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Scheduling Charging of Electric Vehicles in a
Secured Manner using Blockchain Technology
Muhammad Umar Javed and Nadeem Javaid*
COMSATS University Islamabad, Islamabad 44000, Pakistan
*Corresponding author: www.njavaid.com, nadeemjavaidqau@gmail.com
Abstract—In the proposed work, a Mobile charging Vehicle-
to-Vehicle (M2V) charging strategy is introduced for an efficient
charging of Electric Vehicles (EVs). It also covers the conventional
Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging
strategies. The charging of vehicles is done in a Peer-to-Peer (P2P)
manner; vehicles charging from Charging Stations (CSs) or Mo-
bile Vehicles (MVs) in the absence of a central entity. Blockchain
technology is used to overcome the privacy issues. Further,
it promotes transparency, trustworthiness, data immutability
and security. The main objectives of the proposed work are
the charging cost reduction and scheduling the EVs’ charging.
Two algorithms are proposed which deal with the charging
schedule and waiting time at CSs, respectively. Mathematical
formulation is done and the total charging cost is calculated.
Simulation results prove that the proposed work outperforms the
conventional techniques in minimizing the EVs’ charging cost.
Index Terms—Mobile Vehicles, Vehicle-to-Vehicle Communica-
tion, Scheduling, Blockchain, Security
I. INTRODUCTION
With the huge increase in the population and urbanization,
issues such as drastic climate changes, increased gas emissions
and depletion of fossil fuels arise. Rapid progress has been
made in the vehicle industry over the past few years. Road
congestion has increased drastically owing to the large number
of vehicles. It created huge amount of environmental pollution,
which includes noise pollution, air pollution, land pollution,
etc. These factors disturb the global economy and community
to a great extent, which leads to the need of new revolutions
for mitigating the previously mentioned problems [1].
To reduce the huge amount of energy required by the vehicles,
scientific and research community have joined hands and
started focusing on the Electric Vehicles (EVs) as a source
of clean energy. EVs have the ability of reducing the fuel
demands as well as the gas emissions. EVs can be powered
either from Charging Stations (CSs) or the batteries installed
within [2]. The EVs emerging drastically in the local market
aim to make the grid a beneficial entity by introducing the
concept of Grid-to-Vehicle (G2V) charging strategy [3].
With an immense development being made in the Information
and Communication Technologies (ICT) sector, bi-directional
communication and trading is becoming a reality. In Smart
Grids (SGs), Plug-in Hybrid Electric Vehicles (PHEVs) are de-
veloped, which play major roles in transportation management
[4]. The increasing number of EVs also poses some problems
like the range anxiety problem, lack of charging spots and
privacy and security issues [5]. To overcome the issues of
conventional energy trading system such as single point failure
and privacy leakage, a Peer-to-Peer (P2P) system is the only
solution. Though it still has some problems, such as privacy,
security and trust issues [6].
To overcome the above mentioned problems, a decentralized
system is required which ensures security, privacy and data
immutability. For this, blockchain technology is used in the
vehicular sector. Blockchain technology promotes trust, se-
curity, data immutability, etc. The transactions are saved in
a distributed ledger; copies are available with every node
ensuring transparency which means that the transactions’
data being stored can not be altered. For data verification,
consensus algorithms like Proof of Work (PoW), Proof of
Authority (PoA) and Proof of Stake (PoS) are used. In PoW,
nodes compete against each other to solve the puzzle and the
winner gets to mine the blocks. In PoA, the nodes which
prove their identities get to mine the blocks. Whereas, in PoS,
the node with the highest stake gets the upper hand to mine
the blocks. 51% agreement between nodes is required for any
action to take place [7]. A number of problems are associated
with the charging of EVs like limited battery capacities, less
number of CSs, trust issues, etc. The cost of charging also
plays a vital role. The vehicle users are reluctant to have
their vehicles charged at higher costs. Primary focus of the
current research is the scheduling strategies of the EVs. The
problem of less number of CSs can be resolved using the
blockchain technology. It ensures trust between users which
further promotes users’ willingness to trade energy among
themselves. By using Global Positioning System (GPS), the
location information of the EVs can be traced and sent to
the other vehicles of the network. Then, the shortest distance
between EVs is calculated from the information obtained
through GPS [8].
