Battery State of Charge Estimation Considering the Uncertainties of Battery Ageing

Xiaopeng TANG; Ke YAO; Qi LIU; Furong GAO

Battery State of Charge Estimation Considering the Uncertainties of Battery Ageing

Xiaopeng TANG, Ke YAO, Qi LIU, Furong GAO

Research output: Other Conference Contributions › Conference Paper (other) › Other Conference Paper

Abstract

The inevitable battery degradation poses significant challenges to the accurate estimation of the state-of-charge (SoC), a key battery state that reflects the remaining driving distance of electric vehicles. We here develop an estimator that is robust to battery ageing. First, a set of random models is drawn upon an accurate base model. Then, SoC estimations are carried out on these random models with light-weight gain-switching observers. Finally, the outputs of the gain-switching observers are fused to generate accurate SoC values. The proposed solution is experimentally verified, exhibiting 2% estimation accuracy for batteries with different ageing degrees.

Original language	English
Number of pages	4
Publication status	Published - 2021
Externally published	Yes
Event	The 4th International Conference on Electrical Intelligent Vehicles - Nanjing, Jiangsu, China Duration: 25 Jun 2021 → 28 Jun 2021

Conference

Conference	The 4th International Conference on Electrical Intelligent Vehicles
Abbreviated title	ICEIV2021
Country/Territory	China
City	Nanjing, Jiangsu
Period	25/06/21 → 28/06/21

Bibliographical note

This work was supported in part by the Ministry of Science and Technology of People's Republic of China (SQ2019YFB170029), Guangdong scientific and technological project (2017B010120002), Hong Kong Research Grants Council CERG project (16208520), and the Shenzhen-Hong Kong Innovation Circle Category D Project: SGDX 2019081623240948.

Keywords

Electric Vehicles
State of Charge
State Observer
Battery Management System

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https://repository.hkust.edu.hk/ir/bitstream/1783.1-116514/1/039970_1.pdf

Cite this

@conference{c1f33e9e5dcc4c70a7e746dbccc1c175,

title = "Battery State of Charge Estimation Considering the Uncertainties of Battery Ageing",

abstract = "The inevitable battery degradation poses significant challenges to the accurate estimation of the state-of-charge (SoC), a key battery state that reflects the remaining driving distance of electric vehicles. We here develop an estimator that is robust to battery ageing. First, a set of random models is drawn upon an accurate base model. Then, SoC estimations are carried out on these random models with light-weight gain-switching observers. Finally, the outputs of the gain-switching observers are fused to generate accurate SoC values. The proposed solution is experimentally verified, exhibiting 2% estimation accuracy for batteries with different ageing degrees.",

keywords = "Electric Vehicles, State of Charge, State Observer, Battery Management System",

author = "Xiaopeng TANG and Ke YAO and Qi LIU and Furong GAO",

note = "This work was supported in part by the Ministry of Science and Technology of People's Republic of China (SQ2019YFB170029), Guangdong scientific and technological project (2017B010120002), Hong Kong Research Grants Council CERG project (16208520), and the Shenzhen-Hong Kong Innovation Circle Category D Project: SGDX 2019081623240948.; The 4th International Conference on Electrical Intelligent Vehicles , ICEIV2021 ; Conference date: 25-06-2021 Through 28-06-2021",

year = "2021",

language = "English",

}

TY - CONF

T1 - Battery State of Charge Estimation Considering the Uncertainties of Battery Ageing

AU - TANG, Xiaopeng

AU - YAO, Ke

AU - LIU, Qi

AU - GAO, Furong

N1 - This work was supported in part by the Ministry of Science and Technology of People's Republic of China (SQ2019YFB170029), Guangdong scientific and technological project (2017B010120002), Hong Kong Research Grants Council CERG project (16208520), and the Shenzhen-Hong Kong Innovation Circle Category D Project: SGDX 2019081623240948.

PY - 2021

Y1 - 2021

N2 - The inevitable battery degradation poses significant challenges to the accurate estimation of the state-of-charge (SoC), a key battery state that reflects the remaining driving distance of electric vehicles. We here develop an estimator that is robust to battery ageing. First, a set of random models is drawn upon an accurate base model. Then, SoC estimations are carried out on these random models with light-weight gain-switching observers. Finally, the outputs of the gain-switching observers are fused to generate accurate SoC values. The proposed solution is experimentally verified, exhibiting 2% estimation accuracy for batteries with different ageing degrees.

AB - The inevitable battery degradation poses significant challenges to the accurate estimation of the state-of-charge (SoC), a key battery state that reflects the remaining driving distance of electric vehicles. We here develop an estimator that is robust to battery ageing. First, a set of random models is drawn upon an accurate base model. Then, SoC estimations are carried out on these random models with light-weight gain-switching observers. Finally, the outputs of the gain-switching observers are fused to generate accurate SoC values. The proposed solution is experimentally verified, exhibiting 2% estimation accuracy for batteries with different ageing degrees.

KW - Electric Vehicles

KW - State of Charge

KW - State Observer

KW - Battery Management System

M3 - Conference Paper (other)

T2 - The 4th International Conference on Electrical Intelligent Vehicles

Y2 - 25 June 2021 through 28 June 2021

ER -

Battery State of Charge Estimation Considering the Uncertainties of Battery Ageing

Abstract

Conference

Bibliographical note

Keywords

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Cite this