BATTERIES
technically excellent performance; it will be difficult
for immersion cooling to enter the mass production
automotive market due to the increased
weight and cost compared with currently utilised
methods. However, regulations regarding thermal
safety of electric vehicles are changing, and with
it these emerging technologies may start to take a
more significant market share.
Immersion cooling is just one of the emerging
technologies covered in the recent report from
IDTechEx on Thermal Management for Electric
Vehicles 2020-2030 which addresses and analyses the currently
utilised and emerging strategies for thermal management
across the electric vehicle market. In addition to the batteries, the
electric motors, power electronics and vehicle charging stations
XING Mobility provide complete
drivetrain solutions built around
their immersion cooled battery
packs which are modular to allow
fitting into the required geometry.
Source: XING Mobility, IDTechEx
Show, Santa Clara 2019.
have crucial thermal management considerations, with varying
approaches from manufacturers and emerging alternatives, all of
which are addressed in this brand new report.
Machine learning slashes battery fast charging
scheme development time
By Nick Flaherty
Researchers from Stanford University, MIT and the Toyota
Research Institute in the US have used machine learning
to cut the fast charging time for electric vehicle and energy
storage batteries dramatically. The group initially tested their
method on battery charge speed, and said it can be applied to
numerous other parts of the battery development pipeline and
even to non-energy technologies.
“In battery testing, you have to try a massive number of
things, because the performance
you get will vary drastically,” said
Stefano Ermon, an assistant professor
of computer science. “With
AI, we’re able to quickly identify the
most promising approaches and
cut out a lot of unnecessary experiments.”
The researchers wrote a machine
learning framework that,
based on only a few charging
cycles, predicted how batteries
would respond to different charging
approaches. The software also
decided in real time what fast charging approaches to focus on
or ignore. By reducing both the length and number of trials, the
researchers cut the testing process from almost two years to 16
days.
“We figured out how to greatly accelerate the testing process
for extreme fast charging,” said Peter Attia, who co-led the
study while he was a graduate student. “What’s really exciting,
though, is the method. We can apply this approach to many
other problems that, right now, are holding back battery development
for months or years.”
Fast charging optimization uses many trial-and-error
tests, something that is inefficient for humans, but the perfect
problem for a machine. “Machine learning is trial-and-error, but
in a smarter way,” said Aditya Grover, a graduate student in
computer science who also co-led the study. “Computers are
far better than us at figuring out when to explore – try new and
different approaches – and when to exploit, or zero in, on the
most promising ones.”
In a previous study, the researchers found that instead of
charging and recharging every battery until it failed – the usual
way of testing a battery’s lifetime –they could predict how long
a battery would last after only its first 100 charging cycles. This
is because the machine learning system, after being trained on
a few batteries cycled to failure, could find patterns in the early
data that determined how long the cell would last.
Machine learning then reduced the number of methods
they had to test. Instead of testing
every possible charging method
equally, or relying on intuition, the
computer learned from its experiences
to quickly find the best
protocols to test. By testing fewer
methods for fewer cycles, the team
quickly found an optimal ultra-fastcharging
protocol for their battery.
In addition to dramatically speeding
up the testing process, the solution
was also better, and much more
unusual, than what a battery scientist
would likely have devised. “It
gave us this surprisingly simple charging protocol – something
we didn’t expect,” said Ermon. “That’s the difference between
a human and a machine: The machine is not biased by human
intuition, which is powerful but sometimes misleading.”
The researchers said their approach could accelerate nearly
every piece of the battery development pipeline: from designing
the chemistry of a battery to determining its size and shape,
to finding better systems for manufacturing and storage. This
would have broad implications not only for electric vehicles but
for other types of energy storage. “This is a new way of doing
battery development,” said Patrick Herring, co-author of the
study and a scientist at the Toyota Research Institute. “Having
data that you can share among a large number of people in academia
and industry, and that is automatically analyzed, enables
much faster innovation.”
The study’s machine learning and data collection system will
be made available for future battery scientists to freely use, he
added.
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