Green Energy and Intelligent Transportation,
Год журнала:
2023,
Номер
2(6), С. 100128 - 100128
Опубликована: Сен. 7, 2023
The
concept
of
the
Internet-of-Batteries
(IoB)
has
recently
emerged
and
offers
great
potential
for
control
optimization
battery
utilization
in
electric
vehicles
(EV).
This
concept,
which
combines
aspects
Internet-of-Things
(IoT)
with
latest
advancements
technology
cloud
computing,
can
provide
a
wealth
new
information
about
health
performance.
be
used
to
improve
management
number
ways,
including
continuous
prognosis
improved
vehicle
management.
In
this
paper,
we
reviewed
detail
basic
structure
IoB,
based
on
many
existing
studies.
We
also
explored
benefits
approach,
such
as
Implementing
IoB
EVs
is
not
without
challenges,
faces
security
data,
cross-platform
functionality,
technical
complexities
applying
large
scale.
However,
are
significant
continued
research
development,
it
ability
revolutionize
EV
industry.
purpose
review
paper
comprehensive
overview
discussing
its
challenges.
provides
roadmap
future
development
highlighting
key
areas
that
need
addressed
fully
realize
technology.
Journal of Energy Chemistry,
Год журнала:
2023,
Номер
84, С. 335 - 346
Опубликована: Июнь 8, 2023
State
of
health
(SoH)
estimation
plays
a
key
role
in
smart
battery
prognostic
and
management.
However,
poor
generalization,
lack
labeled
data,
unused
measurements
during
aging
are
still
the
major
challenges
to
accurate
SoH
estimation.
Toward
this
end,
paper
proposes
self-supervised
learning
framework
boost
performance
Different
from
traditional
data-driven
methods
which
rely
on
considerable
training
dataset
obtained
numerous
cells,
proposed
method
achieves
robust
estimations
using
limited
data.
A
filter-based
data
preprocessing
technique,
enables
extraction
partial
capacity-voltage
curves
under
dynamic
charging
profiles,
is
applied
at
first.
Unsupervised
then
used
learn
characteristics
unlabeled
through
an
auto-encoder-decoder.
The
learned
network
parameters
transferred
downstream
task
fine-tuned
with
very
few
sparsely
boosts
framework.
has
been
validated
different
chemistries,
formats,
operating
conditions,
ambient.
accuracy
can
be
guaranteed
by
only
three
initial
20%
life
cycles,
overall
errors
less
than
1.14%
error
distribution
all
testing
scenarios
maintaining
4%,
robustness
increases
aging.
Comparisons
other
supervised
machine
demonstrate
superiority
method.
This
simple
data-efficient
promising
real-world
applications
variety
scenarios.
Advanced Energy Materials,
Год журнала:
2023,
Номер
13(39)
Опубликована: Авг. 18, 2023
Abstract
Precise
prediction
of
lithium‐ion
cell
level
aging
under
various
operating
conditions
is
an
imperative
but
challenging
part
ensuring
the
quality
performance
emerging
applications
such
as
electric
vehicles
and
stationary
energy
storage
systems.
Accurate
real‐time
battery‐aging
models,
which
require
exact
understanding
degradation
mechanisms
battery
components
materials,
could
in
turn
provide
new
insights
for
materials
basic
research.
Furthermore,
primary
barrier
to
meaningful
artificial
intelligence/machine
learning
accelerating
period
exploitation
accurate
mechanistic
descriptors.
This
review
comprehensively
summarizes
evolution
deterioration
at
material
different
environments
usage
scenarios,
including
intricate
relationships
between
mechanisms,
modes,
external
influences,
are
cornerstones
modeling
simulation
machine
techniques.
Recent
advances
electrochemical
models
coupled
with
internal
well
identification
tracking
parameters
shown,
particular
emphasis
on
electrode
balance
anticipated
trend
learning‐assisted
reliable
remaining
useful
life
prediction.
will
continue
play
essential
role
advanced
smart
research
management,
enhancing
its
while
shortening
experimental
sequences.
Advanced Materials,
Год журнала:
2023,
Номер
36(14)
Опубликована: Дек. 12, 2023
Lithium-metal
batteries
(LMBs)
with
high
energy
density
are
becoming
increasingly
important
in
global
sustainability
initiatives.
However,
uncontrollable
dendrite
seeds,
inscrutable
interfacial
chemistry,
and
repetitively
formed
solid
electrolyte
interphase
(SEI)
have
severely
hindered
the
advancement
of
LMBs.
Organic
molecules
been
ingeniously
engineered
to
construct
targeted
SEI
effectively
minimize
above
issues.
In
this
review,
multiple
organic
molecules,
including
polymer,
fluorinated
organosulfur,
comprehensively
summarized
insights
into
how
corresponding
elastic,
fluorine-rich,
organosulfur-containing
SEIs
provided.
A
variety
meticulously
selected
cases
analyzed
depth
support
arguments
molecular
design
SEI.
Specifically,
evolution
molecules-derived
is
discussed
principles
proposed,
which
beneficial
guiding
researchers
understand
architect
based
on
molecules.
This
review
provides
a
guideline
for
constructing
molecule-derived
will
inspire
more
concentrate
exploitation
Green Energy and Intelligent Transportation,
Год журнала:
2023,
Номер
2(6), С. 100128 - 100128
Опубликована: Сен. 7, 2023
The
concept
of
the
Internet-of-Batteries
(IoB)
has
recently
emerged
and
offers
great
potential
for
control
optimization
battery
utilization
in
electric
vehicles
(EV).
This
concept,
which
combines
aspects
Internet-of-Things
(IoT)
with
latest
advancements
technology
cloud
computing,
can
provide
a
wealth
new
information
about
health
performance.
be
used
to
improve
management
number
ways,
including
continuous
prognosis
improved
vehicle
management.
In
this
paper,
we
reviewed
detail
basic
structure
IoB,
based
on
many
existing
studies.
We
also
explored
benefits
approach,
such
as
Implementing
IoB
EVs
is
not
without
challenges,
faces
security
data,
cross-platform
functionality,
technical
complexities
applying
large
scale.
However,
are
significant
continued
research
development,
it
ability
revolutionize
EV
industry.
purpose
review
paper
comprehensive
overview
discussing
its
challenges.
provides
roadmap
future
development
highlighting
key
areas
that
need
addressed
fully
realize
technology.
