Toward a Wireless Charging for Battery Electric
Vehicles at Traffic Intersections
Shahram Mohrehkesh, Tamer Nadeem
Department of Computer Science
Old Dominion University
{smohrehk, nadeem}@cs.odu.edu
Abstract— In near future, Battery Electric Vehicles (BEVs) will
become widely accepted and used. One of the main challenges of
BEVs is their limited energy capacity. Current battery
technologies require BEVs to make frequent trips, in comparison
to traditional refuel, to charging stations for recharging. In this
paper, we envision a new scheme for charging of BEV based on
wireless charging. Our scheme exploits the frequent BEVs stops
at traffic intersections to charge their batteries via wireless
charging device. We study how to integrate control strategy at
traffic intersections for maximizing charging while minimizing
waiting delays. Simulation results are provided to show the
effectiveness of proposed charging schemes.
Keywords-component; battery; charging; vehicles; electric;
intersection;traffic light.
I.
INTRODUCTION
Number of operated battery electric vehicles (BEVs) is
increasing every day. One main challenge of the current BEVs
is limited capacity of current batteries. Due to this limitation,
BEV’s battery requires frequent recharging [1]. Currently,
BEVs stops by special charging stations for recharging. Due to
current battery technologies, the number of stops for recharging
is more than the number needed for traditional gas refueling.
Smart recharging solutions are required to encourage the BEVs
usage. One smart solution is to utilize the frequent stops of
vehicles at traffic lights for recharging.
In urban cities, the amount of time a vehicle spends at
traffic lights on a daily basis is significant. This intersection
delay or stop time could be utilized for BEVs recharging. In
doing this, wireless recharging scheme is required rather the
traditional recharging schemes that require physical plugging.
Stop time can be very significant in some scenarios like
Manhattan in New York City because of its large grid of
intersections. In such cities, there is a huge number of vehicles
(e.g., cabs and buses) that keep running most of the day.
Stopping and parking at charging stations for long duration for
recharging is not a practical solution for these vehicles.
Utilizing the short duration stops at traffic light for wireless
recharging instead of parking at charging stations is more
practical and appealing for such scenarios in terms of time and
cost saving. We name this scheme a Vehicular Wireless
Charging (VWC).
To realize the VWM scheme; two main questions need to be
answered. The first question is how much energy VWC scheme
could provide to BEVs? The second one is how to control
intersection delays to maximize recharging while maintaining
low delays? This paper tries to answer these questions by
showing how to exploit intersection delays in recharging BEVs
using wireless charging. To our knowledge, this is the first
work that studies wireless charging of BEVs at intersections.
The remainder of this paper is organized as follows. In the
next section, we describe the specification of batteries in BEVs.
Then, we introduce the charging and discharging properties of
batteries. Next, in section III developed modules and methods
are described. In Section IV, simulation results are shown and
analyzed and followed by discussion in section V. Finally,
Section VI concludes the paper and discusses future work.
II.
BACKGROUND
A. Battery’s Technology for BEVs
Batteries are mainly classified to different types according
to their chemical material (e.g., Ni—MH and Li-ion). For
BEVs, Lithium-ion is the best choice and outperforms other
types in terms of different metrics such as safety, performance,
life span, specific energy, and cost [2]. The most important
parameter is specific energy, or simply Energy, shown in scale
of kilo Watt hour (kWh). Typical values for current batteries
are from 5 to 50 kWh. This value is for a pack of battery cells
and is also related directly to another specification of battery
that is shown in mile scale. As an example, a battery of 7
kilowatt-hours means that the vehicle can go for 14-28 miles
without stop.
The quantity of energy consumption per each mile varies.
Many parameters such as vehicle’s weight, weight of battery
pack, weather conditions and road condition affect energy
consumption [3]. However, as a rule of thumb based on
different experiments, 325 Watt-hours energy is consumed for
every mile (1600 meters).
Other important parameter in batteries is their capacity
which is shown in terms of Ampere-hour (Ah). This parameter
and voltage of battery specify the charging time. Charging of
batteries depends on many other parameters like battery’s type.
However, generally it is assumed that there is a linear charging
model. For example, if a battery with capacity of 10Ah and
voltage of 1 kV is connected to a 1kW charging system, it takes
10 hours to full charge.
There are different levels and connection types for charging.
On 1998, the California Air Resources Board classified levels
of charging power that have been codified in title 13 of the
California Code of Regulations, the U.S. 1999 National
Electrical Code section 625 and SAE International standards.
They define three levels of charging which are categorized
based on ampere and voltage of charging connection. In this
paper, we focus on the characteristics of wireless charging
B. Wireless Charging for BEVs
Wireless charging uses induction technology or magnetic
charging. We discuss three methods of wireless charging which
two of them have developed particularly for BEV.



