355 research outputs found
A Study on the Integration of a High-Speed Flywheel as an Energy Storage Device in Hybrid Vehicles
The last couple of decades have seen the rise of the hybrid electric vehicle as a compromise between the outstanding specific energy of petrol fuels and its low-cost technology, and the zero tail-gate emissions of the electric vehicle. Despite this, considerable reductions in cost and further increases in fuel economy are needed for their widespread adoption.
An alternative low-cost energy storage technology for vehicles is the high-speed flywheel. The flywheel has important limitations that exclude it from being used as a primary energy source for vehicles, but its power characteristics and low-cost materials make it a powerful complement to a vehicle's primary propulsion system. This thesis presents an analysis on the integration of a high-speed flywheel for use as a secondary energy storage device in hybrid vehicles.
Unlike other energy storage technologies, the energy content of the flywheel has a direct impact on the velocity of transmission. This presents an important challenge, as it means that the flywheel must be able to rotate at a speed independent of the vehicle's velocity and therefore it must be coupled via a variable speed transmission. This thesis presents some practical ways in which to accomplish this in conventional road vehicles, namely with the use of a variator, a planetary gear set or with the use of a power-split continuously variable transmission. Fundamental analyses on the kinematic behaviour of these transmissions particularly as they pertain to flywheel powertrains are presented. Computer simulations were carried out to compare the performance of various transmissions, and the models developed are presented as well.
Finally the thesis also contains an investigation on the driving and road conditions that have the most beneficial effect on hybrid vehicle performance, with a particular emphasis on the effect that the road topography has on fuel economy and the significance of this
Definition and verification of a set of reusable reference architectures for hybrid vehicle development
Current
concerns
regarding
climate
change
and
energy
security
have
resulted
in
an
increasing
demand
for
low
carbon
vehicles,
including:
more
efficient
internal
combustion
engine
vehicles,
alternative
fuel
vehicles,
electric
vehicles
and
hybrid
vehicles.
Unlike
traditional
internal
combustion
engine
vehicles
and
electric
vehicles,
hybrid
vehicles
contain
a
minimum
of
two
energy
storage
systems.
These
are
required
to
deliver
power
through
a
complex
powertrain
which
must
combine
these
power
flows
electrically
or
mechanically
(or
both),
before
torque
can
be
delivered
to
the
wheel.
Three
distinct
types
of
hybrid
vehicles
exist,
series
hybrids,
parallel
hybrids
and
compound
hybrids.
Each
type
of
hybrid
presents
a
unique
engineering
challenge.
Also,
within
each
hybrid
type
there
exists
a
wide
range
of
configurations
of
components,
in
size
and
type.
The
emergence
of
this
new
family
of
hybrid
vehicles
has
necessitated
a
new
component
to
vehicle
development,
the
Vehicle
Supervisory
Controller
(VSC).
The
VSC
must
determine
and
deliver
driver
torque
demand,
dividing
the
delivery
of
that
demand
from
the
multiple
energy
storage
systems
as
a
function
of
efficiencies
and
capacities.
This
control
component
is
not
commonly
a
standalone
entity
in
traditional
internal
combustion
vehicles
and
therefore
presents
an
opportunity
to
apply
a
systems
engineering
approach
to
hybrid
vehicle
systems
and
VSC
control
system
development.
A
key
non-‐functional
requirement
in
systems
engineering
is
reusability.
A
common
method
for
maximising
system
reusability
is
a
Reference
Architecture
(RA).
This
is
an
abstraction
of
the
minimum
set
of
shared
system
features
(structure,
functions,
interactions
and
behaviour)
that
can
be
applied
to
a
number
of
similar
but
distinct
system
deployments.
It
is
argued
that
the
employment
of
RAs
in
hybrid
vehicle
development
would
reduce
VSC
development
time
and
cost.
This
Thesis
expands
this
research
to
determine
if
one
RA
is
extendable
to
all
hybrid
vehicle
types
and
combines
the
scientific
method
with
the
scenario
testing
method
to
verify
the
reusability
of
RAs
by
demonstration.
A
set
of
hypotheses
are
posed:
Can
one
RA
represent
all
hybrid
types?
If
not,
can
a
minimum
number
of
RAs
be
defined
which
represents
all
hybrid
types?
These
hypotheses
are
tested
by
a
set
of
scenarios.
The
RA
is
used
as
a
template
for
a
vehicle
deployment
(a
scenario),
which
is
then
tested
numerically,
thereby
verifying
that
the
RA
is
valid
for
this
type
of
vehicle.
This
Thesis
determines
that
two
RAs
are
required
to
represent
the
three
hybrid
vehicle
types.
One
RA
is
needed
for
series
hybrids,
and
the
second
RA
covers
parallel
and
compound
hybrids.
This
is
done
at
a
level
of
abstraction
which
is
high
enough
to
avoid
system
specific
features
but
low
enough
to
incorporate
detailed
control
functionality.
One
series
hybrid
is
deployed
using
the
series
RA
into
simulation,
hardware
and
onto
a
vehicle
for
testing.
This
verifies
that
the
series
RA
is
valid
for
this
type
of
vehicle.
