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IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014 1969
Rapid-Charging Navigation of Electric Vehicles
Based on Real-Time Power Systems and TrafficData
Qinglai Guo, Senior Member, IEEE, Shujun Xin, Student Member, IEEE, HongbinSun, Senior Member, IEEE,
Zhengshuo Li, Student Member, IEEE, and Boming Zhang, Fellow, IEEE
Abstract—Electric vehicles (EVs) have attracted growing atten-
tion in recent years. However, daily charging of EVs, in particular
rapid charging, may impact power systems, especially during peak
hours, and the effects may occur in different places as trafficcon-
ditions change. To address these issues, we describe an integrated
rapid-charging navigation strategy that considers both the traffic
conditions and the status of the power grid. The system is based on
an intelligent transport system (ITS), and contains four modules:
a power system control center (PSCC), an ITS center, charging
stations, and EV terminals. The PSCC calculates the available
charging capacity and station charging capacity based on power
grid data and transmits the results to the charging stations. The
charging stations determine their charging plans and estimate the
available charging power for future EVs (CPFE), and transmit
these data to the ITS center. After receiving CPFE data and traffic
data from the ITS center, the EV terminal estimates the total
time for charging (TTC) for different stations, which includes the
driving time, waiting time, and charging time. The driver can view
these results and choose to be navigated to the charging station
corresponding to the minimum TTC. The modular design of the
navigation system reduces data transmission, which protects the
drivers’ privacy since they can choose which charging station
to use and are not required to send any data to the ITS system.
Simulation results demonstrate the feasibility of the proposed
method for different working conditions for power system and
traffic conditions.
Index Terms—Charge, distribution network, electric vehicle
(EV), traffic control.
NOMENCLATURE
ACC Available charging capacity.
CPFE Charging power for future EVs.
SCC Station charging capacity.
TTC Total time for charging.
Time interval for load prediction.
Manuscript received September 16, 2013; revised January 05, 2014; accepted
February 20, 2014. Date of publication April 25, 2014; date of current version
June 18, 2014. This work was supported by the National Key Basic Research
Program of China (973 Program) (2013CB228202), the National Science Fund
for Distinguished Young Scholars (51025725), and the Tsinghua University Ini-
tiative Scientific Research Program. Paper no. TSG-00735-2013.
The authors are with the Department of Electrical Engineering, Tsinghua Uni-
versity, Beijing 100084, China (e-mail: shb@mail.tsinghua.edu.cn).
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/TSG.2014.2309961
Three-phase SCC/power/reactive
power/voltage/phase angle of node
between .
Power factor/higher limit of the SCC at
charging station .
Lower/upper power limit of the charging
poles at charging station .
Lower limit/higher limit of voltage of
node .
Three-phase current constraints of the
branch .
CPFE for during .
EV state of charge/driving distance to
charging station at time .
EV energy storage after charging/energy
consumption rate per km.
Energy capacity/minimum energy storage
of the EV battery.
Set of all time windows for the charging
plan.
Driving/waiting/charging/battery
switching time.
I. INTRODUCTION
WITH growing concern about the sustainability of energy
resources and climate change, there has been much
recent interest in electric vehicles (EVs) [1], [2]. EVs are
zero-emission during driving and can be more energy-efficient
than conventional vehicles with combustion engines. Besides,
through proper regulation, EV charging loads could be utilized
to help integrate renewable but intermittent energy sources
for further carbon emission reduction as well [3]–[5]. Though
the EV have not been widely used now, it is expected to be-
come an integral part of the traffic. In the USA, the Obama
administration has embraced a goal of having one million
electric-powered vehicles by 2015 [6]. Recently, a number of
car manufactures, including Nissan and Toyota, have already
developed commercially available electric vehicles [7]–[9].
The necessary infrastructure, including the charging stations
and charging poles, is currently being expanded in a number of
countries across the world.
However, as the number of EVs grows, the heavy and
unpredictable loads due to charging may cause prob-
lems for the power system, such as thermal overloads and
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1970 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014
under-voltages [10]–[12]. In order to mitigate these effects,
studies have been carried out focusing on optimized charging
methods, including controlling the charging duration and
rate [13]–[16], as well as using dynamic pricing to manage the
time distribution of the load [17]–[21]. In most of these studies,
the goals have been to shift the load or to ensure the reliability
of large-scale power systems. By assuming that the charging
power can be fully adjusted, and the charging rate can be slowed
down, these optimization methods have mainly been applied
to residential charging, especially during the night when large
numbers of EVs are plugged in. The charging facility can be
viewed as an ordinary residential electric device and it can be
connected directly to low voltage distribution system.