Mats: Mats use induction charging method. Nowadays,
several existing mats are used to offer charging for
devices like cell phones, laptops and etc. These mats
provide voltage of 18v and ampere of 0.83A. Although,
there is no existing mats for charging BEVs, making
charging mats with high ampere for BEVs is feasible.
Mats can be a potential choice for BEVs’ wireless
charging.
Plugless power: it is developed by Evatran in March
2010[4]. They claim it is the world’s first hands-free,
plugless, and proximity charging system for electric
vehicles. Charging is conducted while the BEV is
parked. Their system currently uses a pole that transfers
the power to the vehicle. So this method is not currently
feasible to use in intersection because the need of this
pole unless it is customized for intersection by putting
the pole horizontally on the ground for example. This
method also uses inductive charging method
OLEV: Researchers at the Korea Advanced Institute of
Science and Technology (KAIST) have developed an
electric transport system (called Online Electric
Vehicle, OLEV) where the vehicles get their power
from cables underneath the surface of the road via noncontact magnetic charging. In such system, power
source is placed underneath the road surface and power
is wirelessly picked up by the vehicle itself [5]. This
method is applicable for wireless charging at
intersections,
though
it
requires
significant
modifications to infrastructures.
III.
consumption for a non-moving vehicle is negligible. Charging
rate defines how much energy a vehicle will be recharged
during the intersection delay.
Electric vehicles will get charged only when they are within
charging zones. A charging zone is the area at that intersection
that is equipped with wireless charging equipment. Only nonmoving vehicles at red traffic light and within the charging
zone will be recharged wirelessly. Red phase duration of traffic
light in addition to charging rate determine the recharged
energy amount. Energy discharging model is a linear function
of the distance traveled by the vehicle and the consumption
rate.
B. Control Strategy Module
This is the main component of our system. As described in
previous section, a vehicle is recharged while it is waiting at the
intersection traffic light. Schedule and duration of traffic light
phases (e.g., red phase) impacts the amount of recharged
energy. Our objective is to adapt the traffic light’s control
strategies at intersections in order to maximize the amount of
recharging energy while minimizing the intersections delays.
Adapted control strategy will be compared with the original
pre-programmed control strategies in our simulation. Duration
of traffic light’s stop time will be increased or decreased to
provide more/less recharging time for vehicles based on various
parameters such as vehicle’s level of energy. Other factors will
be described later in our proposed schemes.
Various methods and strategies have been proposed in
literature (e.g. [6] , [7]) to control the traffic lights based on
different traffic parameters. However, none of these strategies
has considered the status of vehicle’s battery. In this paper, our
goal is to show the impacts of the traffic light’s control strategy
on the BEVs charging performance. Thus, we compare our
methods with static pre-programmed control strategy. We
believe that our methods can be used in combination with smart
and sophisticated control methods. This is part of our future
work.
We develop three control strategies and compare them with
each other and also with static pre-programmed traffic light
scheme.

Only Battery based Strategy (OBS): in this strategy,
periodically for each intersection, we count number of
non-moving vehicles with battery’s energy level above
a minimum, energymin, and below a maximum
threshold, energymax. In our model, the values of
energymin and energymax are 5% and 20% of battery’s
capacity respectively. The energymin is the threshold
that we used to instantly fully recharge the vehicle’s
battery as described earlier. If the portion of counted
vehicles to the total number of vehicles at the
intersection is above a threshold (default is 10%), we
increase the remaining time of current traffic light’s
phase (i.e., red phase) as a linear function of calculated
portion. We limit the maximum increase to 20% of the
original pre-programmed phase duration. On the other
hand, if the calculated portion is zero, current phase’
time is reduced to compensate for increased delays at
other intersections.

