Initialisation Schemes for the Polynomial Nonlinear State Space Model of the Lithium-ion Battery from Multiple Datasets .

Rishi Relan; Koen Tiels; Jean-Marc Paul Timmermans; Joannes Schoukens

Initialisation Schemes for the Polynomial Nonlinear State Space Model of the Lithium-ion Battery from Multiple Datasets .

Rishi Relan
, Koen Tiels
, Jean-Marc Paul Timmermans
, Joannes Schoukens

Research output: Unpublished contribution to conference › Unpublished paper

Abstract

Due to slow dynamics data acquisition for battery modelling is a time consuming process and sometimes increasing the measurement time is either not possible due to constraints in data acquisition hardware or it is not possible to cover the whole range of operation in one experiment, hence data from multiple experiments must be acquired. In this paper, two different methodologies to initialise the Polynomial Nonlinear State Space (PNLSS) model structure are proposed, which are based on the Local Polynomial Method (LPM) to estimate nonparametrically the Best Linear Approximation BLA) from multiple datasets.

Original language	English
Pages	140
Number of pages	1
Publication status	Published - 28 Mar 2017
Event	36th Benelux Meeting on Systems and Control - Sol-Cress, Spa, Spa, Belgium Duration: 28 Mar 2017 → 30 Mar 2017 http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/

Conference

Conference	36th Benelux Meeting on Systems and Control
Country/Territory	Belgium
City	Spa
Period	28/03/17 → 30/03/17
Internet address	http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/ http://www.beneluxmeeting.nl/2017/

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title = "Initialisation Schemes for the Polynomial Nonlinear State Space Model of the Lithium-ion Battery from Multiple Datasets .",

abstract = "Due to slow dynamics data acquisition for battery modelling is a time consuming process and sometimes increasing the measurement time is either not possible due to constraints in data acquisition hardware or it is not possible to cover the whole range of operation in one experiment, hence data from multiple experiments must be acquired. In this paper, two different methodologies to initialise the Polynomial Nonlinear State Space (PNLSS) model structure are proposed, which are based on the Local Polynomial Method (LPM) to estimate nonparametrically the Best Linear Approximation BLA) from multiple datasets.",

author = "Rishi Relan and Koen Tiels and Timmermans, \{Jean-Marc Paul\} and Joannes Schoukens",

year = "2017",

month = mar,

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language = "English",

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note = "36th Benelux Meeting on Systems and Control ; Conference date: 28-03-2017 Through 30-03-2017",

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T1 - Initialisation Schemes for the Polynomial Nonlinear State Space Model of the Lithium-ion Battery from Multiple Datasets .

AU - Relan, Rishi

AU - Tiels, Koen

AU - Timmermans, Jean-Marc Paul

AU - Schoukens, Joannes

PY - 2017/3/28

Y1 - 2017/3/28

N2 - Due to slow dynamics data acquisition for battery modelling is a time consuming process and sometimes increasing the measurement time is either not possible due to constraints in data acquisition hardware or it is not possible to cover the whole range of operation in one experiment, hence data from multiple experiments must be acquired. In this paper, two different methodologies to initialise the Polynomial Nonlinear State Space (PNLSS) model structure are proposed, which are based on the Local Polynomial Method (LPM) to estimate nonparametrically the Best Linear Approximation BLA) from multiple datasets.

AB - Due to slow dynamics data acquisition for battery modelling is a time consuming process and sometimes increasing the measurement time is either not possible due to constraints in data acquisition hardware or it is not possible to cover the whole range of operation in one experiment, hence data from multiple experiments must be acquired. In this paper, two different methodologies to initialise the Polynomial Nonlinear State Space (PNLSS) model structure are proposed, which are based on the Local Polynomial Method (LPM) to estimate nonparametrically the Best Linear Approximation BLA) from multiple datasets.

M3 - Unpublished paper

SP - 140

T2 - 36th Benelux Meeting on Systems and Control

Y2 - 28 March 2017 through 30 March 2017

ER -

Initialisation Schemes for the Polynomial Nonlinear State Space Model of the Lithium-ion Battery from Multiple Datasets .

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