Besides slow charging method, rapid charging method should
also be considered. Slow charging method takes 6–8 hours,
mostly for long-term parked EVs, while the rapid charging
method supplies higher power, which can reach 50–100 kW [22],
so that charging occurs over 15 minutes to 2 hours. However,
it requires specific charging facilities and mostly takes place
in commercial charging stations [23]. Because of the shorter
charging time, some EV drivers may choose this method to
continue their driving soon. It is especially applicable to those
drivers who consume large amounts of energy and have a
long-distance trip, such as taxi and shuttle drivers.
Obviously, different from wide-spreading and low-power res-
idential charging, rapid charging loads in charging stations are
much more concentrated and heavy. Because of its high power
level, rapid charging stations should be connected to medium
voltage distribution system in three-phase rather than to low
voltage distribution system in single-phase [22], [24]. In China,
most rapid charging stations are connected to 10-kV feeder in
three-phase [25].
Therefore, analyzing impact of rapid charging load should
also take power system structure into consideration rather than a
simple load curve [23]–[27]. Besides, as rapid charging station
operation is affected by traffic a lot, both planning [28], [29]
and load analyzing [30]–[32] of rapid charging station should
consider traffic factor. In [32], a mathematical model of the EV
charging demand for a rapid-charging station was reported and
used to analyze the expected charging loads at different exits of a
highway. This model used fluid dynamics to describe the arrival
rate of EVs to aid the forecasting of demand and construction
planning of charging stations.
When operating a rapid-charging station, it is necessary not
only to analyze the additional demand due to EV charging, but
also to develop a strategy to mitigate the impact on the grid and
maximize the available rapid-charging power during operation.
In contrast to the adjustable slow-charging method, rapid
charging offers high power immediately following the start of
charging, so that the controlling strategies for slow-charging
method cannot employ temporal optimization. In this case,
with considering EV drivers’ subjectivities, spatial optimiza-
tion may be a more valuable approach. There are two aspects
to the optimization of EV charging considered here. First, the
driver should reach the charging station as soon as possible;
second, the load should have minimal impact on the operation
of the power grid.
Now, GPS-based navigation systems are installed in many
vehicles, which are employed to navigate the vehicle to a cer-
tain destination and can be utilized for EV charging navigation.
Some applications are even able to account for real-time traffic
information to select routes that avoid traffic congestion, such
as Baidu Map [33], which also has a phone version, and it
offers application programming interface (API) for software
developing [34]. However, such navigation cannot consider
the power system information or constraints. Sometimes,
distribution system may be partially overloaded because of
unexpectable reasons, such as residential air conditioning
load in an extremely hot day, or sudden change of distributed
generation’s (DG’s) power output. Under these scenarios, if
toomanyEVsareguidedtoacharging station and the feeder
this charging station connected to has already been overloaded,
this local overload may become more serious. Besides, com-
pared with other power loads, spatial distribution of EV rapid
charging load is easier and more applicable to adjust to help
alleviate local overload of the power system, thus ensure
safety of the power system. Therefore, it could be beneficial to
consider the spatial distribution of the load due to EV charging
when providing route information to drivers.
A navigation strategy considering power grid operation that
revises the traffic distance to an electrical distance has been
reported previously [35], where the basic architecture of the
charging and navigation system was introduced. However, it is
difficult to transform the power system information into a dis-
tance term, and the system might not be exploitedbyEVdrivers,
who may choose to disregard the proposed routes because the
electrical distance makes little sense to them. Here, we report a
follow-up study, where we aim to build a more integrated nav-
igation system and put forward a more practical and efficient
navigation strategy.
There are two major challenges that must be addressed to im-
plement such an integrated navigation system. The first is finding
a suitable method of transforming the demand on the operation
of the power grid into the navigation of EVs. In this paper, traffic
and power grid information are unified into a “time” term, and the
drivers are expected to choose the option that is the least time con-
suming, where this time includes the driving time, waiting time,
and charging time. The second challenge is reducing the informa-
tion exchange as well as protecting the drivers’ privacy during
navigation. To solve these problems, an integrated architecture
was designed in a modular fashion. Each module performs cal-
culations locally, reducing the data exchange required between
modules compared with a centralized calculation method. The
EV terminal can complete the calculations and determine the best
route based on broadcast data about the status of the power grid
and the trafficsystem.Noda
ta about the EV will be sent to the
server, which ensures the privacy of the driver.
