Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

被引：11

作者：

Huang Kai ^{[1
]}

Guo Yong-Fang ^{[2
]}

Li Zhi-Gang ^{[1
]}

Lin Hsiang-Cheng ^{[3
]}

Li Ling-Ling ^{[1
]}

机构：

[1] Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China

[2] Hebei Univ Technol, Sch Comp Sci & Engn, Tianjin 300130, Peoples R China

[3] Natl Chin Yi Univ Technol, Dept Elect Engn, Taichung 41170, Taiwan

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2018年 / 2018卷

基金：

中国国家自然科学基金;

关键词：

PARTICLE SWARM OPTIMIZATION; EQUIVALENT-CIRCUIT MODELS; MANAGEMENT-SYSTEMS; CHARGE ESTIMATION; ONLINE ESTIMATION; STATE; IDENTIFICATION; PARAMETER; HYBRID; PACKS;

D O I：

10.1155/2018/3793492

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The performance behavior of the lithiwn-ion battery can be simulated by the battery model and thus applied to a variety of practical situations. Although the particle swarm optimization (PSO) algorithm has been used for the battery model development, it is usually unable to find an optimal solution during the iteration process. To resolve this problem, an adaptive random disturbance PSO algorithm is proposed. The optimal solution can be updated continuously by obtaining a new random location around the particle's historical optimal location. There are two conditions considered to perform the model process. Initially, the test operating condition is used to validate the model effectiveness. Secondly, the verification operating condition is used to validate the model generality. The performance results show that the proposed model can achieve higher precision in the lithium-ion battery behavior, and it is feasible for wide applications in industry.

引用

页数：13