Backstepping PDE-based adaptive observer for a single particle model of lithium-ion batteries
File(s)AAP_TAC_Sept_2017.pdf (947.1 KB)
Accepted version
Author(s)
Ascencio, P
Astolfi, A
Parisini, T
Type
Conference Paper
Abstract
This article deals with the observer design problem for the simultaneous estimation of the solid Lithium concentration and of the diffusion parameter for a Single Particle Model of Lithium-Ion Batteries. The design is based on the Backstepping PDE methodology, including a modified Volterra transformation to compensate for the diffusivity uncertainty. The resulting coupled/uncoupled Kernel-PDE and Ordinary Differential Equation (ODE) are recast, via a Sum-of-Squares decomposition, in terms of a convex optimization problem and solved by semidefinite programming, allowing, at each fixed time, an efficient computation of the state and parameter observer gains. In addition, based on the Moment approach, a novel scheme of inversion of the nonlinear output mapping of the Single Particle Model is presented. The effectiveness of this approach is illustrated by numerical simulations.
Date Issued
2016-12-29
Date Acceptance
2016-12-12
Citation
Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp.5623-5628
ISSN
0743-1546
Publisher
IEEE
Start Page
5623
End Page
5628
Journal / Book Title
Decision and Control (CDC), 2016 IEEE 55th Conference on
Copyright Statement
© 2016 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
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000400048105132&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
55th IEEE Conference on Decision and Control (CDC)
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Operations Research & Management Science
Engineering
CELL
Publication Status
Published
Start Date
2016-12-12
Finish Date
2016-12-14
Coverage Spatial
Las Vegas, NV