The remainder of this paper is organized as follows. Section II
introduces the intelligent transport system (ITS) as well as the
architecture of the integrated charging guide system. Section III
describes the functions and strategies of each system module
as well as the data transmission between modules, and presents
a minimum total time for charging (TTC) strategy, then dis-
cusses how this charging navigation method helps reduce in-
formation transmission and protect privacy. Section IV presents
a simulation example to assess the performance of the naviga-
tion strategy. Section V concludes with a summary of the study
findings.
GUO et al.: RAPID-CHARGING NAVIGATION OF EVs BASED ON REAL-TIME POWER SYSTEMS 1971
Fig. 1. Working process of the integrated charging controlling system.
II. ARCHITECTURE
To effectively apply a navigation strategy that considers the
location of charging stations, a comprehensive system that com-
bines the power grid data and traffic data is required. The power
grid data may be obtained from a network control center, and the
trafficdatamaybeobta
ined from an intelligent transport system
(ITS).
A. Brief Introduction of ITS
An ITS is a transport management system where the goal is
to reduce traffic congestion by optimizing the routing of vehi-
cles. It integrates advanced information, data communication,
electronic sensor, and electronic control technologies [36]–[40].
Now many countries are actively developing this technology,
including the USA and China [41], [42].
There are many applications of ITS technologies, of which
the most widely-used one is vehicle navigation system. In
order to implement location and navigation, vehicle navigation
system includes geographic information systems (GIS), global
positioning system (GPS), image monitoring, and wireless
communication technology [43], [44]. Besides, vehicle navi-
gation system is able to obtain real-time trafficdatawithother
assistive tools, such as Traffic Message Channel (TMC) [45]
and Vehicle Information and Communication System (VICS)
[46]. These tools allow delivery of dynamic traffic information
to vehicle terminals through conventional FM radio broad-
casts without interrupting normal broadcast services [47]. The
real-time traffic data could be utilized to improve navigation by
avoid traffic congestion [48].
B. Architecture Design
Adriv
er of an EV first considers whether the state of charge
(SOC)oftheEVissufficient for the desired journey. If not, they
will consider how to recharge the EV as quickly as possible.
An integrated charging navigation system should be able to re-
spond to the charging demand of the EV and navigate the EV
to a charging station afterwards, minimizing the overall journey
time.
To ensure reliability of the power system, if a feeder has
a high load level, the available power at a charging station
connected to this feeder should be limited. Therefore, properly
distributingchargingloadtodifferent rapid-charging stations
should be required and beneficial. To perform these two func-
tions, the proposed system contains four modules: a power
system control center (PSCC), an ITS center, charging stations,
and EV terminals. The system structure is shown in Fig. 1.
The PSCC evaluates the stability of the power system and
determines SCC of each charging station. The charging station
further optimizes the charging power of each pole based on SCC
and estimates charging power for future EVs (CPFE). The ITS
center receives data from both charging stations and trafficnet-
work, then releases them to the public. The EV terminal calcu-
lates TTC for each charging station after receiving information
released by the ITS center, then displays the result to the driver
and shows the journey data after the driver determines where to
charge.
During operation, information about the grid, charging sta-
tions, traffic, and EV is used, and most information is analyzed
where it is gathered, so data transmission is reduced as much
as possible. In this system, the PSCC and ITS centers do not
require data about the EV to make decisions, and EV drivers
decide where to charge by comparing the different times for
different options. EV drivers are not required to upload infor-
mation such as the SOC or spatial location.
III. METHODS
A. PSCC Strategy
Charging stations are distributed at different feeders of the
distribution system. The maximum charging power of each
charging station should be limited for the safety of the power
system. Define the maximum charging power of each station as
the station charging capacity (SCC).
The maximum permissible charging load of all rapid-
charging stations, defined as the available charging capacity
(ACC), can be calculated through summing SCCs. ACC could
represent charging capacity of the whole system.
System controllers hope to maximize ACC without harming
the safety of power system. Therefore, PSCC needs to optimize
ACC for the system and SCC for each charging station.
As mentioned before, because of high power level, rapid
charging station is connected to medium voltage distribution
system in three-phase. With AC/DC converter, charging sta-
tion could adjust charging load on each phase. Therefore, the
optimization of ACC and SCC should consider three-phase
distribution system model. The SCC as well as the ACC during
1972 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014
, can be calculated by assuming the slack
bus of the system is the root node, whose process is shown as
follows:
(1)
where the operator represents Hardmard product of two
matrices.