Topology and Traffic based Strategy (TTS): in this
strategy, it is assumed that the network topology of the
VEHICULAR WIRELESS CHARGING (VWC)
ARCHITECTURE
In this section, we describe the components of VWC. First,
we describe the implementation details of two modules that we
have developed to evaluate the system. Next, we describe
another module that is required in real implementation of
system.
A. Chargning and Discharging Models for BEVs
We model the rechargeable battery in BEVs by four
characteristics: energy capacity, initial energy of the battery,
consumption rate and charging rate. The initial energy shows
the initial energy level of the vehicle’s battery. In our model,
we assume a vehicle with battery’s energy below 5% of its total
capacity is at high risk to become disable because of running
out of battery. Therefore, a full recharging at charging station is
required. In this paper we abstract this assumption by instantly
replace the battery with a full charged one whenever its current
energy drops below its 5% capacity. Consumption rate is the
amount of energy the vehicle consumes while moving. Energy
road is known. In this scheme, non-moving vehicle
with minimum level of battery at each intersection is
found. Recharging duration to boost this vehicle
battery’s energy to arrive to the next intersection is
estimated. If the remaining time of traffic light’s
intersection delay is less than the estimated duration,
we increase the traffic light stop time provided that stop
time will not increased more than two times of original
stop time. Otherwise, traffic light’s stop time is not
changed.

high dense traffic scenarios, vehicles will stay longer at traffic
intersections and higher chance for charging. Charging zone is
assumed to be 20 meters in front of the intersections. Random
values for battery’s initial energy are assigned to vehicles at the
start of simulation. Ratio of charging rate to consumption rate is
selected from 1, 0.5, 0.1, or 0.01. Summary of all simulation
parameters is shown in Table I.
TABLE I.
Traffic Light based strategy (TLS): in this strategy, not
only the topology and traffic information are available
but also we know the traffic lights which has the
charging ability. In the other methods, it was assumed
that all intersections have the charging capability. This
strategy is developed to study the effects of enabling
some intersections with charging capability on the
control strategies. This strategy is an extension to
topology and traffic based strategy (TTS). Therefore,
the strategy of extending the stop time of a traffic light
is similar to topology and traffic based strategy.
However, the distance to next intersection used the
topology and traffic based strategy is replaced with the
distance to the next intersection enabled with charging
capabilities. Note that this control strategy is used only
at intersections enabled with charging capabilities.
C. Communications and Billing Sub-systems
One of the VMC’s requirements is the ability to
communicate with BEVs to collect corresponding information
such as battery level values of the vehicles, battery’s capacity,
charging and consumption rate. In doing this, we assume
vehicles are equipped with some communication mechanism
such as DSRC technology that enable sending up-to-date
information (e.g. location, speed, time, identification)
periodically to intersection controller that is equipped with the
corresponding communication unit. In addition, intersection
controllers might be able to communicate with each other in
order to exchange their information in addition to traffic
statistics such as the traffic flow rate. This information could be
used to improve the traffic lights control strategy. The
infrastructure is also responsible for authentication and billing
process. Vehicles approaching the intersection authenticate
themselves to the intersection. Consequently, the intersection
permits only authenticated vehicle to get charged through the
wireless charging equipment. Also, charging rate and charging
duration for each individual vehicle is sent to the central billing
system.
IV.
PERFORMANCE EVALUATION
Parameter
Network and traffic Model
Number of intersections
Number of vehicles
Number of routes
Simulation duration
Value(s)
Charging zone
Maximum speed at roads
Distance between intersections
Vehicle Model
Vehicle Energy Capacity
Energy consumption
Battery cell capacity
Battery output voltage
Vehicle length
Initial Energy of Vehicles
Charging rate
20m
13 m/s
100 m
24
150
75
7200 second s
5 kWh
200 Wh per km
90 Ah
3.6 V
4m
random
9.6, 4.8, 0.96,
0.096 kW
B. Simulation Scenarios
We evaluate scenarios using different charging strategies
described in section III in addition to two charging schemes: No
Charging and No Strategy described as follow:

No Charging (NoC): in which no intersection is
enabled with charging capabilities. Only energy
consumption is calculated and reduced from total
vehicles’ energy.

