An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries

Zhiliang Luo; Yanjie Li; Yunjiang Lou

doi:10.1109/ICInfA.2015.7279474

An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries

Zhiliang Luo
, Yanjie Li
, Yunjiang Lou

Harbin Institute of Technology Shenzhen

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

5 Scopus citations

Abstract

Fast and accurate estimation of battery state of charge (SOC) is a key technology in the battery management system. Based on the non-linear response characteristics of lithium batteries, an adaptive Kalman filter algorithm is put forward in this paper. It is known that the battery model parameters vary with SOC, battery temperature and battery aging. Moreover, the relationship between open circuit voltage (OCV) and SOC is nonlinear. To solve these issues, a piecewise linear approximation of the model parameters is proposed based on the SOC, and then the nonlinear battery model is turned into a piecewise linear one. On these bases, an adaptive Kalman filter can be implemented and thus the amount of computation can be reduced. In addition, we apply the Arrhenius equation to update internal resistance and the remaining capacity of battery which can reflect the aging state of battery. The algorithm achieves an adaptive SOC estimation and improves the estimation accuracy with a small amount of calculation. Finally, the simulation results show the accuracy and applicability of the algorithm.

Original language	English
Title of host publication	2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1227-1232
Number of pages	6
ISBN (Electronic)	9781467391047
DOIs	https://doi.org/10.1109/ICInfA.2015.7279474
State	Published - 28 Sep 2015
Externally published	Yes
Event	2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics - Yunnan, China Duration: 8 Aug 2015 → 10 Aug 2015

Publication series

Name	2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics

Conference

Conference	2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
Country/Territory	China
City	Yunnan
Period	8/08/15 → 10/08/15

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Arrhennius equation
adaptive Kalman filter
linear piecewise
state of charge

Access to Document

10.1109/ICInfA.2015.7279474

Cite this

Luo, Z., Li, Y., & Lou, Y. (2015). An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries. In 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics (pp. 1227-1232). Article 7279474 (2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICInfA.2015.7279474

Luo, Zhiliang ; Li, Yanjie ; Lou, Yunjiang. / An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries. 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1227-1232 (2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics).

@inproceedings{413a48093b234a5b942564f076b8825c,

title = "An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries",

abstract = "Fast and accurate estimation of battery state of charge (SOC) is a key technology in the battery management system. Based on the non-linear response characteristics of lithium batteries, an adaptive Kalman filter algorithm is put forward in this paper. It is known that the battery model parameters vary with SOC, battery temperature and battery aging. Moreover, the relationship between open circuit voltage (OCV) and SOC is nonlinear. To solve these issues, a piecewise linear approximation of the model parameters is proposed based on the SOC, and then the nonlinear battery model is turned into a piecewise linear one. On these bases, an adaptive Kalman filter can be implemented and thus the amount of computation can be reduced. In addition, we apply the Arrhenius equation to update internal resistance and the remaining capacity of battery which can reflect the aging state of battery. The algorithm achieves an adaptive SOC estimation and improves the estimation accuracy with a small amount of calculation. Finally, the simulation results show the accuracy and applicability of the algorithm.",

keywords = "Arrhennius equation, adaptive Kalman filter, linear piecewise, state of charge",

author = "Zhiliang Luo and Yanjie Li and Yunjiang Lou",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics ; Conference date: 08-08-2015 Through 10-08-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/ICInfA.2015.7279474",

language = "英语",

series = "2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics",

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

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}

Luo, Z, Li, Y & Lou, Y 2015, An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries. in 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics., 7279474, 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics, Institute of Electrical and Electronics Engineers Inc., pp. 1227-1232, 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics, Yunnan, China, 8/08/15. https://doi.org/10.1109/ICInfA.2015.7279474

An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries. / Luo, Zhiliang; Li, Yanjie ; Lou, Yunjiang.
2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1227-1232 7279474 (2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics).

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

TY - GEN

T1 - An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries

AU - Luo, Zhiliang

AU - Li, Yanjie

AU - Lou, Yunjiang

PY - 2015/9/28

Y1 - 2015/9/28

N2 - Fast and accurate estimation of battery state of charge (SOC) is a key technology in the battery management system. Based on the non-linear response characteristics of lithium batteries, an adaptive Kalman filter algorithm is put forward in this paper. It is known that the battery model parameters vary with SOC, battery temperature and battery aging. Moreover, the relationship between open circuit voltage (OCV) and SOC is nonlinear. To solve these issues, a piecewise linear approximation of the model parameters is proposed based on the SOC, and then the nonlinear battery model is turned into a piecewise linear one. On these bases, an adaptive Kalman filter can be implemented and thus the amount of computation can be reduced. In addition, we apply the Arrhenius equation to update internal resistance and the remaining capacity of battery which can reflect the aging state of battery. The algorithm achieves an adaptive SOC estimation and improves the estimation accuracy with a small amount of calculation. Finally, the simulation results show the accuracy and applicability of the algorithm.

AB - Fast and accurate estimation of battery state of charge (SOC) is a key technology in the battery management system. Based on the non-linear response characteristics of lithium batteries, an adaptive Kalman filter algorithm is put forward in this paper. It is known that the battery model parameters vary with SOC, battery temperature and battery aging. Moreover, the relationship between open circuit voltage (OCV) and SOC is nonlinear. To solve these issues, a piecewise linear approximation of the model parameters is proposed based on the SOC, and then the nonlinear battery model is turned into a piecewise linear one. On these bases, an adaptive Kalman filter can be implemented and thus the amount of computation can be reduced. In addition, we apply the Arrhenius equation to update internal resistance and the remaining capacity of battery which can reflect the aging state of battery. The algorithm achieves an adaptive SOC estimation and improves the estimation accuracy with a small amount of calculation. Finally, the simulation results show the accuracy and applicability of the algorithm.

KW - Arrhennius equation

KW - adaptive Kalman filter

KW - linear piecewise

KW - state of charge

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DO - 10.1109/ICInfA.2015.7279474

M3 - 会议稿件

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T3 - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics

SP - 1227

EP - 1232

BT - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics

Y2 - 8 August 2015 through 10 August 2015

ER -

Luo Z, Li Y , Lou Y. An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries. In 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1227-1232. 7279474. (2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics). doi: 10.1109/ICInfA.2015.7279474

An adaptive kalman filter to estimate state-of-charge of lithium-ion batteries

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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