Advances in computer and electrical engineering book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 193 - 250
Published: Dec. 6, 2024
Nature-inspired
optimization
approaches
play
a
vital
role
in
fostering
smart
cities
by
adopting
natural
system
efficiency.
These
approaches,
which
are
founded
on
phenomena
biology,
ecology,
and
physical
science,
optimize
resource
use,
energy
transportation
systems.
They
offer
new
possibilities
for
intelligent
to
mimic
naturally
occurring
processes,
may
lead
sustainable
development.
Besides
renown
resilience,
they
possess
high
problem-solving
capabilities
that
critical
addressing
in-city
unforeseen
challenges.
The
most
recent
publications
explore
opportunities
using
such
methods
grids,
traffic
flows,
waste
recycling,
resources
cities.
By
combining
AIML
techniques
with
these
algorithms,
researchers
developing
more
powerful
adaptive
models
address
the
evolving
needs
of
modern
urban
environments.
This
study
presents
an
overview
innovative
shaping
future
promoting
sustainability,
efficiency,
resilience
infrastructure
services.
International Journal of Low-Carbon Technologies,
Journal Year:
2025,
Volume and Issue:
20, P. 590 - 604
Published: Jan. 1, 2025
Abstract
The
hybrid
AI-based
battery
management
system
(HAI-BMS)
is
proposed
to
solve
the
complex
problem
of
electric
vehicle
(EV)
management.
It
combines
conventional
manipulation
processes
with
system-gaining
knowledge
neural
networks
and
reinforcement
learning
algorithms.
This
simulation
showcases
capability
BMS
transform
electric-powered
transportation
by
demonstrating
substantial
improvements
performance,
lifespan,
average
efficiency.
By
incorporating
AI
techniques
into
BMSs
automobiles,
HAI-BMS
paving
manner
for
future
options
that
are
sensible,
bendy,
eco-friendly.
Energies,
Journal Year:
2024,
Volume and Issue:
17(16), P. 4032 - 4032
Published: Aug. 14, 2024
Electric
mobility
is
a
sustainable
alternative
for
mitigating
carbon
emissions
by
replacing
the
conventional
fleet.
However,
low
availability
of
data
from
charging
stations
makes
planning
energy
systems
integration
electric
vehicles
(EVs)
difficult.
Given
this,
this
work
focuses
on
developing
an
adaptive
computational
tool
simulation,
considering
many
EVs
and
patterns.
Technical
specifications
are
considered
such
as
battery
capacity,
driving
range,
time,
standard
each
EV,
Different
simulations
analyses
weekly
load
profiles
carried
out,
portraying
characteristics
different
challenges
that
system
planners
expect.
The
research
results
denote
importance
manufacturers
models
in
composition
aggregate
profile
patterns
region.
developed
model
can
be
adapted
to
any
system,
expanded
with
new
EVs,
scaled
supporting
areas.
Frontiers in Energy Research,
Journal Year:
2024,
Volume and Issue:
12
Published: Feb. 21, 2024
The
surging
demand
for
electricity,
fueled
by
environmental
concerns,
economic
considerations,
and
the
integration
of
distributed
energy
resources,
underscores
need
innovative
approaches
to
smart
home
management.
This
research
introduces
a
novel
optimization
algorithm
that
leverages
electric
vehicles
(EVs)
as
integral
components,
addressing
intricate
dynamics
household
load
study’s
significance
lies
in
optimizing
consumption,
reducing
costs,
enhancing
power
grid
reliability.
Three
distinct
modes
management
are
investigated,
ranging
from
no
outages,
with
focus
on
time-of-use
(ToU)
tariff
impact,
inclining
block
rate
(IBR)
pricing,
combined
effect
ToU
IBR
outcomes.
algorithm,
multi-objective
approach,
minimizes
peak
optimizes
cost
factors,
resulting
7.9%
reduction
integrated
payment
costs.
Notably,
EVs
play
pivotal
role
planning,
showcasing
16.4%
loads
decrease
expenses.
Numerical
results
affirm
algorithm’s
adaptability,
even
under
interruptions,
preventing
excessive
increases
paid
Incorporating
dynamic
pricing
structures
like
rates
alongside
time
use
reveals
costs
loads.
In
conclusion,
this
provides
robust
framework
management,
demonstrating
benefits,
potential,
enhanced
reliability
through
strategic
EV
pricing.
