基于IBOA-PF的锂电池健康状态预测

doi:10.19799/j.cnki.2095-4239.2020.0391

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (2): 705-713.doi: 10.19799/j.cnki.2095-4239.2020.0391

基于IBOA-PF的锂电池健康状态预测

李鹏¹(), 李立伟²(), 杨玉新³

^1.青岛大学电气工程学院
^2.青岛大学威海创新研究院
^3.青岛大学图书馆，山东青岛 266071

收稿日期:2020-12-02 修回日期:2020-12-22 出版日期:2021-03-05 发布日期:2021-03-05
作者简介:李鹏（1996—），男，硕士研究生，研究方向为新能源汽车电控系统开发，E-mail：847007907@qq.com|李立伟，教授，研究方向为电力系统的智能检测和状态维修，可再生能源接入与智能配电网技术，电能质量调节与控制，高速列车运行监测及控制系统及新能源汽车电控系统开发等，E-mail：ytllw@163.com。
基金资助:
山东省自然科学基金项目(Y2008F23);山东省科技发展计划项目(2011GGB01123);山东省重点研发计划项目(2017GGX50114)

Estimation of lithium-ion battery state of health based on IBOA-PF

Peng LI¹(), Liwei LI²(), Yuxin YANG³

^1.School of Electrical Engineering of Qingdao University
^2.Weihai Innovation Institute of Qingdao University
^3.Library of Qingdao University, Qingdao 266071, Shandong, China

Received:2020-12-02 Revised:2020-12-22 Online:2021-03-05 Published:2021-03-05

摘要/Abstract

摘要：

应用传统的粒子滤波(PF)算法估计锂电池健康状态(SOH)时，会出现粒子权值退化和样本贫化而导致预测精度较低的问题。为了解决该问题，本工作提出了基于改进蝴蝶优化算法和粒子滤波(IBOA-PF)的联合算法，在基本蝴蝶优化算法(BOA)的基础上，用混沌数替代固定的切换概率，并引入共生生物搜索的互生阶段，弥补了蝴蝶算法易陷入局部最优和开发能力差的局限性，提高了BOA的收敛速度，再用蝴蝶表示粒子，用蝴蝶向食物移动的过程表示粒子变化为更符合真实后验分布的新采样值。然后基于双指数经验模型和时间指标(TI)构建了非线性系统的状态空间模型，用单纯形法改进高斯牛顿法进行参数拟合，提出了一种基于IBOA-PF的锂电池SOH估计方法。仿真实验结果表明，该方法优于传统PF方法，具有较高的精确度和较好的适应性。

关键词: 电池健康状态, 粒子滤波, 改进蝴蝶优化算法, 改进高斯牛顿法

Abstract:

When the traditional particle filter (PF) algorithm is used to estimate the state of health (SOH) of lithium-ion batteries, several problems arise, such as particle weight degeneration and species decrease, leading to lower prediction accuracy. In this paper, a novel hybrid algorithm, the improved butterfly optimization algorithm based on PF (IBOA-PF), is proposed to solve these problems. This algorithm based on the basic butterfly optimization algorithm (BOA) replaces the stable switching probability with the chaotic maps. It uses the mutualism phase of symbiosis organism search to make up for the limitations of the butterfly algorithm (i.e., it easily falls into the local optimum and has poor development ability) and improve the convergence speed of BOA. Butterflies are used to represent particles, and the process of butterflies moving to the food is similar to the change of particles having better values that are more possibly equal to the true values. This paper proposed an SOH estimation method using IBOA-PF for lithium batteries based on the double exponential model and time index (TI), constructed the state-space model of the nonlinear system, used the simplex method to improve the Gauss-Newton method for parameter fitting, and estimated SOH. The simulation results show that this method is superior to the traditional PF method, with higher accuracy and better adaptability.

Key words: state of health of battery, particle filter, improved butterfly optimization algorithm, improved Gauss-Newton algorithm

中图分类号:

TM 912

李鹏, 李立伟, 杨玉新. 基于IBOA-PF的锂电池健康状态预测[J]. 储能科学与技术, 2021, 10(2): 705-713.

Peng LI, Liwei LI, Yuxin YANG. Estimation of lithium-ion battery state of health based on IBOA-PF[J]. Energy Storage Science and Technology, 2021, 10(2): 705-713.

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参考文献 23

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参数	初始值	单纯形法	RMSE	改进法	RMSE
a₀	2	2.0327	0.2901	0.0088	0.0386
b₀	0.2	-0.1390		-0.1325
c₀	2	2.0393		2.0032
d₀	0.1	-0.0039		-0.0032
q₁	2	0.5873	0.2944	0.0494	0.0022
q₂	2	0.9094		0.9458
q₃	2	1.6964		-0.0103
p₁	2	0.9857	0.0270	1.0735	0.0023
p₂	2	0.0243	0.0270	-0.067	0.0023

算法	PF	BOA-PF	IBOA-PF
MRE	2.88%	1.80%	1.36%
RMSE	0.0073	0.0050	0.0037
MAPE	0.62%	0.47%	0.36%

算法	PF	BOA-PF	IBOA-PF
MRE	2.09%	1.51%	1.02%
RMSE	0.0058	0.0045	0.0037
MAPE	0.54%	0.42%	0.34%

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基于IBOA-PF的锂电池健康状态预测

Estimation of lithium-ion battery state of health based on IBOA-PF

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