No Strategy (NoS): in this method, schedule of traffic
lights are not modified and they operate based on their
pre-programmed static control program.

Charging strategy: one of the OBS, TTS, and TLS are
used. TLS will be compared only with NOS because it
is a special strategy based on intersections with
charging capability which is not included in the other
methods.
To compare these methods, four metrics are defined:

Average Remaining Energy: this parameter shows how
much each charging scheme could provide energy to
the vehicles.

Average Recharging Occurrence: it shows on average
the number of times the vehicles’ energy level falls
below the energymin threshold. In order to avoid
stopping of vehicles with energy level below energymin,
we charge their battery back to full capacity. This
metric shows the effect of the charging schemes on the
number of trips to charging stations for full recharge.

Average Waiting Time: it shows the average waiting
time experienced by vehicles at intersections. This
parameter evaluates how a charging scheme is able to
In this section, we describe our simulation tools, evaluation
metrics, and the results of extensive experiments.
A. Simulation Tools and Parameters
We use SUMO [8] for our experiments and TracI [9]
module to control the traffic lights and collect performance
metrics. For simulation, a network grid of 24 intersections is
developed that are connected to each other through single lane
in both directional. Distance between two consecutive
intersections is 100m. Scenarios are evaluated with 150
vehicles (20 vehicles per kilometer- low dense traffic). We use
a low dense traffic scenario to show the effectiveness of
mechanism even in low dense traffic scenarios. Obviously, in
SIMULATION PARAMETERS
No Strategy
OBS
TTS
Figure 1. Average remaining energy using different methods with charging
rate of 9.6kW
TTS
OBS
No Strategy
TTS
OBS
No Strategy
TTS
OBS
0.096 kW
3
0.96 kW
4.8 kW
0
kW
TTS
No Charging
OBS
0
No Strategy
1
No Charging
2
Average Time to Rirst Recharging(s)
8000
7000
6000
5000
4000
3000
2000
1000
0
TTS
2.36
OBS
3
Different charging control strategies also improve the
Average Time to First Recharge. In our calculation of the
average first recharging metric, we use the total simulation
duration (i.e., 7200sec) as the average first recharging for any
vehicle that didn’t run out of energy during the simulation.
Figure 4 shows that with high charging rates, charging schemes
outperform NoC scheme significantly. In addition, OBS and
TTS scheme outperform NoS scheme for high charging rates.
With the very high charging rate 9.6kW, all charging schemes
have similar performance. This is because only 3 vehicles (2%
of all vehicles) ran out of energy during the simulation for this
scenario.
No Strategy
4
4.8 kW
Figure 3. Average waiting time for differenet t charging schemes with
different charging rates
TTS
4.33
0.96 kW
OBS
Avg Remaining Energy(kWh)
4.43
4.26
5
0.096 kW
No Strategy
Figure 2 shows the Average Remaining Energy and
Average Recharging Occurrence for different charging rates
under charging schemes. In all cases, OBS and TTS outperform
NoS by a significant decrease (10%-20%) in the Average
Recharging Occurrence while maintaining very low increase in
the Average Waiting Time as shown in Figure 3.
Avg. Waiting time(s)
TTS
C. Simulation Results
Figure 1 shows the significant impact of different charging
schemes on the Average Remaining Energy of vehicles when
the charging rate is 9.6 kW that is equal to the consumption
rate. The figure shows that all charging scheme (NoS, OBS,
TTS) have increased the Average remaining energy, compared
to the no charging (NoC) scheme, with a factor of more than
1.8. In addition, the Average Recharging Occurrence (not
shown due to space limitation) has decreased from 1.8 for NoC
scheme to 0.02 for the other charging schemes (NoS, OBS,
TTS). Moreover, charging schemes have increased the Average
Waiting Delays (not shown) with 57 seconds only over the
original 3800 seconds in case of NoC scheme.
4500
4000
3500
3000
2500
2000
1500
1000
500
0
OBS
Average Time to First Recharge: it shows the average
time to the first time the vehicle’s energy falls below
the energymin threshold. In our model, as described
earlier, once the vehicle’s energy falls below energymin,
it gets recharged to its full capacity instantly.
No Strategy

