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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 50, NO. 4, JULY/AUGUST 2014 2323
Optimal Charging of Plug-in Electric Vehicles for a
Car-Park Infrastructure
Tan Ma, Student Member, IEEE, and Osama A. Mohammed, Fellow, IEEE
Abstract—This paper proposes an intelligent workplace park-
ing garage for plug-in hybrid electric vehicles (PHEVs). The
system involves a developed smart power charging controller, a
75-kW photovoltaic (PV) panel, a dc distribution bus, and an ac
utility grid. Stochastic models of the power that is demanded by
PHEVs in the parking garage and the output power of the PV
panel are presented. In order to limit the impact of the PHEVs’
charging on the utility ac grid, a fuzzy logic power-flow controller
was designed. Based on their power requirements, the PHEVs
were classified into five charging priorities with different rates
according to the developed controller. The charging rates depend
on the predicted PV output power, the power demand by the
PHEVs, and the price of the energy from the utility grid. The
developed system can dramatically limit the impact of PHEVs
on the utility grid and reduce the charging cost. The system
structure and the developed PHEV smart charging algorithm are
described. Moreover, a comparison between the impact of the
charging process of the PHEVs on the grid with and without the
developed smart charging technique is presented and analyzed.
Index Terms—Charging priority levels, fuzzy logic, hybrid dc
distribution system, impact limitation, plug-in hybrid electric
vehicles (PHEVs), solar energy.
I. INTRODUCTION
PLUG-IN hybrid electric vehicles (PHEVs) are gaining
popularity due to several reasons, i.e., they are convenient,
visually appealing, quiet, and produce less pollution in the
environment. PHEVs have the potential to reduce fossil energy
consumption and greenhouse gas emissions, and they increase
the penetration of sustainable energy sources, such as solar
energy and wind energy, into our daily lives [1]–[3]. Further-
more, most personal vehicles in the USA are parked more than
95% of the day and generally follow the same daily schedule
[4]. Therefore, PHEVs can be used as mobile energy-storage
devices in the future. More than 75% of drivers in the USA
travel less than 45 mi in their daily commute, and since many
of today’s PHEVs can go up to 100 mi on a single charge, their
implementation can be widespread. Battery technology contin-
Manuscript received February 11, 2013; revised October 28, 2013;
accepted December 2, 2013. Date of publication January 2, 2014; date of
current version July 15, 2014. Paper 2013-IACC-110.R1, presented at the 2012
IEEE Industry Applications Society Annual Meeting, Las Vegas, NV, USA,
October 7–11, and approved for publication in the IEE E T RANSACTIONS ON
INDUSTRY APPLICATIONS by the Industrial Automation and Control Commit-
tee of the IEEE Industry Applications Society. This work was supported by
grants from the Office of Naval Research and the U.S. Department of Energy.
The authors are with the Energy Systems Research Laboratory, Depart-
ment of Electrical and Computer Engineering, College of Engineering and
Computing, Florida International University, Miami, FL 33174 USA (e-mail:
tma004@fiu.edu; mohammed@fiu.edu).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIA.2013.2296620
ues to advance, with batteries becoming smaller in size while
storing more energy. It is forecasted that in North America,
PHEVs will be on the roads in large numbers in the very near
future [5].
The increasing number of PHEVs can have a huge impact
on the electric utility if properly designed smart charging tech-
niques are not utilized. Uncoordinated and random charging
activities could greatly stress the distribution system, caus-
ing several kinds of technical and economic issues, such as
suboptimal generation dispatches, huge voltage fluctuations,
degraded system efficiency and economy, and increasing the
likelihood of blackouts because of network overloads. In order
to maximize the usage of renewable-energy sources and limit
the impact of the PHEVs’ charging to the utility ac grid, a
smart power-flow charging algorithm and controller should be
designed. Moreover, accurate forecasting models of the pho-
tovoltaic (PV) output power and PHEVs’ power requirement
should be built. PHEVs need to participate in vehicle-to-grid
(V2G) and vehicle-to-vehicle (V2V) power transactions during
the charging process. Fully controlled bidirectional ac–dc and
dc–dc converters are needed in this system.
