Download Modeling of Electric Vehicles (EVs) for EV Grid Integration

Modeling of Electric Vehicles (EVs) for EV
Grid Integration Study
Qiuwei Wu, Arne H. Nielsen, Jacob Østergaard, Seung Tae Cha, Francesco Marra
and Peter B. Andersen
Center for Electric Technology, Technical University of Denmark, Elektrovej 325,
Kgs. Lyngby, DK – 2800, +45 4525 3529, [email protected]
Abstract— In order to successfully integrate EVs into power systems, it is
necessary to develop a detailed EV model considering both the EV users’
driving requirements and the battery charging and discharging characteristics.
A generic EV model was proposed which takes into account charging and
discharging characteristics of EV batteries, the driving distance per trip and
the availability of EVs for charging and providing grid service. The charging
and discharging characteristics of EV batteries were used to determine the
upper and lower limits of the state of charge (SOC) of EV batteries and to
calculate the charging and discharging power. The driving distance per trip
and availability of EVs were used to reflect the driving requirements and to
implement intelligent charging and discharging management.
Index Terms— Electric Vehicles (EVs), EV model, state of charge (SOC),
driving pattern
I. INTRODUCTION
With more and more renewable energy integrated into power systems, it has
become a good option to use electric vehicles (EVs) to balance the uncertainties
introduced by the intermittency of the renewable energy. At the same time, replacing
conventional internal combustion engine (ICE) cars with EVs can reduce the “green
house” gas emission from the transport sector. Therefore, the idea of replacing
conventional ICE cars with EVs is expected to be realized in the near future with the
development of battery technologies, efforts on the prototyping of more efficient EVs
and the financial incentives provided by governments over the world.
Denmark is a unique place regarding the renewable energy utilization and the use
of EVs. At this moment, the wind power penetration level in Denmark is around 20%.
The Danish government has set an aggressive target of using wind power that 50%
of electricity consumption will be supplied by wind power in 2025.The average driving
distance in Denmark is 42.7 km per day [1]. It is possible to meet the driving
requirement for a whole day with a fully charged 20 kWh battery. Therefore, from
perspectives of balancing the uncertainties introduced by wind power and meeting
driving requirements by EVs, it is a good idea to deploy a large amount of EVs in
Denmark and integrate them into the Danish power system.
The possibility of using vehicle to grid (V2G) to improve wind power integration was
studied in [2]. The traffic data were used to calculate the vehicle fleet availability. It
was concluded that it is possible to have EVs providing instantaneous disturbance
and manual reserve to help integrate more wind power. The feasibility study of
implementing V2G scenario in Denmark was done in [3]. The system constraints for
integrating EVs into power systems were examined and the technical and
economical viability of various possible V2G architectures were studied. In Ref. [4],
the potential of using EVs in Denmark was investigated to identify the benefits for
power systems with high wind power penetration with intelligent EV charging
management. The research initiatives of the Edison project were also presented. A
vehicle to grid demonstration project was implemented in AC Propulsion Inc. to
evaluate the feasibility and practicality of EVs providing regulation service [5]. A test
vehicle was fitted with a bi-directional grid power interface and wireless internet
connectivity to carry out the demonstration. It was shown that it is feasible for EVs to
provide regulation service from a technical and economical point of view.
A three time-constant dynamic electric vehicle battery model for lead-acid, nickel
metal hydride (Ni-MH), and lithium ion (Li-ion) batteries was proposed in [6] to
represent the terminal voltage, state of charge (SOC) and power losses of each
battery type. A generic battery model for dynamic simulation of hybrid electric
vehicles was proposed in [7] which used the battery SOC as the state variable. It was
shown that the proposed battery model can represent the battery discharge curves
and the transient states. The naturalistic driving schedules obtained from field
operational tests of passenger vehicles in southeast Michigan were used to predict
energy usage as a function of trip length [8]. The analysis of naturalistic driving
schedules can provide the times spent at given locations as well as the likely battery
SOC at the time of arrival. Data from a fleet of PHEVs under normal operation were
collected and analyzed to assess the impact of usage patterns on vehicle
performance [9].
