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

doi:10.19799/j.cnki.2095-4239.2020.0391

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 11

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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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