No Strategy
increase the stop time without introducing long waiting
time.
9.6 kW
2.5
Figure 4. Averagee Time to First Recharge for different charging schemes
with different charging rates
2
1.5
Avg Remaining
Energy(kWh)
1
0.5
Avg. Recharging
Occurance
0.096 kW
0.96 kW
TTS
OBS
No Strategy
TTS
OBS
No Strategy
TTS
OBS
No Strategy
0
4.8 kW
Figure 2. Performance Comparison of different charging schemes with
different charging rates
Figure 5 shows the effect of different control strategies in
comparison to NoS and NoC schemes over time when charging
rate is 0.96kW. For NoC scheme, vehicles lose their energy
faster and several of them reach to the point that they need to be
recharged around the time of 1800 seconds, while several
vehicles in the charging schemes recharge around 5400. The
Average Recharging Occurrence (not shown) for all charging
schemes is less than the NoC. In addition, as shown, OBS and
TTS schemes have low fluctuations in their Average Remaining
Energy with respect to NoS scheme.
traffic at the intersection in the control strategy. That is part of
our future work.
3.5
3
0.6
2.5
2
NOC
1.5
NOS
1
OBS
0.5
TTS
0.5
Avg. Recharging Occurance
times for NOS
0.4
Avg. Recharging Occurance
times for TLS
0.3
7200
6600
6000
5400
4800
4200
3600
3000
2400
1800
1200
600
0
0
0.2
0.1
time(seconds)
Figure 5. Average Remaining Energy(kWh) with 0.96 kW charging rate
scenario
The Average Remaining Energy when the charging rate
equals the consumption rate (9.6kW) is shown in Figure 6. In
this scenario, all the charging schemes have provided to
vehicles enough energy that exceeded the consumed energy
during simulation. Therefore, the control strategies (OBS, TTS)
could be optimized to avoid extra recharge and consequently
reduce delays at intersections.
Average Remaining Energy (kWh)
5
4.5
4
3.5
0
40
50
60
70
80
90
Percentage of intersection with charging capability
100
Figure 7. Effect of percentage of intersection with charging capabilty on
Average Recharging Occurence
TLS also behaves better than NoS in terms of Average
Remaining Energy (Figure 8). Even though the difference is
not significant, it shows that inclusion of knowledge about the
topology of intersections with charging capability can provide
more energy. This improvement happens with some delays
especially at low percentage such as 40% as shown in Figure
9. This delay gets reduced significantly with large percentage
such as 60%.
4.5
3
4
2.5
3.5
2
NOC
NOS
OBS
TTS
1.5
1
0.5
3
2.5
2
Avg Remaining Energy(kWh)-NOS
1.5
0
0
900 1800 2700 3600 4500 5400 6300 7200
time(s)
Figure 6. Average Remaining Energy(kWh) with 9.6 kW charging rate
scenario
Next, we investigate the presence of charging capability
only at a percentage of intersections. Intersections with
charging capabilities are selected uniformly. Average
Recharging Occurrences are shown in Figure 7 for different
percentage of intersections with charging capability. We
compare Traffic light based Strategy (TLS) only with NoS
method because the other methods do not include the
knowledge of charging capability of intersections in their
decision. As it is shown, when 40% to 70% of the intersections
have a charging capability, TLS outperforms NoS significantly.
Both converge when the number of intersections with charging
capability is 70% or more.
Vehicles in TLS scheme are forced to stop as much as they
can in the traffic light with charging capability to have enough
energy to go to the next traffic light with charging capability.
This situation causes vehicles to wait for long times at
intersections when the percentage of intersection with low
charging capabilities. This situation can be solved with smart
control strategies. For example, we could include the volume of
1
Avg Remaining Energy(kWh)-TLS
0.5
0
40
50
60
70
80
90
Percentage of intersection with charging capability
100
Figure 8. Effect of percentage of intersection with charging capability on
Average Remaining Energy
3960
3940
Avg. Waiting time(s)- NoS
3920
Avg. Waiting time(s)-TLS
3900
3880
3860
3840
3820
3800
3780
40
50
60
70
80
90
Percentage of intersection with charging capability
100
Figure 9. Effect of percentage of intersection with charging capability on
Average Waiting Time
The results demonstrate that the TLS performance could be
