Online battery state of power prediction using PRBS and extended Kalman filter

Nejad, S. and Gladwin, D.T. orcid.org/0000-0001-7195-5435 (2019) Online battery state of power prediction using PRBS and extended Kalman filter. IEEE Transactions on Industrial Electronics. ISSN 0278-0046

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Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Battery excitation; Kalman filtering; Pseudo random binary sequences
Dates:
  • Accepted: 17 March 2019
  • Published (online): 11 June 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 23 Jul 2019 13:54
Last Modified: 23 Jul 2019 13:54
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/tie.2019.2921280

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