In [6] and [7], load management solutions for coordinating
the charging process of multiple PHEVs in a smart grid system
based on a real-time minimization of the total cost of generating
the energy and the associated grid energy losses were proposed
and developed. However, they did not consider the inclusion
of a renewable-energy source in the system, which holds the
implementation of these algorithms back, since the concept
of PHEVs involves obtaining the power to charge them from
renewable-energy sources. In addition, the control strategy did
consider a charging priority level, but the level is based on how
much the owner of the PHEV is willing to pay and not the state
of charge (SOC) of the PHEV’s batteries; therefore, the effi-
ciency of the V2V service and that of the V2G service are low.
In [8] and [9], an intelligent method for scheduling the use
of available energy-storage capacity from PHEVs is proposed.
The batteries in these PHEVs can either provide power to the
grid or take power from the grid to charge the batteries on the
vehicles. However, the detail about the energy dispatch during
charging and the V2G process is not given. Moreover, the SOCs
of the PHEV’s batteries are not considered during the process.
A fully controlled bidirectional ac–dc converter has been
designed and implemented in [10]. This converter has the
capability of controlling the power flow between the ac and dc
sides of the systems in both directions while operating at the
unity power factor and within the acceptable limits of the time
harmonic distortion for the current that is drawn from the grid.
Hence, the amount of power that is flowing in either direction
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2324 IEEE T RAN SAC TIO NS ON I NDUSTRY APPLICATIONS, VOL. 50, NO. 4,JULY/AUGUST 2014
TAB L E I
PARAMETERS FOR PHEVs OF DIFFERENT SIZES
can be set to a certain preset value while a controlled rectifier,
which is working as a voltage rectifier, maintains the power
balance, as it is free to supply any power that is needed in the dc
grid. In addition, a controlled dc–dc boost converter and a bidi-
rectional dc–dc converter are proposed and tested in [11]–[13].
In this paper, a workplace parking-garage charging system
of hybrid dc PHEVs is established and tested. A 318-V grid-
connected dc power distribution network that is combined with
the PV and PHEV parking garage is designed. Accurate PV and
PHEV power stochastic models that are based on a statistical
theory are studied. A fuzzy logic power-flow controller is also
designed.
This paper is organized as follows. The system description
and problem formulations are given in Section II. The stochastic
models of the PHEV’s parking system and the PV panel are
given in Section III. The details of the developed real-time
fuzzy logic power-flow controller are given in Section IV. The
method to classify the PHEVs into five priority levels and to
adjust their charging rates is given in Section V. Results and
discussion are given in Section VI. Concluding remarks are
provided in Section VII.
II. SYSTEM DESCRIPTION AND PROBLEM FORMULATION
Consider a workplace parking-garage hybrid dc power sys-
tem that is equipped with a PV farm. Each workday, various
vehicles will park in the garage during their owner’s working
hours. The vehicles all differ in size, battery capacity, and
energy consumption per mile. The specific details are shown in
Table I. Whenever a PHEV is connected to the parking garage,
its owner will set the departure time, and the system will make
a record of this time. Usually, at the departure time, the SOC of
the batteries is expected to be at least 80% of its full capacity. In
order to take battery protection into consideration, the SOC of
the PHEV’s battery should not go below a certain limit. If this
limitation is reached, the PHEV will stop using electric energy
and will begin consuming gas from its combustion engine.
The schematic of the system under study is shown in Fig. 1.
As shown, the PHEVs, with their bidirectional dc–dc chargers,
and the PV source, with its dc–dc regulating interface, share
a common dc bus. Hence, the parking-garage charging system
acts as a dc microgrid that has the ability to send or receive
power from the utility ac grid. The amount of power that is
transferred between the ac and dc sides is determined accord-
Fig. 1. Schematic of the investigated system.
