Online Battery State-of-Charge Estimation Based on Sparse Gaussian Process Regression

被引：0

作者：

Ozcan, Gozde ^{[1
]}

Pajovic, Milutin ^{[2
]}

Sahinoglu, Zafer ^{[2
]}

Wang, Yebin ^{[2
]}

Orlik, Philip V. ^{[2
]}

Wada, Toshihiro ^{[3
]}

机构：

[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY 13244 USA

[2] Mitsubishi Elect Res Labs, 201 Broadway, Cambridge, MA 02139 USA

[3] Mitsubishi Electr Corp, Adv Technol R&D Ctr, 8-1-1 Tsukaguchi Honmachi, Amagasaki, Hyogo 6618661, Japan

来源：

2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM) | 2016年

关键词：

Battery management system; Lithium-ion battery; sparse Gaussian process regression; state of charge estimation; ION BATTERIES; FILTER;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents a new online method for state-of charge (SoC) estimation of Lithium-ion (Li-ion) batteries based on sparse Gaussian process regression (GPR). Building upon sparse approximation of the regular GPR, the proposed method is computationally more efficient. The battery SoC is estimated based on measured voltage, current and temperature. The accuracy of the proposed method is verified using LiMn2O4/hard-carbon battery data collected from a constant-current discharge test. In addition, the estimation performance of the proposed method is compared with a SoC estimation method using regular GPR with different covariance functions.

引用

页数：5

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