A data-driven fuzzy information granulation approach for battery state of health forecasting

被引：59

作者：

Pan, Wenjie ^{[1
]}

Chen, Qi ^{[1
]}

Zhu, Maotao ^{[1
]}

Tang, Jie ^{[1
]}

Wang, Jianling ^{[1
]}

机构：

[1] Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China

来源：

JOURNAL OF POWER SOURCES | 2020年 / 475卷

基金：

中国国家自然科学基金;

关键词：

Lithium-ion battery; Data-driven; Information granulation; Health indicator; Least squares support vector machine; LITHIUM-ION BATTERIES; MODEL; DEGRADATION; ELECTROLYTE; COMBINATION; PERFORMANCE; PROGNOSTICS; REGRESSION; FRAMEWORK;

D O I：

10.1016/j.jpowsour.2020.228716

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：

This paper proposes an estimation method for battery state of health (SOH) based on fuzzy information granulation. The time interval of the equal charging current difference (TIECCD) in the constant voltage charging mode is extracted as a feature. Then, grey relational analysis is employed to find the optimal health indicators. After granulating fuzzy information on SOH and health indicators, the three parameters Low, R and Up are obtained to characterize the SOH range. In the implementation, the least squares support vector machine (LSSVM) is selected to construct the nonlinear regression model of the parameters and the granulated feature data to realize the prediction of the trend of battery health. Finally, one reference group is set as contrast, and the prediction results based on experimental data prove the superiority of the proposed method.

引用

页数：12

共 45 条

[1] Critical review of state of health estimation methods of Li-ion batteries for real applications [J].

Berecibar, M. ;

Gandiaga, I. ;

Villarreal, I. ;

Omar, N. ;

Van Mierlo, J. ;

Van den Bossche, P. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 56 :572-587

[2] Accurate electrical battery model capable of predicting, runtime and I-V performance [J].

Chen, Min ;

Rincon-Mora, Gabriel A. .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 2006, 21 (02) :504-511

[3] Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications [J].

Chen, Zheng ;

Mi, Chunting Chris ;

Fu, Yuhong ;

Xu, Jun ;

Gong, Xianzhi .

JOURNAL OF POWER SOURCES, 2013, 240 :184-192

[4] SUPPORT-VECTOR NETWORKS [J].

CORTES, C ;

VAPNIK, V .

MACHINE LEARNING, 1995, 20 (03) :273-297

[5]

Dai Haifeng, 2009, 2009 IEEE Vehicle Power and Propulsion Conference (VPPC), P1649, DOI 10.1109/VPPC.2009.5289654

[6] Managing transport properties in composite electrodes/electrolytes for all-solid-state lithium-based batteries [J].

Falco, Marisa ;

Ferrari, Stefania ;

Appetecchi, Giovanni Battista ;

Gerbaldi, Claudio .

MOLECULAR SYSTEMS DESIGN & ENGINEERING, 2019, 4 (04) :850-871

[7] Understanding the Effect of UV-Induced Cross-Linking on the Physicochemical Properties of Highly Performing PEO/LiTFSI-Based Polymer Electrolytes [J].

Falco, Marisa ;

Simari, Cataldo ;

Ferrara, Chiara ;

Nair, Jijeesh Ravi ;

Meligrana, Giuseppina ;

Bella, Federico ;

Nicotera, Isabella ;

Mustarelli, Piercarlo ;

Winter, Martin ;

Gerbaldi, Claudio .

LANGMUIR, 2019, 35 (25) :8210-8219

[8] UV-Cross-Linked Composite Polymer Electrolyte for High-Rate, Ambient Temperature Lithium Batteries [J].

Falco, Marisa ;

Castro, Laurent ;

Nair, Jijeesh Ravi ;

Bella, Federico ;

Barde, Fanny ;

Meligrana, Giuseppina ;

Gerbaldi, Claudio .

ACS APPLIED ENERGY MATERIALS, 2019, 2 (03) :1600-1607

[9] Using probability density function to evaluate the state of health of lithium-ion batteries [J].

Feng, Xuning ;

Li, Jianqiu ;

Ouyang, Minggao ;

Lu, Languang ;

Li, Jianjun ;

He, Xiangming .

JOURNAL OF POWER SOURCES, 2013, 232 :209-218

[10] A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction [J].

Guo, Peiyao ;

Cheng, Ze ;

Yang, Lei .

JOURNAL OF POWER SOURCES, 2019, 412 :442-450

← 1 2 3 4 5 →