Optimization of dynamic battery paramter characterization experiments via differential evolution

Joel Forman, Jeffrey Stein, Hosam Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

Characterization is important for making models match reality and allowing for quick and accurate measurements of parameters. In this paper we present a method for designing dynamic battery experiments using an evolutionary algorithm that directly generates Pareto fronts via differential evolution. This optimization creates current trajectories for multiple objectives, namely, maximizing Fisher information gathered while minimizing battery damage. An estimator is used on simulated battery experiments to verify the improvements associated with these trajectories. This exercise illustrates the experimental trade-offs between gathering parameter information and causing battery degradation. The procedure in this paper is widely applicable as both the battery model and parameter's of interest can be substituted as needed.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages867-874
Number of pages8
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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