Instrumentation & Measurement Magazine 25-7 - 19

Fig. 6. Experimental setup for validation of BFG, showing the different equipment used for data collection for BFG
evaluation (from [17], ©2015 Elsevier).
metrics reported in is that these metrics can only be computed
in a laboratory setting using data collected through the
special BFG validation load profile. This means that a BFG
can only be tested before it is deployed. However, the performance
of a BFG may be affected after deployment due to
factors such as failing measurement systems, change in battery
behavior, and other types of faults. Cloud-based battery
management techniques might be exploited to identify suitable
data for BFG validation after its deployment to various
devices.
Summary
Rechargeable batteries are becoming mainstays in various
transportation, industry, and household applications. A BMS
is crucial for the safe, efficient, and reliable operation of a battery
power system. An accurate BFG is critical for ensuring
the safe, efficient, and reliable operation of a battery power
system. Research about battery fuel gauging is very active in
both the academic and industrial communities. One of the major
unresolved challenges in battery fuel gauging is that there
are no direct or indirect measures of the SOH of a battery. At
present, both the PF and CF are computed with reference to an
initial value that is assumed to be known.
When that initial value is unknown, SOH estimation becomes
challenging. Finding an independent and reliable
measure of SOH has the potential to revolutionize battery reuse
applications. Unlike BFG, which is an actively researched
topic, BFG evaluation is among the under-investigated topics.
Formal approaches need to be developed to translate
various uncertainties involved in battery fuel gauging towards
the end user-this is an avenue that best suits the
interests and expertise of the Instrumentation and Measurement
community.
October 2022
References
[1] B. Balasingam, G. V. Avvari, B. Pattipati, K. R. Pattipati, and Y.
Bar-Shalom, " A robust approach to battery fuel gauging, part
I: real time model identification, " J. Power Sources, vol. 272, pp.
1142-1153, 2014.
[2] B. Balasingam, G. V. Avvari, B. Pattipati, K. R. Pattipati, and Y.
Bar-Shalom, " A robust approach to battery fuel gauging, part
II: real time capacity estimation, " J. Power Sources, vol. 269, pp.
949-961, Dec. 2014.
[3] M. S. Ahmed, S. A. Raihan, and B. Balasingam, " A scaling
approach for improved state of charge representation in
rechargeable batteries, " Appl. Energy, vol. 267, p. 114880, Jun.
2020.
[4] K. Movassagh, A. Raihan, B. Balasingam, and K. Pattipati, " A
critical look at Coulomb counting approach for state of charge
estimation in batteries, " Energies 2021, vol. 14, no. 14, p. 4074, Jul.
2021.
[5] A. Abdollahi et al., " Optimal battery charging, part I: minimizing
time-to-charge, energy loss, and temperature rise for OCVresistance
battery model, " J. Power Sources, vol. 303, pp. 388-398,
Jan. 2016.
[6] A. Abdollahi et al., " Optimal charging for general equivalent
electrical battery model, and battery life management, " J. Energy
Storage, vol. 9, pp. 47-58, Feb. 2017.
[7] G. L. Plett, Battery Management Systems, Volume II: Equivalent-Dircuit
Methods. Norwood, Massachusetts, USA: Artech House, 2015.
[8] P. Kumar, D. Chaudhary, P. Varshney, U. Varshney, S. M. Yahya,
and Y. Rafat, " Critical review on battery thermal management and
role of nanomaterial in heat transfer enhancement for electrical
vehicle application, " J. Energy Storage, vol. 32, p. 102003, 2020.
[9] P. Cicconi, P. Kumar, and P. Varshney, " A support approach for
the modular design of li-ion batteries: A test case with PCM, " J.
Energy Storage, vol. 31, p. 101684, Oct. 2020.
IEEE Instrumentation & Measurement Magazine
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Instrumentation & Measurement Magazine 25-7

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