Energy Technology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 25, 2024
The
rapid
growth
of
the
transportation
sector
in
past
few
decades
has
contributed
significantly
to
global
warming
issues,
leading
extensive
research
on
vehicles
having
nearly
zero
or
total
tailpipe
carbon
emissions.
automobiles
within
this
classification
belong
hybrid
electrical
(HEVs),
plug‐in
HEVs,
battery–electric
(BEVs),
fuel‐cell
(FC)
EVs
(FCEVs),
and
FC
HEVs.
FCHEVs
are
powered
by
a
combination
systems,
rechargeable
batteries,
ultracapacitors,
and/or
mechanical
flywheels.
technology
appears
hold
potential
terms
extended
driving
distances
quicker
refueling
times
for
that
emit
no
exhaust
fumes.
A
significant
number
studies
have
examined
various
types
energy‐storage
devices
as
vehicle
power
supply,
their
interfacing
with
drive
mechanism
using
converters
energy
management
strategies
(EMS).
In
article,
EMS
FC‐based
discussed.
Classifications
FCEVs,
BEVs,
EMSs
developed
researchers.
review
report,
it
is
indicated
existing
capable
performing
well,
yet
further
required
better
reliability
intelligence
toward
achieving
greater
fuel
efficiency
lifetime
upcoming
FCHEVs.
Energy and AI,
Journal Year:
2024,
Volume and Issue:
17, P. 100392 - 100392
Published: July 14, 2024
Trucks
consume
a
lot
of
energy.
Hybrid
technology
maintains
long
range
while
realizing
energy
savings.
is
therefore
an
effective
energy-saving
for
trucks.
Recovery
engine
waste
heat
through
the
organic
Rankine
cycle
further
enhances
efficiency
and
provides
thermal
management.
However,
powertrain
greatly
increases
complexity
management
system.
In
order
to
design
system
with
high
robustness,
this
study
proposes
deep
reinforcement
learning
embedded
rule-based
This
method
optimises
key
parameters
by
inserting
into
it.
Therefore,
scheme
combines
good
optimization
effect
excellent
robustness
rule.
verify
feasibility
scheme,
builds
dynamic
model
carries
out
simulation
study.
Subsequently,
hybrid
semi
physical
experimental
bench
was
constructed
rapid
control
prototype
carried
out.
The
results
show
that
can
reduce
consumption
4.31
%
compared
under
C-WTVC
driving
cycle.
addition,
saving
safe
operation
also
be
achieved
other
unfamiliar
untrained
cycles.
shows
has
agreement
in
experiment
simulation,
which
demonstrates
potential
real
vehicle
engineering
applications
promotes
application
learning.
