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

共 50 条

[21] Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL
Wang, Lu
Ma, Jian
Zhao, Xuan
Li, Xuebo
Zhang, Kai
JOURNAL OF ENERGY STORAGE, 2023, 67
[22] The Lithium-ion Battery State-of-Charge Estimation using Random Forest Regression
Li, Chuanjiang
Chen, Zewang
Cui, Jiang
Wang, Youren
Zou, Feng
PROCEEDINGS OF 2014 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-2014 HUNAN), 2014, : 336 - 339
[23] A new neural network model for the state-of-charge estimation in the battery degradation process
Kang, LiuWang
Zhao, Xuan
Ma, Jian
APPLIED ENERGY, 2014, 121 : 20 - 27
[24] Battery State-of-Charge and Parameter Estimation Algorithm Based on Kalman Filter
Dragicevic, Tomislav
Sucic, Stjepan
Guerrero, Josep M.
2013 IEEE EUROCON, 2013, : 1513 - 1518
[25] On-line battery state-of-charge estimation based on an integrated estimator
Wang, Yujie
Zhang, Chenbin
Chen, Zonghai
APPLIED ENERGY, 2017, 185 : 2026 - 2032
[26] Recursive estimation for sparse Gaussian process regression
Schuerch, Manuel
Azzimonti, Dario
Benavoli, Alessio
Zaffalon, Marco
AUTOMATICA, 2020, 120
[27] Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation
Li, Weihan
Rentemeister, Monika
Badeda, Julia
Jost, Dominik
Schulte, Dominik
Sauer, Dirk Uwe
JOURNAL OF ENERGY STORAGE, 2020, 30
[28] High Accuracy State-of-Charge Estimation of Li-Ion Battery Pack based on Screening Process
Kim, Jonghoon
Shin, Jongwon
Jeon, Changyoon
Cho, Bohyung
2011 TWENTY-SIXTH ANNUAL IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC), 2011, : 1984 - 1991
[29] Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-order RC Model
Cheng, Ze
Zhang, Qiu-yan
Zhang, Yu-hui
ENERGY AND POWER TECHNOLOGY, PTS 1 AND 2, 2013, 805-806 : 1659 - 1663
[30] Online Sparse Gaussian Process Regression for Trajectory Modeling
Tiger, Mattias
Heintz, Fredrik
2015 18TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2015, : 782 - 791

← 1 2 3 4 5 →