文献类型：期刊文献

英文题名：Multi-objective optimization of charging patterns for lithium-ion battery management

作者：Liu, Kailong[1];Li, Kang[1];Ma, Haiping[2];Zhang, Jianhua[3];Peng, Qiao[4]

机构：[1]Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland;[2]Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Zhejiang, Peoples R China;[3]North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China;[4]Nanjing Univ Informat Sci & Technol, Sch Phys & Optoelect Engn, Nanjing 210044, Jiangsu, Peoples R China

年份：2018

卷号：159

起止页码：151

外文期刊名：ENERGY CONVERSION AND MANAGEMENT

收录：SCI-EXPANDED(收录号：WOS:000426412000015)、、EI(收录号：20181404977821)、Scopus(收录号：2-s2.0-85044626473)、WOS

基金：This work was partially funded by UK EPSRC under grant 'Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)' EP/L001063/1 and NSFC under grants 61673256 and 61533010. Kailong Liu would like to thank the EPSRC for sponsoring his research.

语种：英文

外文关键词：Lithium-ion battery management; Optimal charging pattern; Energy conversion efficiency; Battery internal temperature variation; Battery thermal management; Multi-objective biogeography-based optimization

外文摘要：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.