Fig. 2. Bidirectional converter response to a step change in the dc reference
from −4to1A,withdcidc (4 A/div, 10 ms), dc voltage vdc(1000 V/div,
10 ms), ac phase voltage ea(30V/div,10ms),andacia(5 A/div, 10 ms).
ing to the decisions from the developed energy management
algorithm. Fig. 2 shows the response of this converter to a step
change in the dc reference from −4 to 1 A, and the current will
reverse its direction, sending power from the dc microgrid to the
ac side; therefore, it can receive power. Moreover, the active and
reactive power flow is separately controlled by using the active
and reactive power decoupling technique. More simulation and
experimental results on this converter, as well as the controlled
rectifier, were illustrated in [1] and [2].
In order to limit the impact of the PHEVs’ charging to the
utility ac grid while letting the PHEVs participate in the V2V
and V2G power transactions, the parking garage should have
a smart charging algorithm that can adjust the charging rates
for PHEVs under different utility ac energy prices Eprice and
different power-flow estimations Pgrid. Since the hourly energy
price is assumed to be known beforehand, it is essential to
estimate Pgrid, which is given by
Pgrid =PPV −Ptotal −ˆ
Pupcoming (1)
MA AN D MOH AM MED: OPTI MAL CHARGI NG O F PLU G-IN E LEC TR IC VE HIC LE S FO R CAR-PAR K INF RASTRU CT URE 2325
where
PPV estimated PV output power for the next
period T;
Ptotal power that is needed by the PHEVs that are
already parked in the parking garage;
ˆ
Pupcoming estimated power requirements by the upcoming
PHEVs that will connect to the parking garage
in the next period T.
In order to design the smart charging control algorithm, an
accurate power requirement forecasting model is needed to
estimate Pgrid.
For the power-flow control for the next period T, the charging
rates for different PHEVs should be adjusted based on Eprice
and Pgrid. Because the system is highly nonlinear, a fuzzy logic
controller is a good choice for solving this issue.
Often times, the PHEVs in the parking garage will have
different SOCs and different departure times; therefore, their
average constant power requirements will differ. Some PHEVs
may need a huge amount of energy within a short period
of time. These kinds of PHEVs should be classified into the
high-priority level. Other PHEVs’ SOCs are already high, with
departure times several hours later. These kinds of PHEVs
should be classified into the lower priority level. Therefore, a
priority classification should be designed for the PHEVs.
The objective of this paper is to design a grid-connected
parking-garage charging system of hybrid dc PHEVs with
fuzzy logic power-flow controller and PV panels. The goal is
to limit the impact of the PHEV’s charging to the utility ac grid
and to maximize the utilization of the power that is generated
from the PV panels.
III. MODELING THE STOCHASTIC PHEVs’ PARKING
SYSTEM
A. PV Output Power Forecasting Model
In order to manage the energy in the PHEVs’ parking garage
in a real-time manner, the power that is available from the PV
source should be predicted and considered. The accuracy of
the decision that is made by an algorithm is affected by the
accuracy of the predictive models that are used to emulate the
uncertainties in the system, i.e., the PV power in this case.
Hence, we count on real data to forecast the PV output power.
The data forecasting process was based on the PV data that are
collected over 15 years on an hourly basis for an example PV
system in the State of Texas, USA. The output power data are
used as the output to be forecasted, whereas the day of the year
(1–365) and the hour of the day (1–24) were used as inputs.
Different model evaluation indexes were used to validate the
developed mathematical models. The forecasting model that is
used to predict the PV output in this paper is regenerated from
the model that is derived in [14] using the historical PV data
described in the earlier section.
B. PHEVs’ Power Requirement Forecasting Model
In order to develop an accurate PHEV parking system model,
it is essential to estimate the probability density function (PDF,
which is a function that describes the relative likelihood for this
random variable to take on a given value [15]) of the power that
TAB L E I I
DISTRIBUTION PARAMETERS OF THE ARRIVAL AND DEPARTURE TIMES
is needed by each PHEV when it is connected to the parking
lot ˆ
PPHEV. This variable is based on the PHEV models, the
parking duration times, and the daily travel distances.