In formula (1), and represent three-phase ac-
tive power and reactive power of node during
obtained from conventional load demand prediction, where
is the time interval for load prediction. and are real and
imaginary parts of the three-phase nodal admittance matrix.
and are real and imaginary parts of the three-phase branch
admittance matrix. represents the set of nodes consisting of
rapid-charging stations.
For a certain charging station , its SCC during
is represented by , which equals to the sum of
SCC on three phases ,and .Ifthesta-
tion has identical charging poles, the total charging power of
whichisintherange ,then .
Following these calculations, the PSCC will distribute the
SCC to each station. The data structure is shown as follows:
location of the charging station
SCC time window
SCC value
SCC limit on each phase
(2)
B. Charging Station Strategy
After receiving the SCC reference value from the PSCC, the
charging station must optimize the available power for each
charging pole, design a charging plan, and predict the charging
demand in the future. The charging station then sends its oper-
ating data to the ITS center, which is then released to the public.
1) Power Optimization for Charging Poles: The charging
station must optimize the available power at each charging pole
after receiving the SCC data. During this process, four factors
should be considered.
a) To ensure fairness, the SCC should be equally distributed
to each operating charging machine.
b) To maximize the number of EVs that can be charged, the
number of working charging poles should be maximized.
c) To ensure reliability, the charging power of each charging
pole at charging station should be in the range .
d) Charging of the EVs should not be interrupted or termi-
nated early.
When optimizing charging power, b is the optimization ob-
jective, and a, c, and d are the constraints. The optimization re-
sults will remain constant unless an EV finishes charging or the
SCC changes. The optimization is as follows:
(3)
where is the time window of the optimization, which begins
from the current time and ends when an EV finishes charging or
the SCC changes; is the number of EVs currently charging
at station ;and is the total number of EVs.
The charging power at each charging pole can be obtained
from
(4)
During the optimization, the upper limit of is unknown, so
constraint (3.4) cannot be considered at first. It will instead be
used to verify and revise the results.
Without considering constraint (3.2), the optimization is the
theoretical maximum number of charging EVs, recorded as
.If is greater than , no EVs are waiting to charge
GUO et al.: RAPID-CHARGING NAVIGATION OF EVs BASED ON REAL-TIME POWER SYSTEMS 1973
so that if another EV reaches charging station at that instant
in time, it can be charged immediately.
2) Charging Plan Designing: If the SCCs in the following
time windows are already known, the charging plan of all EVs
in the station can be obtained through the following process.
a) Optimize the charging rates for the current time (see
Section III-B-1).
b) Recalculate the SOC of all EVs at charging station at the
end of .
c) Set the current time to .Gotostepa)and
continue optimization
Through this process, charging plans for all EVs at the station
can be calculated. Assuming that the time windows for each op-
timization are ,
can be regarded as the total time for the charging plan. In
the absence of new EVs arriving at the charging station, the
charging plan does not change.
3) Estimating the Charging Power for Future EVs (CPFE):
Let the current and theoretical maximum number of charging
EVs be and , and let the charging power of each pole
during be . If an EV arrives at the charging
station during , the immediate charging power should be
if
else (5)
where satisfies .
The CPFE at station can be represented by
. It should be recalculated only when the
charging plan changes.
4) Data Transmission to the ITS Center: For each charging
station, only and the CPFE data must be uploaded to the
ITS center. These must be recalculated and uploaded when the
charging plan changes. The data structure is as follows:
location of the charging station
(6)
C. ITS Center
The main task of the ITS center is to collect the CPFE from
each charging station and transmit it to each EV. The ITS center
also gathers real-time traffic data and transmit this to the EV
terminals for navigation purposes. The center does not require
any information from the EVs.
D. EV Terminal and Minimum TTC Strategy
The time to recharge EVs should be minimized, so the navi-
gation should minimize the TTC, which starts at the current time
and ends when the EV is fully charged. There are three steps for
the driver to follow to recharge their EV: driving to a charging
station; waiting for charging, if necessary; and recharging the
EV.
The EV terminal is part of the ITS architecture, so it can re-
ceive real-time traffic data for route planning. The terminal may
also receive charging station information from the ITS center,
and may estimate the time until charging is complete using each
charging station. Let the current time be . The TTC for station
can be estimated as follows:
(7)
where is the driving time to station ,is the waiting
time at station ,and is the charging time of station .
