Extraction of Intrinsic Parameters of Lead-Acid Batteries Using Energy Recycling Technique

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)4765-4779
Journal / PublicationIEEE Transactions on Power Electronics
Volume34
Issue number5
Online published15 Aug 2018
Publication statusPublished - May 2019

Abstract

This paper presents the use of an energy recycling technique to extract the intrinsic parameters of lead-acid batteries. The charging and discharging currents of the battery under test are programmed by controlling a bidirectional DC-DC converter to profile the power flow between the battery and a supercapacitor. The sampled battery voltage and current information over the charging and discharging periods are used to estimate the parameters of a high-order electrical battery model with a modified particle swarm optimization algorithm and compare with the battery voltage and current predicted with the extracted parameters. With the energy recycling mechanism, the proposed parameter extraction process is environmentally friendly and has low power dissipation, thus increasing power handling density and allowing long testing duration. A prototype that can extract the intrinsic parameters of eight different types of 12V, 130A lead-acid batteries has been built. Its performance has been evaluated with different charging and discharging profiles. The estimated parameters are favorably verified with the theoretical predictions, results obtained by extended Kalman filter method, and results obtained on a calibrated commercial battery testing system. Results reveal that testing batteries with both charging and discharging processes gives a more accurate prediction of battery performance.

Research Area(s)

  • Batteries, batteries, Battery charge measurement, battery parameters, computational intelligence, Current measurement, Energy storage, machine intelligence, optimization, particle swarm optimization, Perturbation methods, Resistance, Supercapacitors, Testing