In order to avoid serious damage, the batteries of the PHEVs
should not be overdischarged. The PHEVs have the capability
of using both electric energy and fossil fuel energy. The PHEV
stops using the electric energy when the SOC of its battery
is below 10%. Therefore, the electric energy of a PHEV that
can be used before its next charge is 70% of the total battery
capacity. If the energy consumption is more than this value,
the PHEV will use gas. If the total energy consumption for a
certain PHEV before the next charge is less than 70% of its
battery capacity, the energy that it will need for the next charge
is M×Em. Otherwise, the energy that it needs is 70% of its
battery capacity. The constant charging power that is needed by
this PHEV is given in (2) and (3). In order to find ˆ
PPHEV,the
distribution of the daily travel distance and the daily parking
duration time should be obtained first.
If the total energy consumption is less than 70% of the battery
capacity, we have
ˆ
PPHEV =Md×Em
Dt−At
.(2)
If the total energy consumption is equal to or more than 70%
of the battery capacity, we have
ˆ
PPHEV =70% ×Bc
Dt−At
(3)
where
Mddriver’s daily travel distance;
AtPHEV’s arrival time;
DtPHEV’s departure time;
EmPHEV’s energy consumption per mile;
BcPHEV’s battery capacity.
In this paper, the parking garage is located by the workplace
of a company whose office hours are from 9:00 A.M.to
6:00 P.M. Based on the central limit theorem (the conditions
under which the mean of a sufficiently large number of indepen-
dent random variables, each with finite mean and variance, will
be approximately normally distributed [16]), the distribution of
the PHEVs’ arrival and departure times is shown in Table II.
With the PDFs of Atand Dt, the joint PDF of Dt−Atcan be
found, which is the daily parking duration time. It is a normally
distributed random variable with μd=8.99 and σd=1.92.The
PDF of the daily parking duration is shown in Fig. 3.
2326 IEEE T RAN SAC TIO NS ON I NDUSTRY APPLICATIONS, VOL. 50, NO. 4,JULY/AUGUST 2014
Fig. 3. PDF of the daily parking duration.
Based on known driving pattern statistics, the average yearly
total miles driven in the USA is 12 000 mi, with 50% of drivers
driving 25 mi per day or less and 80% of drivers driving
40 mi or less. A log normal distribution with μm=3.37 and
σm=0.5is selected to approximate the PDF of Md, which
shows that the total yearly driving distance average is 12 018 mi,
with 48% of the vehicles driven 25 mi or less each day and
83% of the vehicles driven 45 mi or less each day, which
closely approximates the driving performance results from the
work in [1]. The distribution function for Mdis given in
fX(x;μm,σ
m)= 1
xσm√2πexp −(ln x−μm)2
2σ2
m.(4)
With the PDF of the daily duration time, the PDF of the
daily travel distance, and the power consumption of each class
of PHEVs, by using MATLAB’s statistic distribution fitting
toolbox and the Monte Carlo simulation with 30 000 samples,
the PDF of the constant power that is needed by each PHEV
when it is connected to the parking lot ˆ
PPHEV is finally found
as an inverse Gaussian distribution, with μp=1.573 and λp=
3.652. The distribution function for ˆ
PPHEV is given in the
following, and the PDFs of Mdand ˆ
PPHEV are shown in
Figs. 4 and 5, respectively,
fX(x, μp,λ
p)=λp
2πx3exp −λp
2μ2
px(x−μp)2.(5)
After getting the PDF of ˆ
PPHEV, the forecasting model of
the power that is needed by the PHEVs in the parking system is
built. Together with the forecasting model of the power that is
generated by the renewable-energy sources and the hourly price
of the energy from the utility grid, a real-time smart parking
system is established. For instance, at a certain time t, the SOC
of the PHEVs that are already parked in the parking lot and their
power requirements are already known. In order to forecast the
power that is needed by the PHEVs that will arrive during the
upcoming period T, the following can be used:
ˆ
Pupcoming =
t+T
t
fAt(x, μAt,σ
At)dt×NP ׈
PPHEV_avg (6)
Fig. 4. PDF of the daily travel distance.
