3,647 research outputs found
A bidirectional power charging control strategy for Plug-in Hybrid Electric Vehicles
© 2019 by the authors. Plug-in Hybrid Electric Vehicles (PHEVs) have the potential of providing frequency regulation due to the adjustment of power charging. Based on the stochastic nature of the daily mileage and the arrival and departure time of Electric Vehicles (EVs), a precise bidirectional charging control strategy of plug-in hybrid electric vehicles by considering the State of Charge (SoC) of the batteries and simultaneous voltage and frequency regulation is presented in this paper. The proposed strategy can control the batteries charge which are connected to the grid, and simultaneously regulate the voltage and frequency of the power grid during the charging time based on the available power when different events occur over a 24-h period. The simulation results prove the validity of the proposed control strategy in coordinating plug-in hybrid electric vehicles aggregations and its significant contribution to the peak reduction, as well as power quality improvement. The case study in this paper consists of detailed models of Distributed Energy Resources (DERs), diesel generator and wind farm, a generic aggregation of EVs with various charging profiles, and different loads. The test system is simulated and analyzed in MATLAB/SIMULINK software
Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility
According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put âintelligenceâ into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies
New trends in electrical vehicle powertrains
The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft
Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle
As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model
Planning The Velocity of a Parallel Hybrid Electric in Vehicle-to-vehicle Autonomous Driving: an Optimization-based Approach
Improved numerical tools are required to foster flexible and effective advancement of innovative electrified and highly automated road vehicles. This paper proposes an optimization-based approach to off-line plan the longitudinal velocity of a hybrid electric vehicle (HEV) when travelling as Ego vehicle in a vehicle-to-vehicle (V2V) autonomous driving scenario. A parallel P2 hybrid powertrain layout is retained along with the corresponding on-board supervisory controller. A mathematical formulation for the optimal V2V autonomous driving control problem is provided and consequently solved with an optimization method based on dynamic programming (DP). The implemented DP formulation particularly exploits information about the overall longitudinal speed profile of a Lead vehicle in a predefined driving mission to determine the velocity profile of the Ego vehicle. Optimization constraints involve maintaining the inter-vehicular distance value within allowed limits while aiming at minimizing both the magnitude of Ego vehicle acceleration events and the overall Ego vehicle fuel consumption as predicted according to the on-board hybrid supervisory control logic. Simulation results for different driving missions demonstrate that, using the proposed DP formulation, the Ego vehicle can achieve both smoother speed profiles and improved fuel economy by some percentage points in V2V autonomous driving compared to the retained Lead vehicle embedding the same HEV powertrain layout
New Perspectives on Electric Vehicles
Modern transportation systems have adverse effects on the climate, emitting greenhouse gases and polluting the air. As such, new modes of non-polluting transportation, including electric vehicles and plug-in hybrids, are a major focus of current research and development. This book explores the future of transportation. It is divided into four sections: âElectric Vehicles Infrastructures,â âArchitectures of the Electric Vehicles,â âTechnologies of the Electric Vehicles,â and âPropulsion Systems.â The chapter authors share their research experience regarding the main barriers in electric vehicle implementation, their thoughts on electric vehicle modelling and control, and network communication challenges
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-Ââfunctional
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-Ââtypes
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
Heavy-Duty Vehicles Modeling and Factors Impacting Fuel Consumption.
A conventional heavy-duty truck PSAT model was validated and incorporated into the Powertrain System Analysis Toolkit (PSAT). The truck that was modeled was a conventional over-the-road 1996 Peterbilt tractor, equipped with a 550 hp Caterpillar 3406E non exhaust gas circulation (EGR) engine and an 18-speed Roadranger manual transmission. A vehicle model was developed, along with the model validation processes. In the engine model, an oxides of nitrogen (NOx) emissions model and a fuel rate map for the Caterpillar 3406E engine were created based on test data. In the gearbox model, a shifting strategy was specified and transmission efficiency lookup tables were developed based on the losses information gathered from the manufacturer. As the largest mechanical accessory model, an engine cooling fan model, which estimates fan power demand, was integrated into the heavy-duty truck model. Experimental test data and PSAT simulation results pertaining to engine fuel rate, engine torque, engine speed, engine power and NOx were within 5% relative error. A quantitative study was conducted by analyzing the impacts of various parameters (vehicle weights, coefficients of rolling resistance and the aerodynamic drag) on fuel consumption (FC) for the Peterbilt truck. The vehicle was simulated over five cycles which represent typical vehicle in-use behavior. Three contributions were generated. First, contour figures provided a convenient way to estimate fuel economy (FE) of the Peterbilt truck over various cycles by interpolating within the parameter values. Second, simulation results revealed that, depending on the circumstances and the cycle, it may be more cost effective to reduce one parameter value (such as coefficient of aerodynamic drag) to increase FE, or