Liu, K, Li, K orcid.org/0000-0001-6657-0522, Yang, Z et al. (2 more authors) (2016) Battery optimal charging strategy based on a coupled thermoelectric model. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC). 2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 Jul 2016, Vancouver, BC, Canada. IEEE , pp. 5084-5091. ISBN 978-1-5090-0623-6
Abstract
Battery charging strategy is a key issue in battery management system to ensure good battery performance and safe operation during the charging process. In this paper, a novel battery optimal charging strategy is proposed by applying the TLBO algorithm to a LiFeP04 battery for an optimal charging based on a coupled thermoelectric model. A specific dual-objective function including battery charging time and temperature rise (both battery interior and surface) is formulated first. Then a battery optimal charging strategy is presented in detail by using the TLBO algorithm, aiming at finding a suitable constant-current-constant-voltage (CCCV) current profile to minimize the dual-objective function. Besides, the effects of different weights in dual-objective function on the optimal charging profile are analyzed. Simulation results demonstrate that the presented optimal charging strategy can provide effective and acceptable optimal charge current profile. The strategy can be also easily implemented to other battery types to effectively balance the battery charging time and battery temperature rise during charging process.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | battery optimal charging; teaching-learning-based-optimization; coupled thermoelectric model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 12:27 |
Last Modified: | 05 Nov 2019 12:27 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/cec.2016.7748334 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153043 |