1) Driving Time :is the estimated minimum
driving time from the current location to charging station .With
considering real-time SOC and remaining EV battery energy for
driving, the calculation process of driving time is pre-
sented below:
a) Calculate the EV’s maximum driving distance with the
remaining energy through the formula below:
(8)
where represents energy capacity of the battery,
represents the minimum energy storage of the battery,
represents energy consumption per unit driving
distance, and represents EV’s SOC at time .
b) Search for all driving routes from current location to the
charging station and select those which are shorter than
. If all driving routes are longer than , it means
that the EV cannot reach the charging station with the
remaining battery energy. In this case, navigate the EV to
this charging station is inapplicable, so there is no need to
calculate TTC for this charging station. If there are some
routes shorter than , go to the next step to calculate
.
c) Estimate driving time corresponding to each route based
on the real-time geographic and traffic information and
choose the minimum one as . Set the distance of
this route as and store path information of the route
for future navigation.
Since the energy consumption rate, real-time SOC, battery
capacity, and driving route are all known, the energy state when
the EV reaches charging station ,, can be calculated as
follows:
(9)
2) Waiting Time :is the waiting time after the
EV arrives at charging station , which can be calculated from
(10)
where is the starting time of time window .
If a given EV can be charged at rapid-charging station im-
mediately after arriving, it does not need to wait, and .
Otherwise, it must wait.
Only when the CPFE of the charging station is greater than 0
can a newly arriving EV be charged. The waiting time is
.
1974 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014
3) Charging Time :Based on CPFE data received
from the ITS center, the charging energy at a given time interval
can be estimated from
(11)
where is the start time and is the end time for charging
The charging time is the solution to the following
equation:
(12)
in which represents energy capacity of the battery and
can be calculated through the formula as follows:
(13)
After calculating TTCs for all reachable charging stations, the
EV terminal will choose the charging station corresponding to
the minimum TTC as destination and display navigation infor-
mation to the EV driver.
E. Battery Switching Station
Besides directly charging, battery switching is another way to
supply energy for EVs [49]. In China, battery switching facili-
ties and stations are also expanded in many cities by State Grid
Corporation of China [50]. Compared with normal residential
charging method, battery switching takes less time and can only
be finished by specialized exchanging terminals [49], [51]–[53],
which is quite similar to rapid charging method. Based on these
similarities, we regard the battery switching method as a spe-
cial kind of charging, and bring it into our charging navigation
strategy to enlarge its application range.
However, different from rapid charging station, the optimiza-
tion inside the battery switching station could be simplified be-
cause the switching time is nearly the same for each battery. Be-
sides, switched battery packets are coordinated charged and dis-
tributed by the State Grid Corporation [50], which indicates that
all switched battery packets may be charged in a certain place
other than in the battery switching stations. Therefore, battery
switching station can be viewed as a simple battery transfer sta-
tion without calculating its impact to the power grid.
The CPFE information for battery exchanging stations sent
to the ITS center is defined as follows:
location of the battery exchaging station
the time after which a newly arriving EV
can exchange its battery immediately
battery switching time
(14)
In this case, the TTC for battery swapping station can be
calculated from
(15)
In (15), is the driving time to station , whose calcu-
lation process is shown in Section III-D1); is the battery
switching time, which can be obtained from CPFE information
(14); is the waiting time at station calculated through the
following equation:
(16)
F. System Overview and Privacy Protection
After estimating the TTC for each rapid charging/battery
switching station, the driver can view these results from the
terminal display and choose the best charging station and the
corresponding route. As an EV driver may prefer to recharge
their EV as soon as possible, they will tend to choose the station
that corresponds to the minimum TTC rather than the minimum
distance.
In this integrated charging navigation system, estimation and
calculation are carried out locally, and only limited data need to
be transmitted between modules.
Besides, each EV terminal is able to estimate TTC and plan
driving route locally and independently using broadcast infor-
mation of CPFE and traffic network. During calculation, the EV
terminal transforms traffic and power grid information into time
term for charging navigation without uploading any EV data to
the upper center. Therefore, EV drivers’ privacies are ensured.
In addition to traffic information, only CPFEs of rapid
charging stations (battery switching stations) are transmitted
[see data structures (2), (6) and (14)]. As the number of charging
station is limited, data transmission is not challenging.
IV. SIMULATION AND CASE STUDY
In our previous work, we focused on a simulation method
for the operation of EVs, where a hybrid simulation model was
described [59]. As the movement of the EVs will affect the
traffic and charging will affect the power system, this simula-
tion should consider both traffic data and power data.
A. Simulation Model
1) TrafficSystem: Traffic data includes information on the
transport network, including the length of the route and traffic
speeds. However, compared with a real transport network, only
a limited set of traffic data is required for the calculation, so
simplification of the transport network is required first.
