Battery state estimation of least squares support vector machinebased on particle swarm optimization

doi:10.19799/j.cnki.2095-4239.2020.0075

Abstract

Abstract:

As one of the important parameters of battery management system (BMS), it is very important to estimate SOC accurately. It is difficult to estimate SOC accurately because SOC is often affected by many factors such as voltage, current, charge discharge efficiency and so on. In order to improve the accuracy of SOC estimation, the least square support vector machine (LSSVM) based SOC estimation model for lithium-ion battery is proposed. The current, voltage and temperature of the battery are taken as the input vector and SOC as the output vector of the model. In order to better obtain the parameters of LSSVM model, an adaptive particle swarm optimization algorithm is proposed to optimize the parameters, so as to obtain a high-precision SOC estimation model. Compared with the PSO optimized LSSVM and support vector machine (SVM) neural network (NN), the accuracy error of SOC estimation of the proposed model is 1.63%, which proves the effectiveness of the algorithm.

Key words: lithium-ion battery, SOC, least squares support vector machine (LSSVM), particle swarm optimization

CLC Number:

TM 911

WANG Yuyuan, LI Jiabo, ZHANG Fu. Battery state estimation of least squares support vector machinebased on particle swarm optimization[J]. Energy Storage Science and Technology, 2020, 9(4): 1153-1158.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Table 2

Parameter optimization results"

方法	$σ$	c
PSO	865.1	0.03
自适应PSO	567.6	0.4

Table 2

Fig.5

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Fig.8

Fig.9

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Table 3

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名称	参数值
额定容量/A·h	2.25
最大电压/V	4.5
截止电压/V	2.8
电池尺寸	?18.6 mm × 64.9 mm
阳极材料及厚度	Li_xC₆/0.165 mm
阴极材料及厚度	LiCoO₂/0.159 mm

方法	RMSE/%	MAE/%
自适应PS0-LSSVM	1.63	2.05
PS0-LSSVM	2.65	5.12
SVM	4.57	12.89
NN	7.02	39..57

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