An advanced lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

Kailong Liu, Kang Li, Zhile Yang, Cheng Zhang, Jing Deng

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)
2 Downloads (Pure)

Abstract

Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types.
Original languageEnglish
Pages (from-to)330-344
Number of pages15
JournalElectrochimica Acta
Volume225
Early online date23 Dec 2016
DOIs
Publication statusPublished - 20 Jan 2017

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Electrochimica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electrochimica, 225, (2017) DOI: 10.1016/j.electacta.2016.12.129

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • LiFePO4 battery
  • Battery energy conversion
  • Coupled thermoelectric modelTeaching-learning-based-optimization
  • Teaching-learning-based-optimization

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