Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications

被引：223
作者：
Chen, Zheng
Mi, Chunting Chris [1 ]
Fu, Yuhong
Xu, Jun
Gong, Xianzhi
机构：
[1] Univ Michigan, Dept Elect, Dearborn, MI 48188 USA
来源：
JOURNAL OF POWER SOURCES | 2013年 / 240卷
关键词：
State of health (SOH); Electric vehicles; Battery model; Genetic algorithm; Diffusion capacitance; Prediction-error minimization; LITHIUM-ION BATTERIES; OF-CHARGE; LEAD-ACID; PROGNOSTICS; VOLTAGE; MODEL;
D O I：
10.1016/j.jpowsour.2013.03.158
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：
State of health (SOH) of batteries in electric and hybrid vehicles can be observed using some battery parameters. Based on a resistance-capacitance circuit model of the battery and data obtained from abundant experiments, it was observed that the diffusion capacitance shows great correlation with SOH of a lithium-ion battery. However, accurate measurement of this diffusion capacitance in real time in an electric or hybrid electric vehicle is not practical. In this paper, Genetic Algorithm (GA) is employed to estimate the battery model parameters including the diffusion capacitance in real time using measurement of current and voltage of the battery. The battery SOH can then be determined using the identified diffusion capacitance. Temperature influence is also considered to improve the robustness and precision of SOH estimation results. Experimental results on various batteries further verified the proposed method. (C) 2013 Elsevier B.V. All rights reserved.
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收藏
页码：184 / 192
页数：9
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