it may be more beneficial to reduce another (such as the coefficient of rolling resistance). Third, the amount of the energy consumed by auxiliary loads was found to be highly dependent upon the driving cycles. The ratios between average auxiliary power and average engine power were found to be 71.0%, 17.1%, 15.3%, 12.4% and 11.43% for creep, transient, UDDS, cruise and HHDDT_s cycles, respectively. A hybrid electric bus (HEB) also was modeled. The HEB that was modeled was a New Flyer bus with ISE hybrid system, a Cummins ISB 260H engine and a single-reduction transmission. Information and data were acquired to describe all major components of the HEB. The engine model was validated prior to modeling of the whole vehicle model. The load-following control strategy was utilized in the energy management system. Experimental data and PSAT simulated results were compared over four driving schedules, and the relative percent of errors of the FC, FE, CO2 and NOx were all within 5% except for the FE and NOx of the Manhattan cycle, which were 6.93% and 7.13%, respectively. The high fidelity of this model makes it possible to evaluate the FE and NOx emissions of series hybrid buses for subsequent PSAT users
Autonomous Multi-Chemistry Secondary-Use Battery Energy Storage
Battery energy storage is poised to play an increasingly important role in the modern electric grid. Not only does it provide the ability to change the time-of-day and magnitude of energy produced by renewable resources like wind and solar, it can also provide a host of other 3ancillary grid-stabilizing services. Cost remains a limiting factor in deploying energy storage systems large enough to provide these services on the scale required by an electric utility provider. Secondary-use electric vehicle batteries are a source of inexpensive energy storage materials that are not yet ready for the landfill but cannot operate in vehicles any longer. However, the wide range of manufacturers using different battery chemistries and configurations mean that integrating these batteries into a large-format system can be difficult. This work demonstrates methods for the autonomous integration and operation of a wide range of stationary energy storage battery chemistries. A fully autonomous battery characterization is paired with a novel system architecture and transactive optimization to create a system which can provide utility-scale energy services using a multitude of battery chemistries in the same system. These claims are verified using a combination of in-situ testing and a computer modelling testbed. Results are presented which demonstrate the ability of the system to combine a wide range of batteries into an effective single system
Komponentenbasierte dynamische Modellierung von Energiesystemen und Energiemanagement-Strategien fĂŒr ein intelligentes Stromnetz im Heimbereich
The motivation of this work is to present an energy cost reduction concept in a home area power network (HAPN) with intelligent generation and flexible load demands. This study endeavors to address the energy management system (EMS) and layout-design challenges faced by HAPN through a systematic design approach. The growing demand for electricity has become a significant burden on traditional power networks, prompting power engineers to seek ways to improve their efficiency. One such solution is to integrate dispersed generation sources, such as photovoltaic (PV) and storage systems, with an appropriate control mechanism at the distribution level. In recent years, there has been a significant increase in interest in the installation of PV-Battery systems, due to their potential to reduce carbon emissions and lower energy costs. This research proposes an optimal economic power dispatch strategy using Model Predictive Control (MPC) to enhance the overall performance of HAPN. A hybrid AC/DC microgrid concept is proposed to address the control choices made by the appliance scheduling and hybrid switching approaches based on a linear programming optimization framework. The suggested optimization criteria improve consumer satisfaction, minimize grid disconnections, and lower overall energy costs by deploying inexpensive clean energy generation and control. Various examples from actual case study demonstrate the use of the established EMS and design methodology.Die Motivation dieser Arbeit besteht darin, ein Konzept zur Senkung der Energiekosten in einem Heimnetzwerk (HAPN) mit intelligenter Erzeugung und exiblen Lastanforderungen vorzustellen. Im Rahmen dieser Forschungsarbeit wird ein Entwurf fĂŒr ein HAPN entwickelt, indem das Energiemanagementsystem (EMS) und der Entwurf des Layouts auf der Grundlage des Systemmodells und der betrieblichen Anforderungen gelöst werden. Die steigende Nachfrage nach ElektrizitĂ€t ist fĂŒr traditionelle Stromnetze kostspielig und infrastrukturintensiv. Daher konzentrieren sich Energietechniker darauf, die Effizienz der derzeitigen Netze zu erhöhen. Dies kann durch die Integration verteilter Erzeugungsanlagen (z. B. Photovoltaik (PV), Speicher) mit einem geeigneten Kontrollmechanismus fĂŒr das Energiemanagement auf der Verteilungsseite erreicht werden. DarĂŒber hinaus hat das Interesse an der Installation von PV-Batterie-basierten Systemen aufgrund der Reduzierung der CO2-Emissionen und der Senkung der Energiekosten erheblich zugenommen. Es wird eine optimale wirtschaftliche Strategie fĂŒr den Energieeinsatz unter Verwendung einer modellprĂ€diktiven Steuerung (MPC) entwickelt. Es wird zudem ein hybrides AC/DC-Microgrid-Konzept vorgeschlagen, um die Steuerungsentscheidungen, die von den AnsĂ€tzen der GerĂ€teplanung und der hybriden Umschaltung getroffen werden, auf der Grundlage eines linearen Programmierungsoptimierungsrahmens zu berĂŒcksichtigen. Die vorgeschlagenen Optimierungskriterien verbessern die Zufriedenheit der Verbraucher, minimieren Netzabschaltungen und senken die Gesamtenergiekosten durch den Einsatz von kostengĂŒnstiger und sauberer Energieerzeugung. Verschiedene Beispiele aus einer Fallstudie demonstrieren den Einsatz des entwickelten EMS und der Entwurfsmethodik
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