Genetic parameter identification of the Doyle-Fuller-Newman model from experimental cycling of a LiFePO4 battery

Joel C. Forman, Scott J. Moura, Jeffrey L. Stein, Hosam Kadry Fathy

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

44 Scopus citations


This paper examines the identification of the parameters of the Doyle-Fuller-Newman electrochemistry-based Lithium-ion battery model from voltage and current cycling data. The battery used in this study has a lithium iron phosphate cathode chemistry intended for high-power applications such as plug-in hybrid electric vehicles. The variables optimized for model identification include parameterizations of the model's anode equilibrium potential, cathode equilibrium potential, and solution conductivity. A genetic algorithm is used to optimize these model parameters against experimental data. The resulting identified model fits two experimental data sets used for system identification, as well as separate validation data sets corresponding to five different vehicle drive cycles. These drive cycles simulate the current a battery would undergo while used in a plug-in hybrid vehicle battery pack. The accuracy of the parameters is investigated using various validation data sets. This is believed to be the first attempt at fitting nearly all of the parameters and functions in the DFN model simultaneously using only voltage and current data. Computational logistics of using a genetic algorithm to identify 88 parameters of an electrochemistry-based model for 7.5 hours of cycling data are discussed. In addition, a detailed analysis of local parameter identifiability is presented.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Number of pages8
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

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


Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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