Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

被引：152
|
作者：
Zhang, Liqiang [1 ]
Wang, Lixin [1 ]
Hinds, Gareth [2 ]
Lyu, Chao [1 ]
Zheng, Jun [1 ]
Li, Junfu [1 ]
机构：
[1] Harbin Inst Technol, Sch Elect Engn & Automat, Harbin 150001, Peoples R China
[2] Natl Phys Lab, Teddington TW11 0LW, Middx, England
来源：
JOURNAL OF POWER SOURCES | 2014年 / 270卷
基金：
中国国家自然科学基金;
关键词：
Parameter identification; Multi-objective genetic algorithm; Multi-physics model; Lithium-ion battery; PARAMETER SENSITIVITY-ANALYSIS; CAPACITY FADE ANALYSIS; CYCLE LIFE; EXTRACTION; DISCHARGE; CHARGE;
D O I：
10.1016/j.jpowsour.2014.07.110
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 degrees C and 30 degrees C are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：367 / 378
页数：12
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