A sliding mode observer SOC estimation method based on parameter adaptive battery model

Bo Ning; Jun Xu; Binggang Cao; Bin Wang; Guangcan Xu

doi:10.1016/j.egypro.2016.06.088

A sliding mode observer SOC estimation method based on parameter adaptive battery model

Bo Ning
, Jun Xu
, Binggang Cao
, Bin Wang
, Guangcan Xu

School of Mechanical Engineering

Xi'an Jiaotong University

Research output: Contribution to journal › Conference article › peer-review

43 Scopus citations

Abstract

Errors of a battery model will dramatically enlarge as the internal parameters of a battery varying. To reduce the systematic errors, a parameter adaptive battery model is proposed. Based on it, sliding mode algorithm is adopted to estimate the SOC of a battery. The experimental platform is constructed and the UDDS driving cycles is used to verify the method. The results show the error of SOC estimation is less than 2% and it indicates the monitoring algorithm is of great value to power batteries which are generally used in variable environment.

Original language	English
Pages (from-to)	619-626
Number of pages	8
Journal	Energy Procedia
Volume	88
DOIs	https://doi.org/10.1016/j.egypro.2016.06.088
State	Published - 1 Jun 2016
Event	Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 - Fuzhou, China Duration: 15 Nov 2015 → 17 Nov 2015

Keywords

Battery model
Parameter adaptive battery model
SOC estimation
Sliding mode observer

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10.1016/j.egypro.2016.06.088

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title = "A sliding mode observer SOC estimation method based on parameter adaptive battery model",

abstract = "Errors of a battery model will dramatically enlarge as the internal parameters of a battery varying. To reduce the systematic errors, a parameter adaptive battery model is proposed. Based on it, sliding mode algorithm is adopted to estimate the SOC of a battery. The experimental platform is constructed and the UDDS driving cycles is used to verify the method. The results show the error of SOC estimation is less than 2\% and it indicates the monitoring algorithm is of great value to power batteries which are generally used in variable environment.",

keywords = "Battery model, Parameter adaptive battery model, SOC estimation, Sliding mode observer",

author = "Bo Ning and Jun Xu and Binggang Cao and Bin Wang and Guangcan Xu",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.; Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 ; Conference date: 15-11-2015 Through 17-11-2015",

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TY - JOUR

T1 - A sliding mode observer SOC estimation method based on parameter adaptive battery model

AU - Ning, Bo

AU - Xu, Jun

AU - Cao, Binggang

AU - Wang, Bin

AU - Xu, Guangcan

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Errors of a battery model will dramatically enlarge as the internal parameters of a battery varying. To reduce the systematic errors, a parameter adaptive battery model is proposed. Based on it, sliding mode algorithm is adopted to estimate the SOC of a battery. The experimental platform is constructed and the UDDS driving cycles is used to verify the method. The results show the error of SOC estimation is less than 2% and it indicates the monitoring algorithm is of great value to power batteries which are generally used in variable environment.

AB - Errors of a battery model will dramatically enlarge as the internal parameters of a battery varying. To reduce the systematic errors, a parameter adaptive battery model is proposed. Based on it, sliding mode algorithm is adopted to estimate the SOC of a battery. The experimental platform is constructed and the UDDS driving cycles is used to verify the method. The results show the error of SOC estimation is less than 2% and it indicates the monitoring algorithm is of great value to power batteries which are generally used in variable environment.

KW - Battery model

KW - Parameter adaptive battery model

KW - SOC estimation

KW - Sliding mode observer

UR - https://www.scopus.com/pages/publications/85007610521

U2 - 10.1016/j.egypro.2016.06.088

DO - 10.1016/j.egypro.2016.06.088

M3 - 会议文章

AN - SCOPUS:85007610521

SN - 1876-6102

VL - 88

SP - 619

EP - 626

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015

Y2 - 15 November 2015 through 17 November 2015

ER -

A sliding mode observer SOC estimation method based on parameter adaptive battery model

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Keywords

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