We considered a 15 15 km region of a city-center road
network, containing four rapid-charging stations. The transport
network and corresponding topological graph are shown in
Fig. 2. Four rapid-charging stations (labeled CS1 to CS4) were
located at transport nodes (T-nodes) 18, 31, 29, and 21.
Let the distance from T-node to T-node be , the original
average traffic speed from T-node to T-node be ,andthe
number of vehicles (including EVs and conventional vehicles)
in the trafficflow from T-node to T-node be . The speed
of the trafficflow is then
(17)
where is the correction function of the traffic speed
according to the distribution of vehicles on the roads.
2) Distribution System: We considered the three-phase IEEE
33-node distribution system, as shown Fig. 3. The load curve at
96 points, which includes only conventional load demands, is
GUO et al.: RAPID-CHARGING NAVIGATION OF EVs BASED ON REAL-TIME POWER SYSTEMS 1975
Fig. 2. Transportation network and its topological graph.
Fig. 3. Topological structure of IEEE-33 nodes standard distribution system.
Fig. 4. 96 points’ curve of other conventional loads of the system.
shown in Fig. 4. These data are from one day’s operation data
of a substation in Beijing, China.
Four rapid-charging stations are connected to the system in
three-phase, respectively at nodes (D-nodes) 17, 19, 23, and 29,
which aremarkedwithCS1toCS4inFig.3.Theparametersof
the charging stations and EVs are listed in Section 0.
3) EV Model: Many studies have adopted specifictravel
datasets, such as the National Household Travel Survey
(NHTS) [54], [55] and the National Travel Survey (NTS) [56],
to sketch EV load models. In our simulation, EV model is built
based on NHTS dataset to ensure that the simulation is consis-
tent with the travel patterns of EV owners. NHTS includes the
historical travel information of 150 147 households, including
the number of daily trips, and the start and end times, distance,
and purpose of each trip [57].
However, two points need to be noted in this simulation. First,
only rapid charging demand and its distribution in the simula-
tion area should be addressed. However, residential charging ex-
ists in our simulation. EVs will be plugged in to charge imme-
diately after reaching destination through residential charging
method. During simulation, only when an EV’s energy storage
cannot cover its current trip (from a certain origin to a certain
destination) the EV needs to be charged in the rapid-charging
station. Second, and most important, what we concerned about
is rapid EV charging in the simulated area located in the city
center, where traffic and power load vary a lot during the day,
while residential areas are located in other places around the
city, so EVs may not always run in the simulated area. For ex-
ample, vehicles may enter or go through city center in the day-
time and leave in the evening. Because of these two points, after
generating background data structure, it is necessary to add spa-
tial data to the origin dataset, whose process is presented as
follows:
a) Set detailed origin and destination of each daily trip ac-
cording to their types based on Beijing City Master Plan
(2004–2020) [58].
b) Estimate detailed driving route of each vehicle trip
through BaiduMap API [34]. If a driving route passes the
simulated area, set Passing State of trip structure as 1,
otherwise set it as 0.
c) If the Passing State of a driving route is 1, locate and
record the places where the car starts at/enters and stops
at/leaves the simulated area in the topological graph.
d) Estimate the time when the car starts at/enters the simu-
lated area based on trip departure time and results from
step b) and c).
e) Randomly set 3% vehicles as EVs.
f) Select vehicles which pass the simulated area during the
day (at least one trip’s Passing State is 1). These vehicles
will be simulated in the case.
g) Add 2% shuttles and 5% taxis to our EV model.
After generating EV model, we can obtain an EV database
for simulation with the data structure in Fig. 5:
4) Parameter Settings and Assumptions: Each rapid-
charging station contained 12 charging machines, each with a
capacity of 60 kW. The power factor was 0.96. The capacity
of each EV was 60 kWh, and the energy consumption was
1976 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014
Fig. 5. Data structure of the EV database.
Fig. 6. ACC at different time.
assumed to be 0.18 kWh/km (these data apply to the Tesla
Model S [7]). During simulation, the EV would be plugged in
to charge at 5 kW after finishing a certain trip until the next trip
took place, and only when an EV’s energy storage was less than
40% and cannot afford its current trip the EV would choose to
be charged in the rapid-charging station.
The initial average traffic speed at each road was set to 50
km/h. The correction function for the traffic speed was
when
when
when
when
(18)
5) Traffic Simulation: The detailed simulation method is pre-
sented as follows:
a) Set the simulation parameters and navigation and
charging strategy.
b) Utilize known load curves to calculate the ACC and SCC.
c) Generate detailed data about the EV based on NHTS and
other known data.
d) Calculate the locations of each EV and the SOC. If an EV
requires charging, calculate the optimum route according
to the navigation and charging strategy.
e) Analyze the trafficflow on each road and the state of each
charging station. If the SCC changes or a new EV arrives
at a charging station, redesign the charging plan and re-
calculate the CPFE.
f) Record data at the current time and move the simulation
forward one time-step.
g) If the time reaches the end time, stop the simulation or go
back to step d) to continue.
