Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries

Quanqing Yu; Rui Xiong; Cheng Lin

doi:10.1016/j.egypro.2017.03.600

Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries

Quanqing Yu
, Rui Xiong^*
, Cheng Lin

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

56 Scopus citations

Abstract

The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures.

Original language	English
Pages (from-to)	2791-2796
Number of pages	6
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.600
State	Published - 2017
Externally published	Yes
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

H infinity filter
lithium-ion battery
state of charge
unscented Kalman filter

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10.1016/j.egypro.2017.03.600

Cite this

@article{9c22cf13453d43e4ab1ace1e2b3bd9f1,

title = "Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries",

abstract = "The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures.",

keywords = "H infinity filter, lithium-ion battery, state of charge, unscented Kalman filter",

author = "Quanqing Yu and Rui Xiong and Cheng Lin",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.600",

language = "英语",

volume = "105",

pages = "2791--2796",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries

AU - Yu, Quanqing

AU - Xiong, Rui

AU - Lin, Cheng

PY - 2017

Y1 - 2017

N2 - The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures.

AB - The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures.

KW - H infinity filter

KW - lithium-ion battery

KW - state of charge

KW - unscented Kalman filter

UR - https://www.scopus.com/pages/publications/85020692618

U2 - 10.1016/j.egypro.2017.03.600

DO - 10.1016/j.egypro.2017.03.600

M3 - 会议文章

AN - SCOPUS:85020692618

SN - 1876-6102

VL - 105

SP - 2791

EP - 2796

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries

Abstract

UN SDGs

Keywords

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