After reviewing the work done by several authors in [4], [7]
and [9], the motivations for the proposed work are summarized
as follows:
•there is a need of a charging scenario other than conven-
tional scenarios, i.e., G2V and Vehicle-to-Vehicle (V2V),
where vehicles are charged from Mobile Vehicles (MVs),
•a charging schedule should be devised to charge the
vehicles efficiently and to reduce the charging load on
the CSs,
•blockchain technology should be implemented to promote
transparency, data immutability, traceability and security
in a P2P trading scenario and
•such algorithms are required which will reduce both the
charging cost and charging time of the EVs.
The major contributions made in this paper are as follows:
•scheduling algorithms are introduced to deal with the
charging cost and charging time of EVs,
•M2V communication between vehicles is done and com-
pared with existing V2V and G2V communications,
•cost reduction is achieved using the proposed scheduling
algorithm,
•both the number of hashes generated and the mining time
required are calculated using different difficulty levels and
•mathematical formulation is done to calculate the total
charging cost.
II. RE LATE D WOR K
At present, the Vehicular Network (VN) is getting smarter
with every coming day and research is aiming towards making
it an integral part of the smart city infrastructure. PHEVs
play a vital role in distributed transportation and management
in SG. PHEVs are able to get energy both from the CSs
as well as from other PHEVs using V2V trading. In the
near future, the traffic sector will be comprised of a huge
number of intelligent EVs. To ensure the security and cost
reduction, blockchain will surely play a major role. Several
research organizations are currently working on integration of
blockchain in the vehicle sector. Authors in [10] described
methods of making the EV communication secure using
cryptographic keys and establishing a public key infrastructure.
The proposed model used visible light and acoustic side-
channel techniques for minimizing the throughput requirement
and providing device independency. Authors in [11] presented
a decentralized security model. The scheduling of vehicles’
charging is also presented. Authors in [12] developed and
implemented a game based mechanism which involves auction
mechanism. It helped to resolve the large scale EVs’ charging
coordination problem.
In [13], authors gave the concept of a distributed coalition
charging scheme for Plug-in EVs (PEVs) which cut down
the charging cost for PEV fleets. The computations were
performed locally. Authors of [14] proposed a coordinated
EVs’ charging technique in a RES powered Micro Grid (MG)
using a Markov Decision Process (MDP) approach. The MG
dealing with buildings was under study in this work. The EV
energy demands of buildings were claimed to be efficiently
fulfilled using RES in the vicinity of MG. By implementing
various stochastic dynamic programming methods, authors in
[15] investigated the energy management in a Smart Home
(SH) equipped with PEVs to address the issue of volatility of
RES supply while considering the electricity cost.
Authors discussed about the effects of EVs on energy demand
and supply, stability and reliability in [16]. Two different
scenarios were discussed: Vehicle-to-Grid (V2G) and G2V.
Authors in [17] proposed a double-layered model and tried to
properly allocate the charging lots to EVs. In the first stage,
lots were properly allocated; whereas, in the second stage,
integration of RES in the charging lots was studied. Similarly,
authors in [18] proposed the MG scheduling for EVs’ charging
and also discussed about the algorithms which could make the
EV charging and discharging an easy and efficient task. Ways
to reduce the operational costs and environmental pollution
were also discussed. Integrating RES in EVs can prove to
be a beneficial task and can overcome the hazards of the
environmental pollution. Authors in [19] proposed a MG
architecture running on RES. It is equipped with a charging lot
and aggregated EVs. The main objective is the cost reduction
and also to provide incentives to those EV users, who took
part in DR strategies.
III. PROB LE M STATEM EN T
For ensuring the establishment of a smart city, it is necessary
that Vehicular Network (VN) is made smart, intelligent and
is powered using green energy, i.e., RES. The inclusion of
RES makes the ecosystem user-friendly and ecological. In
modern era, vehicles are getting smarter, throttling is getting
faster and infrastructure is becoming more complex. This is
due to the huge number of diversified electronic equipments
and Electronic Control Units (ECUs) being installed in the
vehicles. The interconnection between devices and ECU is
opening doors to new communication streams [20].
