Li-ion battery and supercapacitor modeling for electric vehicles based on pulse - Pseudo random binary sequence Articles uri icon

publication date

  • November 2021

start page

  • 11378

end page

  • 11389

issue

  • 11

volume

  • 70

International Standard Serial Number (ISSN)

  • 0018-9545

Electronic International Standard Serial Number (EISSN)

  • 1939-9359

abstract

  • Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse - Pseudo Random Binary Sequence (Pulse - PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications.

keywords

  • equivalent circuit model; frequency spectrum; lithium-ion battery; parameter identification; pseudo-random binary sequence; supercapacitor