Fig. 7. Average traffic speed at different time.
Fig. 8. ACC and SCCs at different time.
Fig. 9. Comparison of two charging guide strategies.
B. Simulation Results
The simulation considered 1 425 EVs and 36724 conven-
tional vehicles. The estimated TTC and simulated TTC were
obtained when 286 EVs were charged between 6:00 to 21:00.
The simulated data are shown in Figs. 6–10.
1) ACC and SCC: The ACC over one day is shown in Fig. 6.
The time window was 15 minutes. The figure shows that the
ACC changed during the daytime as other loads increased, and
stayed at maximum value between 21:45 and 10:00 the next day.
In other words, all charging stations could provide maximum
power during that time, while the available power was limited
between 10:00 and 22:00.
The average traffic speed is shown in Fig. 7. The trafficcon-
ditions changed significantly during the daytime hours between
GUO et al.: RAPID-CHARGING NAVIGATION OF EVs BASED ON REAL-TIME POWER SYSTEMS 1977
Fig. 10. Traffic condition at different time. (a) Traffic condition at 7:30. (b) Traffic condition at 12:30. (c) Traffic condition at 17:30. (d) Traffic condition at
different time.
6:00 and 22:00. To better simulate and analyze the charging
system and strategy, the simulation time was set between 6:00
and 22:00.
The SCC of each charging station between 6:00 and 22:00
was calculated as shown in Fig. 8. The SCC of each charging
station decreased as the power load increased. However, the ex-
tent of the fluctuations of different charging stations differed.
The SCC of CS1 and CS4 decreased more than that of CS2 and
CS3 at peak hours. CS1 and CS4 were at the end of feeders,
whileCS2andCS3wereclosertotheroot.Topreventtrans-
mission overload and voltage dips, the SCC of CS1 and CS4
should be reduced more during peak hours.
2) ACC and the Minimum TTC Strategy: In this section, the
minimum TTC strategy is compared with another strategy, i.e.,
simply going to the nearest charging station, which is widely
used in driving navigation
All the TTCs of the EVs were obtained from the simulation
results, so the average TTC at different times could be calcu-
lated. Comparison of the average TTC of each charging guide
strategy at different times is shown in Fig. 9.
Figs. 7–9 show that the TTC was affected by both the traffic
conditions as well as ACC and SCCs. The traffic conditions at
four typical times are shown in Fig. 10. From 6:00 to 10:00,
the power load was light and trafficflow was heavy and all of
the charging stations were able to provide full power. During
this time, nearly all EVs could be recharged when they reached
achargingstation. However, the trafficflowsweremorecon-
gested at this time, and the minimum TTC strategy may help
reduce the driving time for EVs because it assists drivers in
choosing the best charging station and best routes. For example,
in Fig. 10(a), driving from T-node 2 directly to T-node 18 was
slower than driving from T-node 2, to T-node 3, and then to
T-nodes 19 and 18, even though this latter route was more than
twice as far.
Between 10:00 and 16:00, the power load on the grid in-
creased, and the trafficflow decreased. As shown in Fig. 8,
during this time, the SCCs of CS1 and CS4 decreased consider-
ably, while those of the two other charging stations remained al-
most constant. Therefore, the difference in the average TTC was
mainly caused by differences in the waiting time. The strategy of
Fig. 11. Error condition of estimated TTCs.
going to the nearest charging station led to an excessive number
of EVs waiting at CS1 and CS4, with some EVs waiting for
over an hour before charging. In this case, CS2 and CS3 did not
operate at full charging power. As traffic conditions improved
[see Fig. 10(b)], driving to CS2 or CS3 required less time than
waiting at CS1 or CS4. Because the minimum TTC strategy con-
siders the SCCs of different charging stations and traffic data, it
can be effectiveinreducingtheTTC.
From 16:00 to 20:00, the power load was heavy and the traffic
was slow. During this time, the SCC of CS1 and CS4 was sig-
nificantly less than that of CS2 and CS3. The TTCs of both
strategies were significantly longer than during other periods.