To ensure maximum EVs participation and to exploit the
social welfare, the energy transactions between EVs and CSs
are being monitored and audited [4]. However, the proposed
system lacks in cost reduction. Due to the dynamic nature
of EVs charging, discharging and the mobility of the EVs,
an efficient load transmission and dispatch to the EVs is
quite challenging [7]. The EV users are being incentivized
to encourage collective charging. However, it created burden
on CSs leading to frequent energy shortages. Hence, either
the user comfort or the energy transaction at reduced cost is
compromised. An optimized algorithm for EV charging and
discharging in different scenarios, e.g., V2V, G2V and Mo-
bile charging Vehicle-to-Vehicle (M2V) needs consideration,
which also reduces the cost.
IV. PROPOSED SYSTEM MODEL
In the proposed system model, four different entities exist,
i.e., charging EVs, discharging EVs, MVs and the CSs.
These entities send the status details to the nearest agents. A
blockchain based EV charging scenario: M2V, is established.
The transaction details are being stored in a distributed ledger.
The Discharging Vehicles (DVs) can be charged either from
the CSs or from the MVs. Whenever a vehicle needs energy,
it sends its request to the agent. The agent then forwards this
request to the charging entities. Meanwhile, the agents collect
and save the incoming demands and the vehicles data, keeping
a check on the total number of demands being made. All
entities, i.e., vehicles and the CSs have their own accounts
and wallets. Whenever the transactions are being made, the
digital currency is used. When buying from the CS, cost is
usually high, so the EVs tend to buy energy from the nearest
MVs which reduces the cost. Figure 1 shows the vehicles
arrangement for three different charging scenarios, each having
EVs, MVs and the CSs, motivated from [21]. The charging
data is stored in blockchain where contracts are deployed.
After a blockchain is established, it is stored in a distributed
Legend
Mobile Charging Vehicle
Ordinary Vehicle
Charging Station
V2V communication and
energy flow
M2V communication and
energy flow
Energy Flow
Data Flow
Battery fully charged
Battery getting
discharged
Agent 1 Agent 2 Agent 3
Ledger LedgerLedger
...
Agent n
Ledger
Blockchain creation
and smart contracts
following
Data stored in ledger and
copies sent to all agents
G2V communication and
energy flow
Fig. 1: Proposed system model
ledger. The copies of this ledger are provided to all the agents,
who are the part of the network.
V. EV CHARGING SCHEDULE
In this section, the charging schedule of the EVs is dis-
cussed. It is important to ensure that the EVs added in
the proposed blockchain-based network should have proper
scheduling scenario. There are four entities in the proposed
charging scenario: agents, CSs, EVs and MVs. Algorithm
1 gives the charging schedule of the vehicles: calculation
of distance between vehicles, CSs and MVs, calculation of
different costs, etc. Whereas, algorithm 2 gives details about
the waiting procedure which the vehicles have to adopt when
they are at the CSs. Figure 2 shows the charging of an EV
from three different sources, i.e., from a MV, from another EV
and from a CS. The charging source of the EV is decided by
the EV according to the distance and price relationship. If the
distance between the EV and the charging source is large, then
the price will be automatically high. So, the EV will discard
that source and travel towards another nearest charging source.
D1
C1
MV
CS
G2V communication
and energy flow
V2V communication
and energy flow
M2V communication
and energy flow
G2V communication and energy flow
DV
CV
Legend
CV: Charged Vehicle
DV: Discharged Vehicle
CS: Charging Station
MV: Mobile Vehicle
Fig. 2: Charging process of an EV from different sources
Algorithm 1 Algorithm of charging schedule
1: Initialization
2: Inputs: EV, MV, CS, disG2V,disM2V,disV2V
3: Outputs: EV charging schedule
4: Find the number of registered vehicles
5: for (Each vehicle EV, EV = 1, ..., n) do
6: Find distance between EV and MV, disM2V
7: Find distance between EV and CS, disG2V
8: Find distance between EV and other EV, disV2V
9: end for
10: for (Each vehicle EV, EV = 1...n) do
11: Find state of charging, SoC
12: if (Vehicle is discharging) then
13: SoC = 1
14: else if (Vehicle is not discharging) then
15: SoC = 0
16: end if
17: end for
18: for (Each vehicle and charging station) do
19: Calculate charging cost using equation 3
20: Calculate distance cost using equation 4
21: Calculate waiting cost using equation 5
22: Calculate reward and penalty cost using equation 6
23: end for
24: Calculate total cost using equation 7
25: End
VI. RE SU LTS AN D DISCUSSION
This section covers the simulation results and discussion.
