Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery

Zhongbao Wei; Hongwen He; Jian Hu

doi:10.1109/ECCE44975.2020.9235630

Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery

School of Mechanical Engineering

Beijing Institute of Technology

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

1 Citation (Scopus)

Abstract

The accurate estimation of state of energy (SOE) is critical for the optimized utilization of lithium-ion battery (LIB). Despite the wide use of model-based observers, their performance can be declined largely by the noise corruption in real applications. This paper focuses on noise effect compensation and SOE estimation for LIB. A method combining the bias compensating recursive least squares (BCRLS) and Frisch scheme is exploited to compensate the noise effect and eliminate the identification bias. The unbiased model identification is further integrated with an observer based on the unscented Kalman Filter (UKF) to estimate the SOE in real time. Simulation and experimental results suggest that the proposed method effectively attenuates the identification bias caused by noise corruption and provides more reliable SOE estimation. Comparison with existing methods are also performed to verify its superiority in terms of the accuracy and the robustness to noises.

Original language	English
Title of host publication	ECCE 2020 - IEEE Energy Conversion Congress and Exposition
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5595-5599
Number of pages	5
ISBN (Electronic)	9781728158266
DOIs	https://doi.org/10.1109/ECCE44975.2020.9235630
Publication status	Published - 11 Oct 2020
Event	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States Duration: 11 Oct 2020 → 15 Oct 2020

Publication series

Name	ECCE 2020 - IEEE Energy Conversion Congress and Exposition

Conference

Conference	12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Country/Territory	United States
City	Virtual, Detroit
Period	11/10/20 → 15/10/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

bias compensation
lithium-ion battery
model parameter identification
state of energy

Access to Document

10.1109/ECCE44975.2020.9235630

Cite this

Wei, Z., He, H., & Hu, J. (2020). Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery. In ECCE 2020 - IEEE Energy Conversion Congress and Exposition (pp. 5595-5599). Article 9235630 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE44975.2020.9235630

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title = "Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery",

abstract = "The accurate estimation of state of energy (SOE) is critical for the optimized utilization of lithium-ion battery (LIB). Despite the wide use of model-based observers, their performance can be declined largely by the noise corruption in real applications. This paper focuses on noise effect compensation and SOE estimation for LIB. A method combining the bias compensating recursive least squares (BCRLS) and Frisch scheme is exploited to compensate the noise effect and eliminate the identification bias. The unbiased model identification is further integrated with an observer based on the unscented Kalman Filter (UKF) to estimate the SOE in real time. Simulation and experimental results suggest that the proposed method effectively attenuates the identification bias caused by noise corruption and provides more reliable SOE estimation. Comparison with existing methods are also performed to verify its superiority in terms of the accuracy and the robustness to noises.",

keywords = "bias compensation, lithium-ion battery, model parameter identification, state of energy",

author = "Zhongbao Wei and Hongwen He and Jian Hu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 ; Conference date: 11-10-2020 Through 15-10-2020",

year = "2020",

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Wei, Z , He, H & Hu, J 2020, Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery. in ECCE 2020 - IEEE Energy Conversion Congress and Exposition., 9235630, ECCE 2020 - IEEE Energy Conversion Congress and Exposition, Institute of Electrical and Electronics Engineers Inc., pp. 5595-5599, 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020, Virtual, Detroit, United States, 11/10/20. https://doi.org/10.1109/ECCE44975.2020.9235630

Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery. / Wei, Zhongbao ; He, Hongwen; Hu, Jian.
ECCE 2020 - IEEE Energy Conversion Congress and Exposition. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5595-5599 9235630 (ECCE 2020 - IEEE Energy Conversion Congress and Exposition).

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

TY - GEN

T1 - Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery

AU - Wei, Zhongbao

AU - He, Hongwen

AU - Hu, Jian

PY - 2020/10/11

Y1 - 2020/10/11

N2 - The accurate estimation of state of energy (SOE) is critical for the optimized utilization of lithium-ion battery (LIB). Despite the wide use of model-based observers, their performance can be declined largely by the noise corruption in real applications. This paper focuses on noise effect compensation and SOE estimation for LIB. A method combining the bias compensating recursive least squares (BCRLS) and Frisch scheme is exploited to compensate the noise effect and eliminate the identification bias. The unbiased model identification is further integrated with an observer based on the unscented Kalman Filter (UKF) to estimate the SOE in real time. Simulation and experimental results suggest that the proposed method effectively attenuates the identification bias caused by noise corruption and provides more reliable SOE estimation. Comparison with existing methods are also performed to verify its superiority in terms of the accuracy and the robustness to noises.

AB - The accurate estimation of state of energy (SOE) is critical for the optimized utilization of lithium-ion battery (LIB). Despite the wide use of model-based observers, their performance can be declined largely by the noise corruption in real applications. This paper focuses on noise effect compensation and SOE estimation for LIB. A method combining the bias compensating recursive least squares (BCRLS) and Frisch scheme is exploited to compensate the noise effect and eliminate the identification bias. The unbiased model identification is further integrated with an observer based on the unscented Kalman Filter (UKF) to estimate the SOE in real time. Simulation and experimental results suggest that the proposed method effectively attenuates the identification bias caused by noise corruption and provides more reliable SOE estimation. Comparison with existing methods are also performed to verify its superiority in terms of the accuracy and the robustness to noises.

KW - bias compensation

KW - lithium-ion battery

KW - model parameter identification

KW - state of energy

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DO - 10.1109/ECCE44975.2020.9235630

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BT - ECCE 2020 - IEEE Energy Conversion Congress and Exposition

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Unbiased Model Identification and State of Energy Estimation of Lithium-Ion Battery

Abstract

Publication series

Conference

UN SDGs

Keywords

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