Predicting the slow death of lithium-ion batteries
Stanford technology predicts the slow death of lithium-ion batteries


Date:
September 14, 2020
Source:
Stanford University
Summary:
A new model offers a way to predict the condition of a battery's
internal systems in real-time with far more accuracy than existing
tools. In electric cars, the technology could improve driving
range estimates and prolong battery life.



FULL STORY ========================================================================== Batteries fade as they age, slowly losing power and storage capacity.


==========================================================================
As in people, aging plays out differently from one battery to another,
and it's next to impossible to measure or model all of the interacting mechanisms that contribute to decline. As a result, most of the systems
used to manage charge levels wisely and to estimate driving range in
electric cars are nearly blind to changes in the battery's internal
workings.

Instead, they operate more like a doctor prescribing treatment without
knowing the state of a patient's heart and lungs, and the particular
ways that environment, lifestyle, stress and luck have ravaged or spared
them. If you've kept a laptop or phone for enough years, you may have
seen where this leads firsthand: Estimates of remaining battery life
tend to diverge further from reality over time.

Now, a model developed by scientists at Stanford University offers a way
to predict the true condition of a rechargeable battery in real-time. The
new algorithm combines sensor data with computer modeling of the physical processes that degrade lithium-ion battery cells to predict the battery's remaining storage capacity and charge level.

"We have exploited electrochemical parameters that have never been used
before for estimation purposes," said Simona Onori, assistant professor
of energy resources engineering in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth). The research appears Sept. 11
in the journal IEEE Transactions on Control Systems Technology.

The new approach could help pave the way for smaller battery packs and
greater driving range in electric vehicles. Automakers today build
in spare capacity in anticipation of some unknown amount of fading,
which adds extra cost and materials, including some that are scarce or
toxic. Better estimates of a battery's actual capacity will enable a
smaller buffer.



========================================================================== "With our model, it's still important to be careful about how we are using
the battery system," Onori explained. "But if you have more certainty
around how much energy your battery can hold throughout its entire
lifecycle, then you can use more of that capacity. Our system reveals
where the edges are, so batteries can be operated with more precision."
The accuracy of the predictions in this model -- within 2 percent of
actual battery life as gathered from experiments, according to the
paper -- could also make it easier and cheaper to put old electric car batteries to work storing energy for the power grid. "As it is now,
batteries retired from electric cars will vary widely in their quality
and performance," Onori said. "There has been no reliable and efficient
method to standardize, test or certify them in a way that makes them competitive with new batteries custom-built for stationary storage."
Dropping old assumptions Every battery has two electrodes -- the cathode
and the anode -- sandwiching an electrolyte, usually a liquid. In a rechargeable lithium-ion battery, lithium ions shuttle back and forth
between the electrodes during charging and discharging. An electric car
may run on hundreds or thousands of these small battery cells, assembled
into a big battery pack that typically accounts for about 30 percent of
the total vehicle cost.

Traditional battery management systems typically rely on models that
assume the amount of lithium in each electrode never changes, said
lead study author Anirudh Allam, a PhD student in energy resources
engineering. "In reality, however, lithium is lost to side reactions as
the battery degrades," he said, "so these assumptions result in inaccurate models." Onori and Allam designed their system with continuously updated estimates of lithium concentrations and a dedicated algorithm for each electrode, which adjusts based on sensor measurements as the system
operates. They validated their algorithm in realistic scenarios using
standard industry hardware.



==========================================================================
On the road The model relies on data from sensors found in the battery management systems running in electric cars on the road today. "Our
algorithm can be integrated into current technologies to make them operate
in a smarter fashion," Onori said. In theory, many cars already on the
road could have the algorithm installed on their electronic control
units, she said, but the expense of that kind of upgrade makes it more
likely that automakers would consider the algorithm for vehicles not
yet in production.

The team focused their experiments on a type of lithium-ion battery
commonly used in electric vehicles (lithium nickel manganese cobalt
oxide) to estimate key internal variables such as lithium concentration
and cell capacity. But the framework is general enough that it should
be applicable to other kinds of lithium-ion batteries and to account
for other mechanisms of battery degradation.

"We showed that our algorithm is not just a nice theoretical work that can
run on a computer," she said. "Rather, it is a practical, implementable algorithm which, if adopted and used in cars tomorrow, can result in
the ability to have longer-lasting batteries, more reliable vehicles
and smaller battery packs."

========================================================================== Story Source: Materials provided by Stanford_University. Original written
by Josie Garthwaite. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Anirudh Allam, Simona Onori. Online Capacity Estimation for
Lithium-Ion
Battery Cells via an Electrochemical Model-Based Adaptive
Interconnected Observer. IEEE Transactions on Control Systems
Technology, 2020; 1 DOI: 10.1109/TCST.2020.3017566 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200914172928.htm

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