The simulations are performed in two different environments.
In the first step, smart contracts are written in Solidity and
then they are verified in RemixIDE. The smart contracts cover
the registration of the vehicles and the nature of the requests
Algorithm 2 Algorithm of waiting at charging station
1: Initialization
2: Inputs: V, v, τ,disG2V,δ, diff(V,v)
3: Outputs: Waiting time
4: Find distance between vehicle and charging station,
disG2V
5: Find the number of vehicles at a charging station, V
6: Find the number of incoming vehicle at the charging
station, v
7: Find the difference between V and v, diff(V,v)
8: if (disG2V≤τ)then
9: Let the vehicle come for charging
10: else if (disV2S> τ)then
11: Ask the vehicle to look for some other source for
charging
12: end if
13: if (diff(V,v) ≤δ)then
14: Vehicle is eligible to be charged
15: else if (diff(V,v) > δ)then
16: Deny the vehicle to be added to the charging queue
17: end if
18: for (disV2S≤τ§§ diff(V,v) ≤δ)do
19: Calculate waiting time
Twaiting =v∗300 (1)
20: end for
21: End
being made: requests accepted and requests denied. In the
later stage, the charging scheduling is done in Spyder (Python
3.6 package) provided by Anaconda. These simulations are
performed on HP 450G ProBook, having 1 TB Hard Drive
and 8 GB RAM. Figure 3 shows the transaction and execution
costs in terms of gas for five different functions. These
functions are used in the smart contract deployed in Solidity.
Figure 4 shows the probabilities of requests being entertained
by different charging entities in 4 different time slots. Each
time slot consists of 6 hours. In time slot 1, when no request
is made, all entities have same probability of requests, i.e.,
0.33. In time slot 2, requests are being entertained by the CS.
Therefore, its probability is increased to 0.50 while probability
of EV and MV is decreased to 0.25 each. In time slots 3 and 4,
requests are being entertained by EVs and MVs, respectively.
The respective probability values are increased to 0.50 while
the other two values are reduced to 0.25. Different probability
values show the active status of different charging entities in
different time slots. Figure 5 shows the charging price and the
travelling price of G2V, V2V and M2V. It is observed that both
the charging price and the travelling price are less for M2V.
This is because when charging through the CSs, waiting cost
and distance cost are also incurred by the vehicles. On the
other hand, charging through the EVs incurs the distance cost
along with the charging cost of the EVs through CSs. Whereas,
MVs have less charging costs because they are equipped with
batteries and are self charged. So the major cost which exists
in M2V is the distance cost. Figure 5 shows the increasing
Fig. 3: Costs comparison
Fig. 4: Request probabilities
trend with the increasing number of vehicles. The travelling
cost primarily consists of fuel costs and maintenance costs.
Figures 6 and 7 show the number of hashes generated and the
mining time for the transactions performed against different
difficulty levels. It is observed that both the number of hashes
generated and the mining time increase with the increase in
difficulty level. Difficulty is defined as the measure of the
complexity for miners to find a hash or a signature for a block
in the network. The hash is generated using random numbers.
The amount of zeroes a signature requires initially determines
the difficulty level. The formula for calculating difficulty is
given in equation 2, taken from [22].
diff iculty =H ash target(genesisblock)
Hash target(curr entblock)
(2)
25 50 75 100 125 150 175 200
Number of vehicles
0
2000
4000
6000
8000
Price ($)
G2V Charging Price
V2V Charging Price
M2V Charging Price
G2V Travelling Price
V2V Travelling Price
M2V Travelling Price
Fig. 5: Cost comparison of different scenarios
Where, target is a 256 bit number. Table I gives all the
important details related to the contract creation phase. Similar
tables can be drawn for different functions involved in smart
contract.