To the best knowledge of the authors, the existing EV models are focused on the
battery dynamic performance for charging and discharging or the driving pattern
analysis. Therefore, a generic EV model is proposed in this paper for the EV grid
integration study which reflects both the EV battery charging and discharging
characteristics and the EV users’ driving requirements.
The rest of the paper is arranged as follows: the requirements of an EV model for
EV grid integration study are described in Section II and the charging and
discharging characteristics of EV batteries are presented in Section III; in Sections IV
and V, the driving pattern information is analyzed and an EV model for EV grid
integration study is given; in the end, a brief conclusion is drawn.
II. REQUIREMENTS OF AN EV MODEL FOR EV GRID INTEGRATION STUDY
There are a few options to charge the EV batteries to meet the driving
requirements:
1. Fast charging – pull over EVs at the fast charging stations and charge the EV
batteries within 10-15 minutes
2. Battery swapping – drive EVs to the battery swapping stations and exchange
the used battery with a fully charged battery, the used batteries can be charged
at the fast charging station or at the battery swapping stations
3. Low power charging – charge EVs at homes, parking lots near home, working
place or shopping malls
The EV model proposed in this paper is for carrying out the grid integration study
with the low power charging scenario.
The EV battery supplies the energy needed for driving. Therefore, the charging
power and time have to be modeled in the EV model. At the same time, the
possibility of using EV batteries to provide power system regulating power makes it
necessary to study the discharging characteristic of EV batteries. Moreover, the
impact of charging and discharging on battery lifetime also has to be considered. The
EV batteries characteristics that have to be included in the EV model are: charging
power, discharging power, maximum SOC, minimum SOC/maximum depth of
discharge (DOD) and charging time.
In order to ensure that the EV users’ driving requirements are met, it is necessary
to study the EV users’ driving pattern. The information of driving pattern that has to
be reflected into the EV model is: initial SOC of EV batteries for the first trip of each
day, starting time and ending time of each trip, destination of each trip, driving
distance of each trip and energy consumption per km.
III. CHARGING AND DISCHARGING ANALYSIS OF BATTERIES FOR EVS
The EV batteries must store enough energy to meet the driving requirement. Leadacid batteries have low energy density typically around 20 Wh/kg whereas Ni-MH
batteries have an energy density of about 70 Wh/kg. Although Ni-MH batteries have
considerably higher energy density than lead acid batteries, they have lower charging
efficiency which is 90%. Li-ion batteries have energy density as high as 180 Wh/kg
[10]. The comparison of volumetric and gravimetric energy density of different
batteries shows that Li-ion batteries represent the most promising developments in
the area of EVs.
The expected life cycle performance of Li-ion, Ni-MH and lead-acid batteries is
shown in Figure 1. The EV batteries need to meet the life time requirement. If it is
assumed there is one time deep discharge per day, it means there are 4000 plus
deep discharge cycles during the 10-15 years battery lifetime. From Figure 1, the
deep discharge should not be more than 80%.
Figure 1 Cycle life Characteristic of Batteries [11]
The charging stages of Li-ion batteries are illustrated in Figure 2.
Figure 2 Charging Stages of Li-ion Batteries [12]
Stage 1 is the constant current charging till the cell voltage limit is reached. Stage 2
starts after the cell voltage limit is reached and the charging current starts to drop as
the full charge is approaching. Stage 2 terminates when the charging current is less
than 3% of the rated current and takes a long time. It is recommended to ignore
Stage 2. It means that the maximum SOC is 85% which is the SOC when the cell
voltage limit is reached.
The charging time can be calculated using Eq. (1).
⁄
where
.
is the charging time,
(1)
.
is the charged battery capacity expressed in Ah,
is the bulk charging current as per the charging rate.
In order to obtain the charging power and the discharging power of EV batteries,
the battery terminal voltage has to be calculated. As the Li-ion battery is the most
promising battery for EVs, the battery model of Li-ion battery is used to calculate the
charging and discharging power.
Depending on the charging or discharging rate, the battery open circuit voltage of
Lithium-Ion batteries can be obtained using Eq. (2) [13].
⁄
exp
(2)
where
is the open circuit voltage at time t during charging or discharging,
is the
-1
constant voltage, K is the polarization constant (Ah ), Q is the maximum battery
capacity (Ah), A is the expotional voltage, B is the expotional capacity (Ah-1).