Fig. 5. Power that is needed by each PHEV when connected to the parking
garage.
where
NP total number of PHEVs that will park in
the parking lot this day;
fAt(x, μAt,σ
At)PDF of arriving time At;
ˆ
PPHEV_avg average constant power requirement for
all PHEVs when they are connected to
the parking lot. ˆ
PPHEV_avg can be cal-
culated from the PDF of ˆ
PPHEV.
IV. REAL-TIME FUZZY LOGIC POWER-FLOW
CONTROLLER
In the earlier section, the details of how to build the model
of the parking garage and how to find the PDF of ˆ
PPHEV are
given. Together with the stochastic model of the PV panel and
the hourly energy price of the ac utility grid, a smart charging
algorithm with a fuzzy logic power-flow controller is designed.
The flowchart is shown in Fig. 6.
The charging rates of the PHEVs at different priority levels
for the next period vary based on the forecasting of the power
that is generated by the PV panel, the forecasting of the power
that is needed by the upcoming PHEVs, the price of the utility
energy grid, and the power that is needed by the current PHEVs.
Without the V2V and V2G services, the power flow in the next
period between the utility ac grid and the hybrid parking system
can be calculated by using (1).
MA AN D MOH AM MED: OPTI MAL CHARGI NG O F PLU G-IN E LEC TR IC VE HIC LE S FO R CAR-PAR K INF RASTRU CT URE 2327
Fig. 6. Flowchart of the developed real-time fuzzy logic charging controller.
TABLE III
FUZZY LOGIC RULES
The price of energy for the next period Eprice and the
forecasting power flow for the next period Pgrid are used as the
two inputs of the real-time Mamdani-type fuzzy logic power-
flow controller to determine the charging index δp, which will
determine the charging rates of the PHEVs at different priority
levels. The power flow between the utility ac grid and the
dc system Pgrid is described as “negative,” “positive small,”
“positive medium,” “positive,” and “positive big.” Similarly,
energy price Eprice is described as “very cheap,” “cheap,”
“normal,” “expensive,” and “very expensive.” The method that
is implemented for the defuzzification is centroid based. Within
the model, the minimum and the maximum are used for the
AND and OR operators, respectively. The output of the fuzzy
controller is index δp, which is used for adjusting the charging
rates for the PHEVs in different priority levels. Parameter δP
is described as “NB,” “N,” “Z,” “P,” and “PB,” which stand
for negative big, negative, zero, positive, and positive big,
respectively. The Mamdani-type model-based fuzzy rules of
the fuzzy logic power-flow controller is given in Table III. The
membership functions of Eprice,Pgrid , and δp, and the surface
of the fuzzy logic controller’s rules are shown in Figs. 7 and 8,
respectively.
With the charging index δp, which varies from −1to1,the
charging rates for the PHEVs in different priority levels will be
obtained.
Fig. 7. Membership functions. (a) Power flow. (b) Energy price. (c) Power-
flow control index.
Fig. 8. Surface of the fuzzy logic controller’s rules.