Fig. 6: Hashes generated for different transactions
TABLE I: Contract creation
Parameter Value
transaction hash 0x2e104...a93a6
contract address 0x35ef0...450cf
from 0xca35b...a733c
to EV.(constructor)
transaction cost 998685 gas
execution cost 713861 gas
hash 0x2e104...a93a6
input 0x608...a0029
decoded input {}
decoded output -
logs []
Fig. 7: Mining time for different transactions
VII. MATHE MATI CA L FOR MU LATI ON
This section provides the mathematical calculation of the
total cost, which is the sum of four different costs, i.e.,
charging cost, waiting cost, distance cost and reward or penalty
cost. These costs are denoted as C1T, C2T, C3Tand C4T,
respectively. These costs are mathematically given below in
equations 3 - 6.
The first cost is the total charging cost for CSs, EVs and MVs.
PCS ,PM V and PEV are the generation costs per unit for CSs,
EVs and MVs, respectively. Whereas, CC S , CM V and CEV are
the energy selling costs for CSs, EVs and MVs, respectively.
C1T= (PCS ∗
I
X
i=1
CCS )+(PM V ∗
J
X
j=1
CMV )+(PE V ∗
K
X
k=1
CEV )
(3)
After calculating the charging cost, the next cost which needs
to be calculated is the distance cost. This cost is related to the
distance between the charging and discharging entities. The
main objective of the vehicles is that they should be charged
from their closest entities, either from the CSs, MVs or from
other EVs. Equation 4 calculates the total distance cost, where
disS2V, disM2Vand disV2Vgive the distance between vehicle
to CS, vehicle to MV and vehicle to EV, respectively.
C2T= (PCS ∗disG2V)+(PMV ∗disM2V)+(PEV ∗disV2V)
(4)
The third cost, i.e., the waiting cost also needs consideration.
This cost is incurred when the vehicle needs to wait at the
time of charging. For instance, a vehicle goes to the CS for
charging. At that CS, a number of vehicles are already present
for charging purpose. Therefore, this new incoming vehicle is
added to the waiting queue. This also implies even when the
vehicle intends to get charged from the MV or the EV. So for
calculating this cost, the distance is calculated, which is then
multiplied with the number of the vehicle in the queue. This
cost calculation is given in equation 5.
C3T=(disG2V∗diff (V, v )) ∗Y
X
y=1
Ve+
Z
X
z=1
Vm (5)
The fourth cost is the reward/penalty cost. If a certain vehicle
saves some units of charge or if it generates some extra units,
which it can sale to other vehicles, then the particular vehicle is
given some reward and vice versa. Equations 6a and 6b show
the reward and penalty calculation. In the given equations, Q
is the price per unit, Tsare the total units saved and Tware
the units wasted. Whereas, ev and mv are the number of EVs
and MVs, respectively.
C4T r = (Q∗Ts)∗L
X
l=1
ev +
M
X
m=1
mv(6a)
or
C4T p =−(Q∗Tw)∗L
X
l=1
ev +
M
X
m=1
mv(6b)
Equations 3 to 6 are all summed up to give up the total cost,
given in equation 7.
CT otal =C1T+C2T+C3T+C4T r/4T p (7)
A. Objective Function
The total charging cost is minimized using equation 8,
which is the objective function [21].
min (CT otal) = C1T+C2T+C3T+C4T(8)
Subject to following constraints:
•Minimizing the EV and MV charging costs, as given in
equations 9a and 9b.
0≤CMV ≤Cmax
MV (9a)
0≤CEV ≤Cmax
EV (9b)
•The other is maximizing the units saved or minimizing
the units being wasted, as given in equation 9c.
max (Ts)or min (Tw)(9c)
VIII. CONCLUSION
In this paper, an improved vehicle charging scenario, i.e.,
M2V is proposed and compared with the existing scenarios,
i.e., G2V and V2V. The vehicles communicate with each other
in a P2P manner for data transfer and energy trading pur-
pose. For an efficient charging schedule of the vehicles, new
algorithms are proposed. A vivid charging cost reduction is
observed when using the scheduling algorithms. The proposed
work uses blockchain technology for vehicles’ registration and
also for ensuring data immutability, security and tamper proof
nature. The blockchain data is then stored in a distributed
ledger; copies of which are placed at every agent’s end. PoW
makes the entire system trustworthy and attracts more users
to be added to the network. The mathematical formulation
guarantees that all possible costs are calculated and the total
cost is reduced.
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