Abstract

Attempts have been made to find the best procedure for the detection of premature battery capacity loss (the so called “PCL”) in AGM-VRLA 48 V batteries operating in telecommunication systems. However, recorded changes in internal resistance and potential did not give clear indications of the beginning of the PCL effect. The obtained correlation between internal resistance and potential derived from used batteries does not show the expected trend in measured parameters. It seems that the application of Electrochemical Impedance Spectroscopy (EIS), which is a faster and non-destructive method, may solve this problem. It is demonstrated that the change in internal resistance (which is an indicator of the state of health (SoH)) can be determined from EIS spectra during continuous operation of 12 V monoblocks in a backup power source of a base transceiver station (BTS).

Highlights

  • Stationary lead-acid batteries are still the most important chemical backup power source used in many technical applications

  • (hereafter called “monoblocks”, in order to distinguish them from the 48 V system) absorptive glass mat (AGM)-VRLA

  • Two different approaches were implemented to determine the premature capacity loss effect in batteries operating in backup power sources of telecommunication antenna stations

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Summary

Introduction

Stationary lead-acid batteries are still the most important chemical backup power source used in many technical applications. Detection of increases in the internal resistance may indicate a loss of the real battery capacity [1,2], but this test is not sufficiently reliable. It is known that a credible assessment of the battery state consists of its discharge, but this requires that the system be turned off for several hours (like during electrical tests such as discharge by currents lasting for 10 or 20 h). It seems that via electrochemical impedance spectroscopy (EIS), it is more likely that faulty batteries will be detected. The combination of the two above-mentioned techniques should allow for the prediction and/or the identification of battery parts with significant capacity losses, without discharging the battery; the method shows high reliability [3,4,5,6,7,8]

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