Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

Xiaosong Hu; Fengchun Sun; Yuan Zou; Huei Peng

doi:10.1109/ACC.2011.5991260

Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

Xiaosong Hu^*
, Fengchun Sun
, Yuan Zou
, Huei Peng

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology
University of Michigan, Ann Arbor

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

109 Citations (Scopus)

Abstract

A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.

Original language	English
Title of host publication	Proceedings of the 2011 American Control Conference, ACC 2011
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	935-940
Number of pages	6
ISBN (Print)	9781457700804
DOIs	https://doi.org/10.1109/ACC.2011.5991260
Publication status	Published - 2011

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

UN SDGs

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

SDG 7 Affordable and Clean Energy

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10.1109/ACC.2011.5991260

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Hu, X., Sun, F., Zou, Y., & Peng, H. (2011). Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 935-940). Article 5991260 (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2011.5991260

@inproceedings{c378055921734dd69b3531feaa5cf70d,

title = "Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting",

abstract = "A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.",

author = "Xiaosong Hu and Fengchun Sun and Yuan Zou and Huei Peng",

year = "2011",

doi = "10.1109/ACC.2011.5991260",

language = "English",

isbn = "9781457700804",

series = "Proceedings of the American Control Conference",

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

pages = "935--940",

booktitle = "Proceedings of the 2011 American Control Conference, ACC 2011",

address = "United States",

}

Hu, X, Sun, F , Zou, Y & Peng, H 2011, Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. in Proceedings of the 2011 American Control Conference, ACC 2011., 5991260, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 935-940. https://doi.org/10.1109/ACC.2011.5991260

Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. / Hu, Xiaosong; Sun, Fengchun ; Zou, Yuan et al.
Proceedings of the 2011 American Control Conference, ACC 2011. Institute of Electrical and Electronics Engineers Inc., 2011. p. 935-940 5991260 (Proceedings of the American Control Conference).

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

TY - GEN

T1 - Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

AU - Hu, Xiaosong

AU - Sun, Fengchun

AU - Zou, Yuan

AU - Peng, Huei

PY - 2011

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N2 - A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.

AB - A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.

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M3 - Conference contribution

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SN - 9781457700804

T3 - Proceedings of the American Control Conference

SP - 935

EP - 940

BT - Proceedings of the 2011 American Control Conference, ACC 2011

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

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