Nejad, S., Gladwin, D.T. orcid.org/0000-0001-7195-5435 and Stone, D.A. orcid.org/0000-0002-5770-3917 (2016) On-chip implementation of Extended Kalman Filter for adaptive battery states monitoring. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IIECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society , 24-27 Jul 2016, Florence, Italy. Institute of Electrical and Electronics Engineers , pp. 5513-5518. ISBN 9781509034741
Abstract
This paper reports the development and implementation of an adaptive lithium-ion battery monitoring system. The monitoring algorithm is based on the nonlinear Dual Extended Kalman Filter (DEKF), which allows for simultaneous states and parameters estimation. The hardware platform consists of an ARM cortex-M0 processor with six embedded analogue-to-digital converters (ADCs) for data acquisition. Two definitions for online state-of-health (SOH) characterisation are presented; one energy-based and one power-based. Moreover, a method for online estimation of battery's capacity, which is used in SOH characterisation is proposed. Two definitions for state-of-power (SOP) are adopted. Despite the presence of large sensor noise and incorrect filter initialisation, the DEKF algorithm poses excellent SOC and SOP tracking capabilities during a dynamic discharge test. The SOH prediction results are also in good agreement with actual measurements.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 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: | Estimation; Lithium-ion; Adaptive; Battery Monitoring System; State-of-Charge; State-of-Health; State-of-Power |
Dates: |
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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 Mar 2017 10:48 |
Last Modified: | 19 Dec 2022 12:51 |
Published Version: | https://doi.org/10.1109/IECON.2016.7793527 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/IECON.2016.7793527 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113904 |