Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications

Anthony Frierson; Bang Hung Tsao; Nicholas Zumberge; Tim Farr; Joseph Fellner; Luis Herrera; Gregory A. Horrocks

doi:10.1109/NAECON58068.2023.10365963

Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications

Anthony Frierson
, Bang Hung Tsao
, Nicholas Zumberge
, Tim Farr
, Joseph Fellner
, Luis Herrera
, Gregory A. Horrocks

Department of Electrical Engineering

University of Dayton
Air Force Research Laboratory
Wright-Patterson AFB

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

An adaptive version of the Kalman Filter (KF) algorithm is investigated as a potential for State of Charge (SOC) estimation in Li-Ion cells. SoC estimation results of the adaptive KF via Battery Management System (BMS) model simulations in MATLAB & Simulink are discussed. The minimal SoC estimation error obtained in the results indicate strong KF adaptability to the complex non-linear cell behavior that typically occurs in active operation, especially in comparison with the Coulomb Counting (CC) current integration, and non-adaptive KF estimation methods.

Original language	English
Title of host publication	NAECON 2023 - IEEE National Aerospace and Electronics Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5-9
Number of pages	5
ISBN (Electronic)	9798350338782
DOIs	https://doi.org/10.1109/NAECON58068.2023.10365963
State	Published - 2023
Event	2023 IEEE National Aerospace and Electronics Conference, NAECON 2023 - Dayton, United States Duration: Aug 28 2023 → Aug 31 2023

Publication series

Name	Proceedings of the IEEE National Aerospace Electronics Conference, NAECON
ISSN (Print)	0547-3578
ISSN (Electronic)	2379-2027

Conference

Conference	2023 IEEE National Aerospace and Electronics Conference, NAECON 2023
Country/Territory	United States
City	Dayton
Period	08/28/23 → 08/31/23

Keywords

Adaptive Kalman Filter
Battery Management System
Lithium-Ion
State-of-Charge

Access to Document

10.1109/NAECON58068.2023.10365963

Cite this

Frierson, A., Tsao, B. H., Zumberge, N., Farr, T., Fellner, J., Herrera, L., & Horrocks, G. A. (2023). Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications. In NAECON 2023 - IEEE National Aerospace and Electronics Conference (pp. 5-9). (Proceedings of the IEEE National Aerospace Electronics Conference, NAECON). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAECON58068.2023.10365963

@inproceedings{4a83f341c0ee48c6b9a0052cffe94103,

title = "Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications",

abstract = "An adaptive version of the Kalman Filter (KF) algorithm is investigated as a potential for State of Charge (SOC) estimation in Li-Ion cells. SoC estimation results of the adaptive KF via Battery Management System (BMS) model simulations in MATLAB \& Simulink are discussed. The minimal SoC estimation error obtained in the results indicate strong KF adaptability to the complex non-linear cell behavior that typically occurs in active operation, especially in comparison with the Coulomb Counting (CC) current integration, and non-adaptive KF estimation methods.",

keywords = "Adaptive Kalman Filter, Battery Management System, Lithium-Ion, State-of-Charge",

author = "Anthony Frierson and Tsao, \{Bang Hung\} and Nicholas Zumberge and Tim Farr and Joseph Fellner and Luis Herrera and Horrocks, \{Gregory A.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE National Aerospace and Electronics Conference, NAECON 2023 ; Conference date: 28-08-2023 Through 31-08-2023",

year = "2023",

doi = "10.1109/NAECON58068.2023.10365963",

language = "English",

series = "Proceedings of the IEEE National Aerospace Electronics Conference, NAECON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5--9",

booktitle = "NAECON 2023 - IEEE National Aerospace and Electronics Conference",

address = "United States",

}

Frierson, A, Tsao, BH, Zumberge, N, Farr, T, Fellner, J, Herrera, L & Horrocks, GA 2023, Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications. in NAECON 2023 - IEEE National Aerospace and Electronics Conference. Proceedings of the IEEE National Aerospace Electronics Conference, NAECON, Institute of Electrical and Electronics Engineers Inc., pp. 5-9, 2023 IEEE National Aerospace and Electronics Conference, NAECON 2023, Dayton, United States, 08/28/23. https://doi.org/10.1109/NAECON58068.2023.10365963

Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications. / Frierson, Anthony; Tsao, Bang Hung; Zumberge, Nicholas et al.
NAECON 2023 - IEEE National Aerospace and Electronics Conference. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5-9 (Proceedings of the IEEE National Aerospace Electronics Conference, NAECON).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications

AU - Frierson, Anthony

AU - Tsao, Bang Hung

AU - Zumberge, Nicholas

AU - Farr, Tim

AU - Fellner, Joseph

AU - Herrera, Luis

AU - Horrocks, Gregory A.

PY - 2023

Y1 - 2023

N2 - An adaptive version of the Kalman Filter (KF) algorithm is investigated as a potential for State of Charge (SOC) estimation in Li-Ion cells. SoC estimation results of the adaptive KF via Battery Management System (BMS) model simulations in MATLAB & Simulink are discussed. The minimal SoC estimation error obtained in the results indicate strong KF adaptability to the complex non-linear cell behavior that typically occurs in active operation, especially in comparison with the Coulomb Counting (CC) current integration, and non-adaptive KF estimation methods.

AB - An adaptive version of the Kalman Filter (KF) algorithm is investigated as a potential for State of Charge (SOC) estimation in Li-Ion cells. SoC estimation results of the adaptive KF via Battery Management System (BMS) model simulations in MATLAB & Simulink are discussed. The minimal SoC estimation error obtained in the results indicate strong KF adaptability to the complex non-linear cell behavior that typically occurs in active operation, especially in comparison with the Coulomb Counting (CC) current integration, and non-adaptive KF estimation methods.

KW - Adaptive Kalman Filter

KW - Battery Management System

KW - Lithium-Ion

KW - State-of-Charge

UR - https://www.scopus.com/pages/publications/85182400453

U2 - 10.1109/NAECON58068.2023.10365963

DO - 10.1109/NAECON58068.2023.10365963

M3 - Conference contribution

AN - SCOPUS:85182400453

T3 - Proceedings of the IEEE National Aerospace Electronics Conference, NAECON

SP - 5

EP - 9

BT - NAECON 2023 - IEEE National Aerospace and Electronics Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

Y2 - 28 August 2023 through 31 August 2023

ER -

Frierson A, Tsao BH, Zumberge N, Farr T, Fellner J, Herrera L et al. Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications. In NAECON 2023 - IEEE National Aerospace and Electronics Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 5-9. (Proceedings of the IEEE National Aerospace Electronics Conference, NAECON). doi: 10.1109/NAECON58068.2023.10365963

Adaptive Method for Li-Ion Cell State-of-Charge Estimation in Smart Aircraft Applications

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this