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Non-Invasive Experimental Identification of a Single Particle Model for LiFePO4 Cells

Authors :
Trivella, Andrea
Corno, Matteo
Radrizzani, Stefano
Savaresi, Sergio M.
Publication Year :
2023

Abstract

The rapid spread of Lithium-ions batteries (LiBs) for electric vehicles calls for the development of accurate physical models for Battery Management Systems (BMSs). In this work, the electrochemical Single Particle Model (SPM) for a high-power LiFePO4 cell is experimentally identified through a set of non-invasive tests (based on voltage-current measurements only). The SPM is identified through a two-step procedure in which the equilibrium potentials and the kinetics parameters are characterized sequentially. The proposed identification procedure is specifically tuned for LiFePO4 chemistry, which is particularly challenging to model due to the non-linearity of its open circuit voltage (OCV) characteristic. The identified SPM is compared with a second-order Equivalent Circuit Model (ECM) with State of Charge dependency. Models performance is compared on dynamic current profiles. They exhibit similar performance when discharge currents peak up to 1C (RMSE between simulation and measures smaller than 20 mV) while, increasing the discharge peaks up to 3C, ECM's performance significantly deteriorates while SPM maintains acceptable RMSE (< 50 mV).<br />Comment: Accepted for publication at the IFAC World Congress 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2306.14195
Document Type :
Working Paper