PSO-Based Identification of the Li-Ion Battery Cell Parameters

Tadeusz Białoń (), Roman Niestrój and Wojciech Korski
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Tadeusz Białoń: Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Roman Niestrój: Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Wojciech Korski: Łukasiewicz Research Network—Institute of Innovative Technologies EMAG, 40-189 Katowice, Poland

Energies, 2023, vol. 16, issue 10, 1-22

Abstract: The article describes the results of research aimed at identifying the parameters of the equivalent circuit of a lithium-ion battery cell, based on the results of HPPC (hybrid pulse power characterization) tests. The OCV (open circuit voltage) characteristic was determined, which was approximated using functions of various types, while making their comparison. The internal impedance of the cell was also identified in the form of a Thevenin RC circuit with one or two time constants. For this purpose, the HPPC pulse transients were approximated with a multi-exponential function. All of the mentioned approximations were carried out using an original method developed for this purpose, based on the PSO (particle swarm optimization) algorithm. As a result of the optimization experiments, the optimal configuration of the PSO algorithm was found. Three different cognition methods have been analyzed here: GB (global best), LB (local best), and FIPS (fully informed particle swarm). Three different swarm topologies were used: ring lattice, von Neumann, and FDR (fitness distance ratio). The choice of the cognition factor value was also analyzed, in order to provide a proper PSO convergence. The identified parameters of the cell model were used to build simulation models. Finally, the simulation results were compared with the results of the laboratory CDC (charge depleting cycle) test.

Keywords: lithium-ion nickel manganese cobalt (NMC) battery; particle swarm optimization (PSO); hybrid pulse power characterization (HPPC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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