Battery state of health and charge estimation using Polynomial Chaos theory

Saeid Bashash; Hosam Kadry Fathy

doi:10.1115/DSCC2013-4088

Battery state of health and charge estimation using Polynomial Chaos theory

Saeid Bashash, Hosam Kadry Fathy

Materials Research Institute (MRI)

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

9 Scopus citations

Abstract

In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.

Original language	English (US)
Title of host publication	Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;
Publisher	American Society of Mechanical Engineers (ASME)
Volume	1
ISBN (Print)	9780791856123
DOIs	https://doi.org/10.1115/DSCC2013-4088
State	Published - Jan 1 2013
Event	ASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States Duration: Oct 21 2013 → Oct 23 2013

Other

Other	ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Country/Territory	United States
City	Palo Alto, CA
Period	10/21/13 → 10/23/13

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

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10.1115/DSCC2013-4088

Cite this

Bashash, S., & Fathy, H. K. (2013). Battery state of health and charge estimation using Polynomial Chaos theory. In Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; (Vol. 1). [V001T05A006] American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2013-4088

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title = "Battery state of health and charge estimation using Polynomial Chaos theory",

abstract = "In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.",

author = "Saeid Bashash and Fathy, {Hosam Kadry}",

year = "2013",

month = jan,

day = "1",

doi = "10.1115/DSCC2013-4088",

language = "English (US)",

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booktitle = "Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;",

publisher = "American Society of Mechanical Engineers (ASME)",

note = "ASME 2013 Dynamic Systems and Control Conference, DSCC 2013 ; Conference date: 21-10-2013 Through 23-10-2013",

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Bashash, S & Fathy, HK 2013, Battery state of health and charge estimation using Polynomial Chaos theory. in Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;. vol. 1, V001T05A006, American Society of Mechanical Engineers (ASME), ASME 2013 Dynamic Systems and Control Conference, DSCC 2013, Palo Alto, CA, United States, 10/21/13. https://doi.org/10.1115/DSCC2013-4088

Battery state of health and charge estimation using Polynomial Chaos theory. / Bashash, Saeid; Fathy, Hosam Kadry.
Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;. Vol. 1 American Society of Mechanical Engineers (ASME), 2013. V001T05A006.

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

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N2 - In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.

AB - In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.

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Bashash S, Fathy HK. Battery state of health and charge estimation using Polynomial Chaos theory. In Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;. Vol. 1. American Society of Mechanical Engineers (ASME). 2013. V001T05A006 doi: 10.1115/DSCC2013-4088

Battery state of health and charge estimation using Polynomial Chaos theory

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