Multi-objective optimization of charging patterns for lithium-ion battery management

Kailong Liu, Kang Li, Haiping Ma, Jianhua Zhang, Qiao Peng

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Lithium-ion (Li-ion) battery charging is a crucial issue in energy management of electric vehicles. Developing suitable charging patterns, while taking into account of various contradictory objectives and constraints is a key but challenging topic in battery management. This paper develops a model based strategy that optimizes the charging patterns while considers various key parameters such as the charging speed, energy conversion efficiency as well as temperature variations. To achieve this, a battery model coupling both the electric and thermal characteristics is first introduced. Three key but conflicting objectives, including the charging time, energy loss and temperature rise especially for internal temperature, are formulated. Then, multi-objective biogeography-based optimization (M-BBO) approaches are employed to search the optimal charging patterns and to balance various objectives with different combinations. Optimization results of four M-BBO approaches are compared, and the Pareto fronts for battery charging with various dual-objectives and triple-objectives are analysed in detail. Experimental results confirm that the developed strategy can offer feasible charging patterns and achieve a desirable trade-off among charging speed, energy conversion efficiency and temperature variations. The Pareto fronts obtained by this strategy can be adopted as references to adjust charging pattern to further satisfy various requirements in different charging applications.
Original languageEnglish
Pages (from-to)151-162
Number of pages12
JournalEnergy Conversion and Management
Volume159
Early online date23 Jan 2018
Publication statusPublished - 01 Mar 2018

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