-order Thevenin equivalent circuit constructs the lithium-ion battery model, calculates the observation equation and the state equation, and then completes the parameter identification online by the least square method based on the forgetting factor, and then uses the gray wolf algorithm that introduces the Levi flight strategy to optimize the PF algorithm. Estimate the drone's remaining power. Finally, simulate and verify the IGWO-PF algorithm, the PF algorithm, and the UKF algorithms using MATLAB. The results show that the IGWO-PF algorithm can converge well to the actual value in the SOC estimation result and is more accurate and stable for the SOC estimation error within 1% than the traditional algorithm error. This research can more accurately estimate the remaining power of the drone during the flight.

Key words: parameter identification, remaining battery analysis, grey wolf algorithm, particle filter

中图分类号:

TM 912

袁建华, 刘雅萍, 赵子玮, 刘宇, 谢斌斌, 何宝林. 基于IGWO-PF算法的无人机锂电池SOC估计[J]. 储能科学与技术, 2022, 11(5): 1601-1607.

Jianhua YUAN, Yaping LIU, Ziwei ZHAO, Yu LIU, Binbin XIE, Baolin HE. SOC estimation of UAV lithium battery based on IGWO-PF algorithm[J]. Energy Storage Science and Technology, 2022, 11(5): 1601-1607.

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参考文献 16

1	刘泽元, 王友仁, 陈则王, 等. 基于改进EKF的飞机蓄电池在线SOC估计方法[J]. 电子测量技术, 2015, 38(7): 119-123.
	LIU Z Y, WANG Y R, CHEN Z W, et al. Method of aircraft battery on-line SOC estimation based on improved EKF algorithm[J]. Electronic Measurement Technology, 2015, 38(7): 119-123.
2	雷涛, 闵志豪, 付红杰, 等. 燃料电池无人机混合电源动态平衡能量管理策略[J]. 航空学报, 2020, 41(12): 324048.
	LEI T, MIN Z H, FU H J, et al. Dynamic balanced energy management strategies for fuel-cell hybrid power system of unmanned air vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(12): 324048.
3	GISMERO A, SCHALTZ E, STROE D I. Recursive state of charge and state of health estimation method for lithium-ion batteries based on coulomb counting and open circuit voltage[J]. Energies, 2020, 13(7): 1811.
4	苏振浩, 李晓杰, 秦晋, 等. 基于BP人工神经网络的动力电池SOC估算方法[J]. 储能科学与技术, 2019, 8(5): 868-873.
	SU Z H, LI X J, QIN J, et al. SOC estimation method of power battery based on BP artificial neural network[J]. Energy Storage Science and Technology, 2019, 8(5): 868-873.
5	盛瀚民, 肖建, 贾俊波, 等. 最小二乘支持向量机荷电状态估计方法[J]. 太阳能学报, 2015, 36(6): 1453-1458.
	SHENG H M, XIAO J, JIA J B, et al. Estimation method for state of charge based on least square support vector machine[J]. Acta Energiae Solaris Sinica, 2015, 36(6): 1453-1458.
6	KIM M J, CHAE S H, MOON Y K. Adaptive battery state-of-charge estimation method for electric vehicle battery management system[C]// 2020 International SoC Design Conference (ISOCC). IEEE, 2020: 288-289.
7	吕佳志. 基于改进粒子滤波的非线性系统故障诊断[J]. 机械制造与自动化, 2019, 48(4): 183-187.
	LÜ J Z. Fault diagnosis of nonlinear systems based on improved particle filter[J]. Machine Building & Automation, 2019, 48(4): 183-187.
8	徐超, 李立伟, 杨玉新, 等. 基于改进粒子滤波的锂电池SOH预测[J]. 储能科学与技术, 2020, 9(6): 1954-1960.
	XU C, LI L W, YANG Y X, et al. Lithium-ion battery SOH estimation based on improved particle filter[J]. Energy Storage Science and Technology, 2020, 9(6): 1954-1960.
9	华宇宁, 崔春娜. 一种基于粒子滤波的红外多目标跟踪算法[J]. 沈阳理工大学学报, 2017, 36(1): 66-70.
	HUA Y N, CUI C N. Infrared multi-target tracking algorithm based on particle filter[J]. Journal of Shenyang Ligong University, 2017, 36(1): 66-70.
10	朱超, 刘以安, 薛松. 基于混沌的萤火虫改进粒子滤波算法研究[J]. 传感器与微系统, 2017, 36(9): 106-109.
	ZHU C, LIU Y A, XUE S. Research of improved particle filtering algorithm for fireflies based on chaos[J]. Transducer and Microsystem Technologies, 2017, 36(9): 106-109.
11	姬鹏, 徐硕硕, 赵一凡, 等. 改进粒子滤波算法的车辆状态估计研究[J]. 机械设计与制造, 2020(2): 43-46, 50.
	JI P, XU S S, ZHAO Y F, et al. Research on vehicle state estimation based on improved particle filter algorithm[J]. Machinery Design & Manufacture, 2020(2): 43-46, 50.
12	张威虎, 郭明香, 贺元恺, 等. 一种改进的蝴蝶算法优化粒子滤波算法[J]. 西安科技大学学报, 2019, 39(1): 119-123.
	ZHANG W H, GUO M X, HE Y K, et al. An improved butterfly algorithm optimizing particle filter algorithm[J]. Journal of Xi'an University of Science and Technology, 2019, 39(1): 119-123.
13	郭向伟, 司阳, 高岩, 等. 动力锂电池最优等效电路模型研究[J]. 电子测量与仪器学报, 2021, 35(1): 48-55.
	GUO X W, SI Y, GAO Y, et al. Research on the optimal equivalent circuit model of lithium-ion battery[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(1): 48-55.
14	李奇, 王晓锋, 孟翔, 等. 基于在线辨识和极小值原理的PEMFC混合动力系统综合能量管理方法[J]. 中国电机工程学报, 2020, 40(21): 6991-7002.
	LI Q, WANG X F, MENG X, et al. Comprehensive energy management method of PEMFC hybrid power system based on online identification and minimal principle[J]. Proceedings of the CSEE, 2020, 40(21): 6991-7002.
15	陈德海, 王超, 朱正坤, 等. 交互多模型无迹卡尔曼滤波算法预测锂电池SOC[J]. 储能科学与技术, 2020, 9(1): 257-265.
	CHEN D H, WANG C, ZHU Z K, et al. Lithium battery state-of-charge estimation based on interactive multimodel unscented Kalman filter Algorithm[J]. Energy Storage Science and Technology, 2020, 9(1): 257-265.
16	杨晓敏. 改进灰狼算法优化支持向量机的网络流量预测[J]. 电子测量与仪器学报, 2021, 35(3): 211-217.
	YANG X M. Improved gray wolf algorithm to optimize support vector machine for network traffic prediction[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(3): 211-217.

SOC	电压/V	SOC	电压/V
1	4.1002	0.5	3.6628
0.9	4.0785	0.4	3.6891
0.8	3.9890	0.3	3.5967
0.7	3.8612	0.2	3.5701
0.6	3.7790	0.1	3.5010

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