The
parallel
RA
is
used
to
develop
two
sub-‐types
of
parallel
hybrids
and
one
compound
hybrid.
This
research
has
been
conducted
with
industrial
partners
who
value,
and
are
employing,
the
findings
of
this
research
in
their
hybrid
vehicle
development
programs
Control of a hybrid electric vehicle with predictive journey estimation
Battery energy management plays a crucial role in fuel economy improvement of
charge-sustaining parallel hybrid electric vehicles. Currently available control strategies
consider battery state of charge (SOC) and driver’s request through the pedal input in
decision-making. This method does not achieve an optimal performance for saving fuel
or maintaining appropriate SOC level, especially during the operation in extreme
driving conditions or hilly terrain. The objective of this thesis is to develop a control
algorithm using forthcoming traffic condition and road elevation, which could be fed
from navigation systems. This would enable the controller to predict potential of
regenerative charging to capture cost-free energy and intentionally depleting battery
energy to assist an engine at high power demand.
The starting point for this research is the modelling of a small sport-utility vehicle by
the analysis of the vehicles currently available in the market. The result of the analysis
is used in order to establish a generic mild hybrid powertrain model, which is
subsequently examined to compare the performance of controllers. A baseline is
established with a conventional powertrain equipped with a spark ignition direct
injection engine and a continuously variable transmission. Hybridisation of this vehicle
with an integrated starter alternator and a traditional rule-based control strategy is
presented. Parameter optimisation in four standard driving cycles is explained, followed
by a detailed energy flow analysis.
An additional potential improvement is presented by dynamic programming (DP),
which shows a benefit of a predictive control. Based on these results, a predictive
control algorithm using fuzzy logic is introduced. The main tools of the controller
design are the DP, adaptive-network-based fuzzy inference system with subtractive
clustering and design of experiment. Using a quasi-static backward simulation model,
the performance of the controller is compared with the result from the instantaneous
control and the DP. The focus is fuel saving and SOC control at the end of journeys,
especially in aggressive driving conditions and a hilly road. The controller shows a
good potential to improve fuel economy and tight SOC control in long journey and hilly
terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap
between the baseline controller and the DP. However, there is little benefit in short trips
and flat road. It is caused by the low improvement margin of the mild hybrid powertrain
and the limited future journey information.
To provide a further step to implementation, a software-in-the-loop simulation model is
developed. A fully dynamic model of the powertrain and the control algorithm are
implemented in AMESim-Simulink co-simulation environment. This shows small
deterioration of the control performance by driver’s pedal action, powertrain dynamics
and limited computational precision on the controller performance
Electric Vehicle Efficient Power and Propulsion Systems
Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countries’ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles
Preliminary design of a hybrid electric powertrain for a earthmoving machine
The goal of this work is to evaluate the benefit of the hybridization of a Compact Wheel Loader (CWL) and to put into evidence the effect of the component size on its performance. To do this, a mathematical model has been developed using a backward approach, i.e. starting from the power request on a typical duty cycle made available by an industrial partner. The goals for the choice of the hybridization architecture were: minimizing fuel consumption, ensuring the simplicity of driveline and power management and ensuring compatibility with the vehicle structure.. A reduction up to 14% of fuel consumption was estimated in this investigation by combining engine downsizing with the usage of a Continuous Variable Transmission together with an optimization of the battery capacity and voltage
Intelligent energy management agent for a parallel hybrid vehicle
This dissertation proposes an Intelligent Energy Management Agent (IEMA) for parallel hybrid vehicles. A key concept adopted in the development of an IEMA is based on
the premise that driving environment would affect fuel consumption and pollutant emissions, as well as the operating modes of the vehicle and the driver behavior do. IEMA incorporates a driving
situation identification component whose role is to assess the driving environment, the driving style of the driver, and the operating mode (and trend) of the vehicle using long and short
term statistical features of the drive cycle.
This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power
split strategy, which is shown to lead to improved fuel economy and reduced emissions
Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle
This paper presents a nonlinear-model based hybrid optimal control technique
to compute a suboptimal power-split strategy for power/energy management in a
parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained
as model predictive control solution to the power management control problem
(PMCP) of the PHEV, i.e., to decide upon the power distribution among the
internal combustion engine, an electric drive, and other subsystems. A
hierarchical control structure of the hybrid vehicle, i.e., supervisory level
and local or subsystem level is assumed in this study. The PMCP consists of a
dynamical nonlinear model, and a performance index, both of which are
formulated for power flows at the supervisory level. The model is described as
a bi-modal switched system, consistent with the operating mode of the electric
ED. The performance index prescribing the desired behavior penalizes vehicle
tracking errors, fuel consumption, and frictional losses, as well as sustaining
the battery state of charge (SOC). The power-split strategy is obtained by
first creating the embedded optimal control problem (EOCP) from the original
bi-modal switched system model with the performance index. Direct collocation
is applied to transform the problem into a nonlinear programming problem. A
nonlinear predictive control technique (NMPC) in conjunction with a sequential
quadratic programming solver is used to compute suboptimal numerical solutions
to the PMCP. Methods for approximating the numerical solution to the EOCP with
trajectories of the original bi-modal PHEV are also presented in this paper.
The usefulness of the approach is illustrated via simulation results on several
case studies
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