improved if information like traffic flow and vehicles’ routes
at intersections is utilized in the control strategy so TLS
becomes more intelligent.
V.
DISCUSSION
A. Improvement to TLS
TLS method can be improved using smart method that tries
to calculate the time needed for arriving more vehicles to the
next enabled intersection rather than only the vehicle with the
minimum level of battery. Vehicles will not have the same
route and will arrive to the enabled intersection at different
times and locations. Hence, this information could be taken into
account to improve the performance of TLS.
B. Coverage of Charging Zones
In our simulation, we assumed all intersections have the
same coverage charging area. However, each intersection could
have different coverage area. Two scenarios could be
investigated: (I) using the global optimum charging zone for all
of the intersections based on different parameters like the flow
of vehicles; (II) finding the local optimum charging zone for
each intersection based on the flow of traffic at that intersection
and also the flows at the adjacent intersections. Also, the
dependency between this parameter and vehicles characteristics
such as battery charging rate can be investigated.
C. Intelligent Traffic Lights
Many of the traffic lights use different intelligent control
strategies (e.g. SCOOT) that depend on different statistics such
as flow of traffic. We believe that these mechanisms can be
used in combination with our methods. In fact, our strategies
can be enhanced based on similar factors like flow of traffic or
synchronization between close intersection traffic lights. We
can make smarter decision at traffic lights if we know the
incoming traffic of BEVs.
D. Market Penetration
BEVs will be presented to market gradually and some
solutions can be adapted based on the percentage of BEVs to all
the vehicles. For example, in OBS, instead of calculating the
portion of BEVs with critical energy level to all vehicles, only
to all BEVs should be considered.
E. Higher Charging Rate
Charging systems are advancing every day and higher
charging rate will be feasible in near future. Presence of these
charging systems provides two opportunities. First, delay time
at intersections can be reduced and also number of intersection
with charging capability can be reduced as well. Second, this
can be used to provide scenarios of charging vehicle even when
they drive in the highways. With fast charging systems, vehicle
can be charged at the ramps before entering the highway and at
exists after exiting the highways.
F. Variation in Vehicles
One important issue that should be addressed in future is the
design of control strategies that take into consideration different
characteristics of various vehicles. In practice, there will be a
blend of vehicles with different charging rates, consumption
rates, and battery capacities. These parameters should be taken
into account when designing a strategy. For instance, when the
majority of vehicles at an intersection have higher charging
rate, stop time can be less than the scenario in which the
vehicles have lower charging rate. Another example, in
strategies like TTS, when the vehicle’s routes are estimated to
calculate energy consumption, varieties of consumption rates
should be taken into account.
VI.
CONCLUSION AND FUTURE WORKS
We evaluated the effectiveness of exploiting frequent stops
at traffic intersections in cities to charge battery electric
vehicles. BEVs will be charged based on wireless charging
technology. Performance of charging methods was evaluated
through extensive simulation scenarios. In summary, charging
methods showed to be very effective. With more collected
information about traffic and vehicles, the control strategy
schemes could be optimized and optimum result could be
achieved.
We believe that there are still some challenges to realize this
idea. However, we generally believe that this idea has
significant advantages and is valuable to further investigation.
In future work, we will evaluate methods while making them
more intelligent using different existing traffic intersection
control strategies.
VII. ACKNWOLDGEMENT
Authors would like to thank Liang Chen on her efforts for the
initial work in this research.
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