V. C LASSIFICATION OF PHEVs INTO FIVE
PRIORITY LEVELS
The charging rates of different PHEVs with different SOCs
and power requirements should be apparently charged at differ-
ent rates. For example, a PHEV is connected to the parking lot
at 9:00 A.M. with a departure time of 6:00 P.M., and the SOC is
65%. The average constant power that is required by this PHEV
is small. At the same time, another PHEV is also connected to
the parking lot at 9:00 A.M. but will leave at 10:30 A.M., and
the SOC is only 10%. The average constant power requirement
of this PHEV is larger than that of the previous PHEV, which
means that its charging condition is also more emergent. There-
fore, in order to reduce the impact of the PHEVs’ charging to
the utility ac grid, at a certain time, different PHEVs should be
2328 IEEE T RAN SAC TIO NS ON I NDUSTRY APPLICATIONS, VOL. 50, NO. 4,JULY/AUGUST 2014
TAB L E I V
CHARGING RATES FOR DIFFERENT CHARGING LEVELS
charged at different rates. Furthermore, since the former PHEV
will stay in the parking lot for more than 8 h, it can be viewed
as an energy-storage device during this period. For instance, at
a certain time, the energy price is below the daily average price,
and the PV panel generates more power than the total PHEV’s
requirements. The extra power can be saved in this PHEV as
backup energy. By doing so, the priority level of this PHEV
decreases. At another time during this period, the price of
the utility grid energy could be high, and the power that is
generated by the PV panel cannot meet the total load and the
PHEV’s power requirement; therefore, instead of buying power
with a high price from the utility grid, the parking system
can get the backup energy from this PHEV. By doing so, the
priority of this PHEV will increase. During the entire day, all
the PHEV’s priorities are varying with their SOCs; thus, energy
can be delivered between the V2G and V2V services. The five
charging priorities are shown in Table IV.
The PHEV’s charging priority levels are only dependent on
their power requirements. Furthermore, because of the bidirec-
tional power-flow converter, the PHEVs can be charged and
discharged; therefore, their charging priority levels are varying
with time. The PHEVs in levels 1, 2, and 3 can be only charged.
Those PHEVs either need a lot of energy (such as having an
SOC of only 10% when connected to the parking station) or will
leave in a short time but still have not met the owner’s charging
requirement (such as having an SOC of only 65% and departing
in 0.5 h). The PHEVs in levels 4 and 5 can be discharged to
fulfill the V2G and V2V services. These PHEVs will continue
staying in the parking lot for a longer duration. The various
SOCs of the PHEVs will change over time; thus, the PHEVs in
the lower priority levels can jump to the higher priority levels
and vice versa.
With the charging index δp, the charging rates of the PHEVs
in levels 1–5, respectively, are given in the following:
pcharging_rate =12 (7)
pcharging_rate =9+3×δp(8)
pcharging_rate =4+4×δp(9)
pcharging_rate =0+5×δp(10)
pcharging_rate =−3+5×δp.(11)
VI. RESULTS AND DISCUSSION
In this section, a 318-V workplace parking-garage hybrid
dc power system that is equipped with a 75-kW PV panel has
350 parking positions, and each workday, around 300 vehicles
will park in the garage during the work hours from 9:00 A.M.to
Fig. 9. Hourly power flow from the ac grid without the optimal controller.
Fig. 10. SOCs of the PHEVs at their departure times without the optimal
controller.
6:00 P.M. Of the 300 vehicles, around 60% of them are PHEVs.
The battery capacities and the energy consumption per mile of
the PHEVs in different sizes are given in Table I. The parking
garage will upgrade all the information every 6 min and will
generate a new charging index δpto adjust the charging rates for
the PHEVs that are parked in it. All the PHEVs are assumed to
be only charged at this workplace parking garage, and the SOC
of the batteries are expected to be over 80% at their departure
times. The SOC of the batteries of the PHEVs should not go
below 10%.
Two experiments are done both in a MATLAB simulation
and a hardware test. The first simulation represents the power
flow between the utility grid and the hybrid dc PHEV park-
ing garage without the real-time charging optimal controller,
and the second simulation contains the real-time fuzzy logic
charging optimal controller. Both experiments are under the
same conditions, i.e., same number and types of PHEVs, same
departure and arrival times, same hourly energy price, and same
power generated by the PV panels.
The simulation of the power flow during daytime and the
SOCs of the PHEVs at their departure times for the parking
garage without an optimal charging method is shown in Figs. 9
and 10, respectively.
Whenever a PHEV is connected to the parking garage, it
will be charged with a constant rate of 10 kW. It will not stop
charging until the SOC of its battery reaches 80%. From the
simulation, it is clear that the peak happens around 9:00 A.M.
