Zhang, L*; Wang, L*; Hinds, G; Lyu, C*; Zheng, J*; Li, J* (2014) Multi-objective optimization of lithium-ion battery model using genetic algorithm approach. J. Power Sources, 270. pp. 367-378.

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Abstract

A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 ºC and 30 ºC are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries.

Item Type:	Article
Keywords:	Parameter identification, Multi-objective genetic algorithm, Multi-physics model, Lithium-ion battery
Subjects:	Advanced Materials Advanced Materials > Electrochemistry
Identification number/DOI:	10.1016/j.jpowsour.2014.07.110
Last Modified:	02 Feb 2018 13:14
URI:	http://eprintspublications.npl.co.uk/id/eprint/6319

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