Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries
File(s)Optimal_doe.pdf (10.02 MB)
Accepted version
Author(s)
Pozzi, Andrea
Gopalakrishnan, Krishnakumar
Ciaramella, Gabriele
Volkwein, Stefan
Raimondo, Davide
Type
Conference Paper
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).
Editor(s)
Teel, Andrew R
Egerstedt, Magnus
Date Issued
2019-01-21
Date Acceptance
2018-07-13
Citation
2018 IEEE Conference on Decision and Control (CDC), 2019, pp.6482-6487
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
6482
End Page
6487
Journal / Book Title
2018 IEEE Conference on Decision and Control (CDC)
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://cdc2018.ieeecss.org/
Source
IEEE Conference on Decision and Control
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Engineering
IDENTIFIABILITY
MANAGEMENT
CHARGE
Publication Status
Published
Start Date
2018-12-17
Finish Date
2018-12-19
Coverage Spatial
Miami Beach, FL, USA
Date Publish Online
2019-01-21