SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm

Ding Xuan Yu; Yan Xia Gao

doi:10.4028/www.scientific.net/AMM.347-350.1852

Paper Titles

An Improved Multistage Variable-Step MPPT Algorithm for Photovoltaic System
p.1833

Fault Analysis of AC Uninterruptible Power Supply System in Gas Pipeline Compressor Station
p.1839

The Design and Implementation of USB Mass Storage and CCID Composite Device
p.1842

Research on the Security Technologies of Power Grid EMS Based on SOA
p.1846

SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm
p.1852

Performance Analysis of Coherent Atmospheric Optical Communications with Soft-Decision Forward Error Correction
p.1856

A Refined Concentric Anchor-Beacons Location Algorithm for Wireless Sensor Networks Based on Received Signal Strength Indicator
p.1860

An Application of LDPC Code for Wireless Coherent-Light Commutation in Atmospheric Channel
p.1864

Study on the Physical Model and Experiments of MIMO Theory
p.1868

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350SOC Estimation of Lithium-Ion Battery Based on...

SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm

Abstract:

This paper presents Extended Kalman-filter (EKF) algorithm which is based on a first-order Lithium-ion batteries model. Curve fitting According to the OCV(open circuit voltage),SOC(state of charge) parameters measured in experiments, descript status equation and observation equation of Lithium-ion battery in detail , so that it can accurately demonstrates the characteristics of the Lithium-ion battery. Simulation and experiment results show the feasibility and effectiveness of the algorithm.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

1852-1855

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.1852

Citation:

Cite this paper

Online since:

August 2013

Authors:

Ding Xuan Yu, Yan Xia Gao

Keywords:

Kalman Filter (KF), Lithium-Ion Battery, SoC

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Gregory L. Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 1. Background, Power Sources, 2004, 134(2): 252～261.

DOI: 10.1016/j.jpowsour.2004.02.031

Google Scholar

[2] Gregory L. Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 2. Modeling and identification, Power Sources, 2004, 134(2): 262～276.

DOI: 10.1016/j.jpowsour.2004.02.032

Google Scholar

[3] Gregory L. Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs, Part 3. State and parameter estimation, Power Sources, 2004, 134(2): 277～292.

DOI: 10.1016/j.jpowsour.2004.02.033

Google Scholar

[4] Mark Verbrugge, Edward Tate. Adaptive state of charge algorithm for nickel metal hybrid batteries including hysteresis phenomena,: Journal of Power Sources, 2004(126): 236-249.

DOI: 10.1016/j.jpowsour.2003.08.042

Google Scholar

[5] Mark Verbrugge, Brian Koch. Generialized Recursive Algorithm for Adaptive Multi parameter, Regression. Journal of Electrochemical SOCiety, 153(1)(2006): A187～A201.

DOI: 10.1149/1.2128096

Google Scholar

[6] Minoru Kitagawa, Hiroshi Shibuya, Jun Takehara. Development of Battery State of Charge Indicator for Electric Vehicles, Proc. of the 11th International Electric Vehicle Symposium(1992).

Google Scholar

[7] T. Shinpo. Develpment of Battery Management System for Electric Vehicle,. Proc. of the 14th International Electric Vehicle Sysposium(1997), 2004(24)(2): 41～4.

Google Scholar