State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles

Ege Anil Bostan; Volkan Sezer

doi:10.23919/ELECO47770.2019.8990474

State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles

Ege Anil Bostan
, Volkan Sezer

Department of Control and Automation Engineering

Istanbul Technical University

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

Abstract

In recent years, the electric vehicles (EVs) earned reputation due to increasing global warming potential and depletion of fossil fuels. Although EVs became popular, they have still some unsolved challenges such as their range issue. In order to solve their range issue their battery management system (BMS), functioning lithium-ion battery should estimate the state of charge (SOC) correctly to provide safe operation. In this paper, several SOC estimation approaches have been investigated to get accurate SOC. They have been presented and analyzed in terms of the estimation accuracy. For model- based approaches including Extended Kalman Filter (EKF) battery equivalent circuit model has been developed to simulate the dynamic behavior of 51 Ah NMC lithium-ion battery cell. The real world dynamometer measurement data (HPPC WLTP, etc.) have been used to identify and validate the model parameters. According to the results, EKF based estimation provides the best performance comparing to the other approaches.

Original language	English
Title of host publication	ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	34-38
Number of pages	5
ISBN (Electronic)	9786050112757
DOIs	https://doi.org/10.23919/ELECO47770.2019.8990474
Publication status	Published - Nov 2019
Event	11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey Duration: 28 Nov 2019 → 30 Nov 2019

Publication series

Name	ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

Conference

Conference	11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Country/Territory	Turkey
City	Bursa
Period	28/11/19 → 30/11/19

Bibliographical note

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 13 Climate Action

Access to Document

10.23919/ELECO47770.2019.8990474

Cite this

Bostan, E. A., & Sezer, V. (2019). State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles. In ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering (pp. 34-38). Article 8990474 (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELECO47770.2019.8990474

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title = "State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles",

abstract = "In recent years, the electric vehicles (EVs) earned reputation due to increasing global warming potential and depletion of fossil fuels. Although EVs became popular, they have still some unsolved challenges such as their range issue. In order to solve their range issue their battery management system (BMS), functioning lithium-ion battery should estimate the state of charge (SOC) correctly to provide safe operation. In this paper, several SOC estimation approaches have been investigated to get accurate SOC. They have been presented and analyzed in terms of the estimation accuracy. For model- based approaches including Extended Kalman Filter (EKF) battery equivalent circuit model has been developed to simulate the dynamic behavior of 51 Ah NMC lithium-ion battery cell. The real world dynamometer measurement data (HPPC WLTP, etc.) have been used to identify and validate the model parameters. According to the results, EKF based estimation provides the best performance comparing to the other approaches.",

author = "Bostan, \{Ege Anil\} and Volkan Sezer",

note = "Publisher Copyright: {\textcopyright} 2019 Chamber of Turkish Electrical Engineers.; 11th International Conference on Electrical and Electronics Engineering, ELECO 2019 ; Conference date: 28-11-2019 Through 30-11-2019",

year = "2019",

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doi = "10.23919/ELECO47770.2019.8990474",

language = "English",

series = "ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering",

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

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Bostan, EA & Sezer, V 2019, State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles. in ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering., 8990474, ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 34-38, 11th International Conference on Electrical and Electronics Engineering, ELECO 2019, Bursa, Turkey, 28/11/19. https://doi.org/10.23919/ELECO47770.2019.8990474

State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles. / Bostan, Ege Anil; Sezer, Volkan.
ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. p. 34-38 8990474 (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering).

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

TY - GEN

T1 - State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles

AU - Bostan, Ege Anil

AU - Sezer, Volkan

PY - 2019/11

Y1 - 2019/11

N2 - In recent years, the electric vehicles (EVs) earned reputation due to increasing global warming potential and depletion of fossil fuels. Although EVs became popular, they have still some unsolved challenges such as their range issue. In order to solve their range issue their battery management system (BMS), functioning lithium-ion battery should estimate the state of charge (SOC) correctly to provide safe operation. In this paper, several SOC estimation approaches have been investigated to get accurate SOC. They have been presented and analyzed in terms of the estimation accuracy. For model- based approaches including Extended Kalman Filter (EKF) battery equivalent circuit model has been developed to simulate the dynamic behavior of 51 Ah NMC lithium-ion battery cell. The real world dynamometer measurement data (HPPC WLTP, etc.) have been used to identify and validate the model parameters. According to the results, EKF based estimation provides the best performance comparing to the other approaches.

AB - In recent years, the electric vehicles (EVs) earned reputation due to increasing global warming potential and depletion of fossil fuels. Although EVs became popular, they have still some unsolved challenges such as their range issue. In order to solve their range issue their battery management system (BMS), functioning lithium-ion battery should estimate the state of charge (SOC) correctly to provide safe operation. In this paper, several SOC estimation approaches have been investigated to get accurate SOC. They have been presented and analyzed in terms of the estimation accuracy. For model- based approaches including Extended Kalman Filter (EKF) battery equivalent circuit model has been developed to simulate the dynamic behavior of 51 Ah NMC lithium-ion battery cell. The real world dynamometer measurement data (HPPC WLTP, etc.) have been used to identify and validate the model parameters. According to the results, EKF based estimation provides the best performance comparing to the other approaches.

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U2 - 10.23919/ELECO47770.2019.8990474

DO - 10.23919/ELECO47770.2019.8990474

M3 - Conference contribution

AN - SCOPUS:85080922182

T3 - ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

SP - 34

EP - 38

BT - ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Conference on Electrical and Electronics Engineering, ELECO 2019

Y2 - 28 November 2019 through 30 November 2019

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Bostan EA, Sezer V. State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles. In ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering. Institute of Electrical and Electronics Engineers Inc. 2019. p. 34-38. 8990474. (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering). doi: 10.23919/ELECO47770.2019.8990474

State of Charge Estimation for Lithium-Ion Batteries in Electric and Hybrid Vehicles

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

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