Improved Realtime State-of-Charge Estimation of LiFePO 4 Battery Based on a Novel Thermoelectric Model

C. Zhang; Kang Li; Jing Deng; S. Song

doi:10.1109/TIE.2016.2610398

Improved Realtime State-of-Charge Estimation of LiFePO 4 Battery Based on a Novel Thermoelectric Model

C. Zhang, Kang Li, Jing Deng, S. Song

Research output: Contribution to journal › Article › peer-review

100 Citations (Scopus)

74 Downloads (Pure)

Abstract

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO 4 batteries confirm the efficacy of the proposed method.

Original language	English
Pages (from-to)	654-663
Number of pages	10
Journal	IEEE Transactions on Industrial Electronics
Volume	64
Issue number	1
Early online date	15 Sept 2016
DOIs	https://doi.org/10.1109/TIE.2016.2610398
Publication status	Published - Jan 2017
Externally published	Yes

Bibliographical note

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

battery management systems
cooling
electric vehicles
iron compounds
Kalman filters
least squares approximations
lithium compounds
minimisation
nonlinear filters
secondary cells
LiFePO4 battery realtime state-of-charge estimation
Li-ion battery
electric vehicle
battery internal state estimation
battery management system
battery internal temperature estimation
realtime SOC estimation
battery thermoelectric model
thermal submodel
electrical submodel
battery thermal behavior
battery electrical behavior
simulation error minimization method
hybrid optimization method
metaheuristic algorithm
least-square approach
time-varying model parameter
heat dissipation condition
joint extended Kalman filter
LiFePO4
Batteries
State of charge
Estimation
Temperature measurement
Temperature
Heating
Integrated circuit modeling
Internal temperature estimation
joint extended Kalman filter (EKF)
state-of-charge (SOC) estimation
thermoelectric model

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10.1109/TIE.2016.2610398

Post-PrintAccepted author manuscript, 1.26 MBLicence: Unspecified

Cheng Zhang
- Centre for E-Mobility and Clean Growth - Associate Professor
Person: Teaching and Research

Cite this

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title = "Improved Realtime State-of-Charge Estimation of LiFePO 4 Battery Based on a Novel Thermoelectric Model",

abstract = "Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO 4 batteries confirm the efficacy of the proposed method.",

keywords = "battery management systems, cooling, electric vehicles, iron compounds, Kalman filters, least squares approximations, lithium compounds, minimisation, nonlinear filters, secondary cells, LiFePO4 battery realtime state-of-charge estimation, Li-ion battery, electric vehicle, battery internal state estimation, battery management system, battery internal temperature estimation, realtime SOC estimation, battery thermoelectric model, thermal submodel, electrical submodel, battery thermal behavior, battery electrical behavior, simulation error minimization method, hybrid optimization method, metaheuristic algorithm, least-square approach, time-varying model parameter, heat dissipation condition, joint extended Kalman filter, LiFePO4, Batteries, State of charge, Estimation, Temperature measurement, Temperature, Heating, Integrated circuit modeling, Internal temperature estimation, joint extended Kalman filter (EKF), state-of-charge (SOC) estimation, thermoelectric model",

author = "C. Zhang and Kang Li and Jing Deng and S. Song",

note = "{\textcopyright} 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright {\textcopyright} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. ",

year = "2017",

month = jan,

doi = "10.1109/TIE.2016.2610398",

language = "English",

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journal = "IEEE Transactions on Industrial Electronics",

issn = "1557-9948",

publisher = "Institute of Electrical and Electronics Engineers",

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AU - Zhang, C.

AU - Li, Kang

AU - Deng, Jing

AU - Song, S.

N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

PY - 2017/1

Y1 - 2017/1

N2 - Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO 4 batteries confirm the efficacy of the proposed method.

AB - Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO 4 batteries confirm the efficacy of the proposed method.

KW - battery management systems

KW - cooling

KW - electric vehicles

KW - iron compounds

KW - Kalman filters

KW - least squares approximations

KW - lithium compounds

KW - minimisation

KW - nonlinear filters

KW - secondary cells

KW - LiFePO4 battery realtime state-of-charge estimation

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KW - electric vehicle

KW - battery internal state estimation

KW - battery management system

KW - battery internal temperature estimation

KW - realtime SOC estimation

KW - battery thermoelectric model

KW - thermal submodel

KW - electrical submodel

KW - battery thermal behavior

KW - battery electrical behavior

KW - simulation error minimization method

KW - hybrid optimization method

KW - metaheuristic algorithm

KW - least-square approach

KW - time-varying model parameter

KW - heat dissipation condition

KW - joint extended Kalman filter

KW - LiFePO4

KW - Batteries

KW - State of charge

KW - Estimation

KW - Temperature measurement

KW - Temperature

KW - Heating

KW - Integrated circuit modeling

KW - Internal temperature estimation

KW - joint extended Kalman filter (EKF)

KW - state-of-charge (SOC) estimation

KW - thermoelectric model

U2 - 10.1109/TIE.2016.2610398

DO - 10.1109/TIE.2016.2610398

M3 - Article

SN - 1557-9948

VL - 64

SP - 654

EP - 663

JO - IEEE Transactions on Industrial Electronics

JF - IEEE Transactions on Industrial Electronics

IS - 1

ER -

Improved Realtime State-of-Charge Estimation of LiFePO 4 Battery Based on a Novel Thermoelectric Model

Abstract

Bibliographical note

Keywords

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