Between 2020 and 2021, electric vehicle (EV) registrations in the U.S. alone grew by 117.4%
year-over-year, according to Experian’s Automotive Market Trends Review: Q2 2021 report.
With the rise of market adoption comes a series of challenges that the automotive industry is
currently grappling with.
So, what are the most pressing issues with regards to Battery Management Systems (BMS)
and how is the supply chain responding?
It is acceptable to divide BMS approaches into two streams from a battery perspective: the
electrical equivalent circuit model (ECM) and the electrochemical model. It is in the latter
that BMS has the most hurdles to straddle, and they relate to safety, efficiency, and
In this regard, safety is certainly the most important aspect. As consumers are looking for
more range and fast-charging efficiency, thermal runaway is found to be the main culprit as
“fast charging requirements result in significant energy waste in the form of heat,” according
to Dr. Balakumar Balasingam, assistant professor at the University of Windsor, in Canada.
Then, the efficiency challenges hinge on the same thermal runaway issue, which produces
heat, waste, and degradation. And these three together affect the reliability of Li-ion batteries
and their capacity to be reused.
But very importantly, Dojan underlines, “SOTIF and FuSa can be developed in parallel if a new function is realized. If FuSa already has been accomplished and SOTIF will be developed afterwards, an ‘impact analysis’ shall be performed to identify issues where SOTIF activities may result in impacts on FuSa.”
A Tough Issue
In the next 10 years —according to predictions— EV sales are expected to soar by almost
500%. In this outlook, “the state-of-the-art BMS algorithms heavily depend on prior
characterization carried out in laboratories, and consequently, they are only effective for first-
time use of batteries,” Balasingam says.
“Considering the fact that the first use of the battery alters its electrochemical characteristics
in unique ways, traditional BMS approaches that rely on empirical modelling, under the
assumption that batteries of the same chemistry and size have similar characteristics, will be
inadequate to manage used batteries,” he adds.
In detail, the state of charge estimation, real-time state of health estimation, optimal charging
—slow SOC and SOH characterizations— are part of the problem. Further, state-of-the-art
BMS is tied to a specific chemistry, manufacturer, and battery. “BMS is not universal; this
restricts battery selection and results in increased cost.”
As these challenges have a direct impact on consumer satisfaction and adoption, there are
several courses of actions industry stakeholders can take.
In terms of open-circuit voltage modelling, for instance, a careful approach and optimization
can yield parameters that are appropriate for an extensive scope of temperatures. “The need
for careful modelling is demonstrated using scaling —a strategy that, when ignored, results in
up to 90% higher SOC errors,” Balasingam asserts.
Aspects such as real-time battery impedance estimation and battery capacity estimation are
essential to overcoming temperature and several other issues and make BMS more effective.
Further, since newer battery versions have somewhat varied chemical compositions, the
question is how to create a BMS that can update itself to battery types that are always
changing. In this regard, Balansingam asserts that one way to go is to rely on probabilistic
data and information fusion.
Cell-balancing circuitry (CBC) can also maintain the battery-pack balanced. “In addition to
this, CBC is also responsible for thermal balancing of the battery pack,” since “cell imbalance
has many drawbacks —from reduced power output and reduced life cycles to catastrophic
failures, including fire.”