Due to the internal resistance of batteries, the battery terminal voltage can be
determined by Eq. (3).
.
where
.
(3)
.
is the battery terminal voltage at time t during charging or discharging,
is the battery internal resistance,
.
is the battery current which is negative for
charging and positive for discharging.
The battery terminal voltage versus time and SOC during discharging and charging
of a 1 Ah 3.6 V Li-Ion battery are illustrated in Figure 3.
Figure 3 Battery Terminal Voltages of A Li-Ion Batteries for 1C, 2C and 5C Discharging and
Charging
The charging and discharging power can be determined by the battery terminal
voltage, the battery current and the charging and discharging loss.
.
.
.
(4)
,
.
,
(5)
where
,
and
,
are the converter loss due to charging and
discharging.
The converter loss varies according to the converter size and the manufacture. The
converter efficiency can reach 98.5%. This number is used to calculate the converter
loss.
The charging and discharging loss can be determined by Eq. (6) and Eq. (7).
1.5% (6)
,
.
. ⁄ 98.5%
,
.
.
1.5%
(7)
The discharging and charging power versus time and SOC of a 1 Ah 3.6 V Li-Ion
battery are illustrated in Figure 4.
Figure 4 Power of A Li-Ion Batteries for 1C, 2C and 5C Discharging and Charging
IV. ANALYSIS OF DRIVING PATTERN INFORMATION
In order to ensure that the EV users’ driving requirements are met, it is necessary
to study the EV users’ driving pattern.
Currently, the EVs on the road are very few. It is difficult to get the real EV driving
pattern. But it is reasonable to assume that the EV users will more or less have the
same driving pattern as the ICE car users. Therefore, the analysis of the driving data
of ICE cars can be a good estimate of the EV driving pattern.
The starting and ending time of each trip can be used to calculate all the possible
time slots for EV charging and discharging. The destination of each trip gives more
information of the availability of EVs for charging and discharging. Depending on the
availability of charging facility in the destination, the available time slots for EV
charging and discharging can be revised a little bit. The destination information also
indicates where the EV is connected to the power system.
The driving distance of each trip and the energy consumption per km can be used
to calculate the SOC change of EV batteries due to the specific trip. The energy used
per km for a home passenger car is between 120 Wh/km and 180 Wh/km. If there is
not detailed information for energy consumption, 150 Wh/km can be used to
calculate the energy consumption with the driving distance.
The information from the driving pattern that should be reflected in the EV model is
illustrated in Table 1.
Table 1 Driving Pattern Information
Information
Energy used per
km
Starting time for
Kth trip
Ending time for
Kth trip
Location
Value or Parameter
150 Wh/km
FA(t)
V. EV MODEL FOR GRID INTEGRATION STUDY
Based on the charging and discharging analysis of batteries and the driving pattern
analysis, an EV model is proposed for the low power charging EV integration study
which is depicted in Eq. (8).
1
0
0
(8)
1
where
is the EV charging power at time period t,
is the battery state.
If
is 1, EV batteries are in the charging state; if
is -1, the EV batteries
are in the discharging state;
equal to 0 means the EV batteries are in the idle
state or EVs are driving.
The constraint of
is given Eq. (9).
0
0
1
1
where
0
&
1
(9)
0
&
1
1 0 1
is the availability of EVs for charging and discharging,
is 0 if EVs is
and
are
driving or there is no charging spot when EVs are parked.
given in Section III which are 20% and 85%, respectively.
VI. CONCLUSION
A generic EV model is proposed for the EV grid integration study with the low
power charging scenario. The proposed EV model takes into account both the EV
battery charging and discharging characteristics and the driving requirements. The
charging and discharging characteristics of EV batteries were used to calculate the
charging and discharging power. The driving pattern information was also reflected
into the proposed EV model. The driving distance per trip was used to calculate the
SOC change of EV batteries and the starting and ending time of each trip was used
to obtain the availability of EVs for charging and discharging.
VII. ACKNOWLEDGEMENT
The authors are grateful to the financial support from the project of “Electric
vehicles in a Distributed and Integrated market using Sustainable energy and Open
Networks” (EDISON) funded by the ForskEl program (ForskEL Project Number
081216).
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