MA AN D MOH AM MED: OPTI MAL CHARGI NG O F PLU G-IN E LEC TR IC VE HIC LE S FO R CAR-PAR K INF RASTRU CT URE 2329
Fig. 11. Hourly power flow from the ac grid with the optimal controller.
Fig. 12. SOCs of the PHEVs at their departure times with the optimal
controller.
because most of the PHEVs arrive around this time every day.
The peak is near 700 kW, and the power flow that is above
300 kW lasts from 7:30 A.M. to 11:20 A.M., which is more than
3.5 h. After 1:30 P.M., the charging stops because all the PHEVs
that are parked in the garage at that time already meet the
charging requirement. After 1:30 P.M., there is no power flow
between the utility ac grid and the parking garage because there
are no new PHEVs connected to the parking garage. However,
at that time, the PV output power is still high, whereas the
energy price is cheap. It is not a good time to sell power to the
ac grid, but the parking garage without the optimal charging
controller does not have any other option other than selling
power. In Fig. 10, it is clear that all the SOCs of the PHEVs
are above 80% at their departure times, since all of them are
charged with the same charging rates.
The simulation of the power flow during daytime and the
SOCs of the PHEVs at their departure times for the parking
garage with an optimal fuzzy logic charging controller are
shown in Figs. 11 and 12, respectively. In Fig. 11, it is clear that
the peak of the power flow from the ac utility grid to the smart
parking garage is limited to 300 kW, and the power flow, which
is above 250 kW, only lasts from 9:30 A.M. to 11:20 A.M. and
partly in the afternoon around 4:00 P.M., which is altogether no
more than 2.5 h.
Furthermore, when the energy price is high, the power flow
from the ac side will apparently decrease, which happens
Fig. 13. Variation of the SOC of a PHEV during the charging process.
around 5:00 P.M. In addition, when the PV output power is
above a certain amount, the power flow from the ac grid to the
smart charging garage will decrease because more PHEVs will
be charged by the power that is generated by the PV panel. In
Fig. 10, we can see that all the SOCs of the PHEVs are above
80% at their departure times, which also meets the charging
requirements.
Fig. 13 shows the variation of a randomly chosen SOC of a
PHEV during the charging process with the optimal fuzzy logic
charging controller. This PHEV is connected to the parking
garage at 8:18 A.M., and the departure time is 5:12 P.M. When
this PHEV is connected to the parking garage, the SOC is
around 28%, and the owner of the PHEV enters the departure
time, i.e., 5:30 P.M. Therefore, the charging system can calcu-
late the real-time average power that is required for this PHEV.
The duration time is long at the beginning of the day, which is
from 8:00 A.M. to 10:00 A.M.; therefore, the PHEV’s average
power requirement is low, with a classification of either level 4
or level 5. At this time, the price of energy is high; therefore, in-
stead of buying power from the ac grid, the parking garage uses
the energy that is stored in this PHEV to charge other PHEVs in
the higher levels of priority. This is why the SOC of the PHEV
is decreasing during this period. From 10:00 A.M.to1:30P.M.,
the ac grid energy price is low; therefore, more power is bought
from the ac side, and since δpis positive, this PHEV’s charging
rate is positive. However, the duration time is still long; there-
fore, the priority level is still low, and the charging rate is low.
The priority levels increase at around 2:00 P.M., when its depar-
ture time is near. At this time, the charging rate is higher than
before. This charging rate is kept until 5:12 P.M., when the SOC
is already above 80% and when the departure time is very close.
This PHEV no longer participates in the V2G or V2V power
transactions, and the SOC remains constant from then on.
Fig. 14 shows the comparison of the voltage variation on
the ac bus corresponding to the PHEV’s charging process
with/without an optimal fuzzy logic charging controller. It is
clear that, during the charging process without the optimal
charging controller, the voltage on the ac bus will drop to
around 0.75 p.u. of the rated voltage. Moreover, the voltage that
is below 0.9 p.u. lasts longer than 3 h. With the optimal charging
controller, the voltage of the ac bus during the whole charging
process is above 0.95 p.u.