However, Fig. 9 shows that the minimum TTC strategy reduced
the average TTC by approximately 25% because it helps reduce
both driving time and waiting time. After 20:00, the power load
decreased and the traffic became faster. During this time, nearly
all EVs could be charged quickly with both strategies because
all stations were able to provide sufficient power and the number
of EVs requiring power was small.
3) Accuracy of Estimated TTC: The difference between the
estimated and simulated TTCs was calculated to determine the
1978 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 4, JULY 2014
error in the estimated values. This is shown in Fig. 11. Most of
the errors were less than 10 minutes, and all of the errors were
less than 15 minutes.
V. CONCLUSION
This paper presents a smart management method for rapid
charging with consideration of spatial load distribution, which
is an essential supplement for current residential charging con-
trolling method through adjusting time and duration.
During charging navigation, both traffic data and power
system data are considered and they are unified into time term
by EV terminal for destination choosing. With considering its
actual performance, the presented charging navigation system is
built on module design. Each module transforms vast complex
original data into simplified results and transmits them, which
reduces data transmission of the system as well as calculation
of upper control center. Furthermore, with this design, the
information released by ITS center is easy to process by EV
terminal. Therefore, the EV terminal could calculate TTC of
each reachable charging station with the broadcast information.
Besides, the presented rapid charging navigationmethodalso
considersthedemandsaswellasprivacy of EV drivers. First of
all, the index for charging navigation is minimum TTC, which
meets drivers’ needs especially during daytime. Besides, the EV
terminal is able to estimate TTC locally using broadcast infor-
mation without uploading any EV data to the upper center, so
EV drivers’ privacies are ensured.
In summary, the presented charging navigation method could
satisfy drivers’ demands with ensuring the security of the power
grid, in which both electric and trafficfactors are considered.
This method is feasible through existing ITS techniques. Fur-
ther researches will be focused on application of the presented
method.
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Qinglai Guo (SM’14) received the B.S. degree from
the Department of Electrical Engineering, Tsinghua
University,Beijing,China,in2000andthePh.D.de-
gree from Tsinghua University in 2005.
His now an Associate Professor at Tsinghua
University. His special fields of interest include
EMS advanced applications, especially automatic
voltage control and V2G.
Shujun Xin (S’13) received the Bachelor degree
from the Department of Electrical Engineering,
Tsinghua University, Beijing, China, in 2012. He is
currently pursuing the Master degree in the Depart-
ment of Electrical Engineering, Tsinghua University.
His research interests include electric vehicles’
smart charging and V2G technology.
Hongbin Sun (SM’12) received the double B.S. de-
grees from Tsinghua University, Beijing, China, in
1992 and the Ph.D. degree from the Department of
Electrical Engineering, Tsinghua University in 1997.
He is now Changjiang Chair Professor of Educa-
tion Ministry of China, Full Professor of electrical
engineering in Tsinghua University and Assistant
Director of State Key Laboratory of Power Systems
in China. From 2007 to 2008, he was a Visiting
Professor with School of Electrical Engineering and
Computer Science, Washington State University,
Pullman, WA, USA. In the last 15 years, he has developed a commercial
system-wide automatic voltage control systems which has been applied to
more than 20 large-scale power grids in China. He published more than 200
academic papers. He held more than 20 patents in China.
Prof. Sun is an IET Fellow, members of IEEE PES CAMS Cascading Failure
Task Force and CIGRE C2.13 Task Force on Voltage/Var support in System
Operations. He won the China National Technology Innovation Award for his
contribution on successful development and applications of New Generation of
EMS for Power Systems in 2008, the National Distinguished Teacher Award
in China for his contribution on power engineering education in 2009, and the
National Science Fund for Distinguished Young Scholars of China for his con-
tribution on power system operation and control in 2010.
Zhengshuo Li (S’12) received the Bachelor degree
from the Department of Electrical Engineering, Ts-
inghua University, Beijing, China, in 2011. He is cur-
rently pursuing the Ph.D. degree in the Department of
Electrical Engineering, Tsinghua University.
His research interests include electric vehicle
smart charging and V2G technology.
Boming Zhang (SM’95–F’10) received the Ph.D.
degree in electrical engineering from Tsinghua
University, Beijing, China, in 1985.
Since 1985, he has been with the Electrical
Engineering Department, Tsinghua University, for
teaching and research and promoted to a Professor
in 1993. His interest is in power system analysis and
control, especially in the EMS advanced applications
in the Electric Power Control Center (EPCC). He
has published more than 300 academic papers and
implemented more than 60 EMS/DTS systems in
China.
Prof. . Zhang is now a steering member of CIGRE China State Committee
and of the International Workshop of EPCC.
