State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model

Giambattista Gruosso; Giancarlo Storti Gajani; Juan D. Valladolid; DIego Patino; Freddy Ruiz

doi:10.1109/VPPC46532.2019.8952458

State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model

Giambattista Gruosso, Giancarlo Storti Gajani, Juan D. Valladolid, DIego Patino, Freddy Ruiz

Departamento de Ingeniería Electrónica

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

7 Citas (Scopus)

Resumen

This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.

Idioma original	Inglés
Título de la publicación alojada	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728112497
DOI	https://doi.org/10.1109/VPPC46532.2019.8952458
Estado	Publicada - oct. 2019
Evento	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Hanoi, Vietnam Duración: 14 oct. 2019 → 17 oct. 2019

Serie de la publicación

Nombre	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

Conferencia

Conferencia	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019
País/Territorio	Vietnam
Ciudad	Hanoi
Período	14/10/19 → 17/10/19

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10.1109/VPPC46532.2019.8952458

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Gruosso, G., Gajani, G. S., Valladolid, J. D., Patino, DI., & Ruiz, F. (2019). State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings Artículo 8952458 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC46532.2019.8952458

Gruosso, Giambattista ; Gajani, Giancarlo Storti ; Valladolid, Juan D. et al. / State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model. 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

@inproceedings{f8d9682ab7a3495296b2d56d83676875,

title = "State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model",

abstract = "This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.",

keywords = "Electric Vehicle (EV), LiFePO4 Battery, Modeling, Principal Component Analysis (PCA), State of Charge (SoC), Support Vector Regression (SVR)",

author = "Giambattista Gruosso and Gajani, {Giancarlo Storti} and Valladolid, {Juan D.} and DIego Patino and Freddy Ruiz",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 ; Conference date: 14-10-2019 Through 17-10-2019",

year = "2019",

month = oct,

doi = "10.1109/VPPC46532.2019.8952458",

language = "English",

series = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",

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

booktitle = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",

address = "United States",

}

Gruosso, G, Gajani, GS, Valladolid, JD, Patino, DI & Ruiz, F 2019, State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings., 8952458, 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019, Hanoi, Vietnam, 14/10/19. https://doi.org/10.1109/VPPC46532.2019.8952458

State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model. / Gruosso, Giambattista; Gajani, Giancarlo Storti; Valladolid, Juan D. et al.
2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8952458 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model

AU - Gruosso, Giambattista

AU - Gajani, Giancarlo Storti

AU - Valladolid, Juan D.

AU - Patino, DIego

AU - Ruiz, Freddy

PY - 2019/10

Y1 - 2019/10

N2 - This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.

AB - This paper presents a new methodology for Stateof- Charge (SoC) estimation based on Support Vector Regression (SVR), Principal component Analysis (PCA) and the Dual Polarization (DP) battery model. The proposed methodology considers several factors that are significant for the prediction of battery current in Electric Vehicles (EVs) such as: speed, acceleration, voltage, pedal position; this information is then used as input to the DP battery model. Battery parameters were estimated using the Nonlinear Least Square (NLS) algorithm.

KW - Electric Vehicle (EV)

KW - LiFePO4 Battery

KW - Modeling

KW - Principal Component Analysis (PCA)

KW - State of Charge (SoC)

KW - Support Vector Regression (SVR)

UR - http://www.scopus.com/inward/record.url?scp=85078743151&partnerID=8YFLogxK

U2 - 10.1109/VPPC46532.2019.8952458

DO - 10.1109/VPPC46532.2019.8952458

M3 - Conference contribution

AN - SCOPUS:85078743151

T3 - 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

BT - 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019

Y2 - 14 October 2019 through 17 October 2019

ER -

Gruosso G, Gajani GS, Valladolid JD, Patino DI, Ruiz F. State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8952458. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). doi: 10.1109/VPPC46532.2019.8952458

State of charge estimation of lifepo4 battery used in electric vehicles using support vector regression, PCA and DP battery model

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