2330 IEEE T RAN SAC TIO NS ON I NDUSTRY APPLICATIONS, VOL. 50, NO. 4,JULY/AUGUST 2014
Fig. 14. Voltage on the ac bus corresponding to the PHEVs’ charging process
with/without the optimal fuzzy logic charging controller.
VII. CONCLUSION
This paper has presented a model of a PHEV workplace car-
park charging infrastructure with a grid-connected hybrid dc
power system involving renewable-energy sources. To forecast
the power flow in the next period, accurate PHEV and PV power
stochastic models were developed. The fuzzy logic power-flow
controller was designed to control the real-time power flow. A
new power dispatch method that is based on PHEV priority
levels and a real-time PHEV charging algorithm was developed.
Furthermore, bidirectional dc–dc and ac–dc converters were
designed to let the PHEVs participate in the V2V and V2G
services. The simulation results show that the optimal power-
flow control algorithm can maximize the utilization of the PV
output power for the charging of PHEVs and can simultane-
ously decrease the impact on the grid greatly. At the same time,
the SOCs of the PHEVs at their departure times are all above
the charging requirement. The system that has been presented
in this paper benefits both the ac utility grid and the owners of
the PHEVs.
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Tan M a (S’09) received the Bachelor of Engi-
neering degree in automation and the M.Eng. de-
gree in control theory and control engineering
from Huazhong University of Science and Tech-
nology, Wuhan, China, in 2007 and 2009, respec-
tively. He is currently working toward the Ph.D.
degree in electrical engineering at the Energy Sys-
tems Research Laboratory, Department of Electrical
and Computer Engineering, College of Engineer-
ing and Computing, Florida International University,
Miami, FL, USA.
His research interests include power system operation and control, artificial
intelligence applications to power systems, energy conservation and alternative
energy sources, and smart grid power system design and optimization.
Osama A. Mohammed (S’79–M’83–SM’84–F’94)
received the M.S. and Ph.D. degrees in electrical
engineering from Virginia Polytechnic Institute and
State University, Blacksburg, VA, USA, in 1981 and
1983, respectively.
He is currently the Director of the Energy Systems
Research Laboratory, Department of Electrical and
Computer Engineering, College of Engineering and
Computing, Florida International University, Miami,
FL, USA, where he is also currently a Professor. He
is the author or coauthor of more than 350 technical
papers in the archival literature and in national and international conference
records. His research interests include power system analysis and electric
drives, and smart grid applications, including communication, cyber–physical
infrastructure and sensor networks for the distributed control of power grids,
and renewable-energy systems.
Dr. Mohammed is a fellow of the Applied Computational Electromagnetic
Society. He was a Member of the IEEE Power and Energy Society (PES,
formerly the Power Engineering Society) Governing Board from 1992 to 1996
and was the Chair of the IEEE PES Constitution and Bylaws Committee. He
also serves as a Chair, an Officer, and an Active Member on several IEEE
PES committees, subcommittees, and technical working groups. He has chaired
sessions and programs at numerous international conferences, in addition to
delivering numerous invited lectures to members of scientific organizations
around the world. He was the Chair of six major international conferences and
served as the International Steering Committee Chair for the IEEE International
Electric Machines and Drives Conference and the IEEE Biennual Conference
on Electromagnetic Field Computation. He is an Editor of the IE EE TRANSAC-
TIONS ON ENERGY CONVERSION, IEEE TRANSACTIONS ON SMART GRID,
IEEE TRANSACTIONS ON MAGNETICS-CONFERENCES,andThe Interna-
tional Journal for Computation and Mathematics in Electrical and Electronic
Engineering; he is an Associate Editor of the IE EE TRANSACTIONS ON
INDUSTRY APPLICATIONS. He also received many awards for excellence in
research, teaching, and service to the profession. He was a recipient of the 2010
IEEE PES Cyril Veinott Electromechanical Energy Conversion Award.
