SOC of estimation of lithium battery based on IACO-PF

doi:10.19799/j.cnki.2095-4239.2020.0421

Abstract

Abstract:

An improved ant colony optimization algorithm (IACO)-optimized particle filter (PF) is proposed for battery state of charge (SOC) estimation, and it is used to solve the particle depletion problem caused by the traditional particle filter algorithm SOC estimation. The ants replace the particles and reposition them before the update step to solve the particle depletion problem by increasing the diversity of the particles. Combined with the second-order Thevenin battery equivalent model, the state and observation equations required by the algorithm are obtained, and parameter identification is then performed in accordance with the pulse discharge experiment. The IACO-PF and PF algorithms are used to estimate the SOC under pulse discharge and DST operating conditions. The experimental results show that the lithium battery SOC estimation result based on the IACO-PF algorithm is more effective and accurate than the traditional PF algorithm.

Key words: lithium battery, state of charge, ant colony optimization, particle filter

CLC Number:

TM 912

Chengxin SHAN, Liwei LI, Yuxin YANG. SOC of estimation of lithium battery based on IACO-PF[J]. Energy Storage Science and Technology, 2021, 10(3): 1145-1152.

Figures/Tables 11

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估算方法	优点	缺点
安时积分法	方法简单，易于实现	估计误差随时间累积，估算结果受影响
开路电压法	方法简单，易于实现	耗时长，很难应用于在线估计
神经网络法	对电池模型依赖度低	需要大量训练数据，算法复杂，计算机成本高
卡尔曼滤波法	精度高，适用于SOC估计	对电池模型依赖度高
粒子滤波法	估算精度高，适用于非线性非高斯系统	粒子数目过多时，计算量大，不易实现

SOC	R₀	R₁	C₁	R₂	C₂
0.9	0.0169	0.0224	716.80	0.0439	3847.9
0.8	0.0183	0.0210	718.61	0.0514	3411.1
0.7	0.0196	0.0192	684.71	0.0669	2789.1
0.6	0.0228	0.0090	2597.9	0.0679	1772.5
0.5	0.0223	0.0229	803.45	0.0763	2651.4
0.4	0.0264	0.0202	1226.3	0.0851	1683.6
0.3	0.0251	0.0254	1006.9	0.0497	3030.4
0.2	0.0270	0.0200	1134.2	0.0611	2701.5
0.1	0.0294	0.0159	1574.1	0.0885	1756.4

算法	均方根误差	最大绝对误差
PF算法	0.9394	2.1795
IACO-PF算法	0.2969	0.6750

算法	均方根误差	最大绝对误差
PF算法	1.2429	2.5138
IACO-PF算法	0.5220	1.1405

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