Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries

Andrea Pozzi; Gabriele Ciaramella; Krishnakumar Gopalakrishnan; Stefan Volkwein; Davide M. Raimondo

doi:10.1109/CDC.2018.8619340

Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries

Andrea Pozzi^*, Gabriele Ciaramella, Krishnakumar Gopalakrishnan, Stefan Volkwein, Davide M. Raimondo

^*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in libro › Contributo a convegno

Abstract

Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).

Lingua originale	English
Titolo della pubblicazione ospite	2018 IEEE Conference on Decision and Control (CDC)
Pagine	6482-6487
Numero di pagine	6
DOI	https://doi.org/10.1109/CDC.2018.8619340
Stato di pubblicazione	Pubblicato - 2018
Evento	57th IEEE Conference on Decision and Control - United States Durata: 17 dic 2018 → 19 dic 2018

Serie di pubblicazioni

Nome	PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL

Convegno

Convegno	57th IEEE Conference on Decision and Control
Città	United States
Periodo	17/12/18 → 19/12/18

Keywords

Optimal Experimental Design

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10.1109/CDC.2018.8619340

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title = "Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries",

abstract = "Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).",

keywords = "Optimal Experimental Design, Optimal Experimental Design",

author = "Andrea Pozzi and Gabriele Ciaramella and Krishnakumar Gopalakrishnan and Stefan Volkwein and Raimondo, {Davide M.}",

year = "2018",

doi = "10.1109/CDC.2018.8619340",

language = "English",

series = "PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL",

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booktitle = "2018 IEEE Conference on Decision and Control (CDC)",

note = "57th IEEE Conference on Decision and Control ; Conference date: 17-12-2018 Through 19-12-2018",

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Pozzi, A, Ciaramella, G, Gopalakrishnan, K, Volkwein, S & Raimondo, DM 2018, Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries. in 2018 IEEE Conference on Decision and Control (CDC). PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL, pagg. 6482-6487, 57th IEEE Conference on Decision and Control, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8619340

Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries. / Pozzi, Andrea; Ciaramella, Gabriele; Gopalakrishnan, Krishnakumar et al.
2018 IEEE Conference on Decision and Control (CDC). 2018. pag. 6482-6487 (PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL).

Risultato della ricerca: Contributo in libro › Contributo a convegno

TY - GEN

T1 - Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries

AU - Pozzi, Andrea

AU - Ciaramella, Gabriele

AU - Gopalakrishnan, Krishnakumar

AU - Volkwein, Stefan

AU - Raimondo, Davide M.

PY - 2018

Y1 - 2018

N2 - Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).

AB - Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).

KW - Optimal Experimental Design

UR - http://hdl.handle.net/10807/193664

U2 - 10.1109/CDC.2018.8619340

DO - 10.1109/CDC.2018.8619340

M3 - Conference contribution

T3 - PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL

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EP - 6487

BT - 2018 IEEE Conference on Decision and Control (CDC)

T2 - 57th IEEE Conference on Decision and Control

Y2 - 17 December 2018 through 19 December 2018

ER -

Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries

Abstract

Serie di pubblicazioni

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Keywords

OSS delle Nazioni Unite

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