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Scheduling Charging of Electric Vehicles in a Secured Manner using Blockchain Technology


Abstract and Figures

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 Mobile 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.
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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:,
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
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
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.
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.
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.
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
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
Agent 1 Agent 2 Agent 3
Ledger LedgerLedger
Agent n
Blockchain creation
and smart contracts
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.
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.
G2V communication
and energy flow
V2V communication
and energy flow
M2V communication
and energy flow
G2V communication and energy flow
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
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,
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 (disG2Vτ)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
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 (disV2Sτ§§ diff(V,v) δ)do
19: Calculate waiting time
Twaiting =v300 (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)
25 50 75 100 125 150 175 200
Number of vehicles
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
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
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.
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)
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=(disG2Vdiff (V, v )) Y
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 = (QTs)L
ev +
C4T p =(QTw)L
ev +
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.
0CMV Cmax
MV (9a)
0CEV 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)
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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[22] [Last Acessed:
June 22, 2019]
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... The authors in [16] proposed vehicle to vehicle mobile charging approach for effective charging of EV (electrical vehicle). It includes various charging plans like G2V (grid-to-vehicle) and V2V (vehicle-to-vehicle). ...
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The emergence and massive growth of cloud computing increased the demand for task scheduling strategies to utilize the full potential of virtualization technology. Efficient task scheduling necessitates efficiency, reduced makespan and execution time, and improvement ratio. Additionally, secure scheduling is a pivotal element in highly distributed environments. Task scheduling is an NP-complete problem where the time required to locate the resource depends on the problem size. Despite the several proposed algorithms, optimal task scheduling lacks an ideal solution and requires further efforts from academia and industry. Recently, blockchain has evolved as a promising technology for combining cloud clusters, secure cloud transactions, data access, and application codes. This study leverages the advantages of blockchain to propose a novel encoding technique to improve the makespan value and scheduling time. The proposed algorithm is an optimal solution for effective and efficient job shop scheduling where an Improved Particle Swarm Optimization (IPSO) and blockchain technology is used to provide efficiency and security. IPSO algorithm is hybridized by acquiring the best data from methods, and selective particles are kept for further iteration generation. The IPSO algorithm effectively traverses to the solution space and obtains optimal solutions by altering the dominant operations. The performance of IPSO is evaluated concerning the makespan, improvement ratio, execution time, and efficiency. Experiment results indicate that the proposed algorithm is practical and secure in handling flexible job scheduling, and outperforms the state-of-the-art task scheduling algorithms. Results suggest that IPSO minimizes the execution time by 8% and increases the efficiency by 35% than the existing scheduling approaches.
... Nowadays, people prefer to travel or opt for EVs due to their various benefits such as low cost charging, reduced air pollution, and low maintenance 3 4 . However, many researchers around the world have discussed the EVs charging schemes via conventional energy sources, which is highly effected by the security and privacy issues such as cyber-attacks, denial of service (DoS), etc. 5 and also cause air pollution. These vulnerabilities prevent users from using and utilizing the EVs charging schemes. ...
The adaptation of intelligent transportation system has evolved the quality of life of people with the huge demand for electric vehicles. Moreover, it becomes essential to schedule an electric vehicle for charging optimally. Therefore, this paper proposes a blockchain-based electric vehicle charging reservation scheme for optimum pricing. It primarily aims to secure data transactions between electric vehicles and charging stations. It uses the communication channel as 5G to ensure ultra-low latency and extremely high reliability. Furthermore, the proposed scheme uses a double auction mechanism to optimize the payoff for both electric vehicles and charging stations. The performance of the proposed scheme is evaluated by distinguishing it from the conventional networks such as 4G and LTE-A. The performance parameters are considered profit and loss for electric vehicles, scalability, cost overhead for data storage, communication overhead for data transactions, and data storage cost. Results show that the proposed scheme is secure and achieves the optimized payoff for electric vehicles and charging stations compared to traditional approaches.
... For instance, blockchain technology can be utilized in the network layer by exchanging data of vehicles and infrastructures to each other in a secure manner. Examples of such exchanged information are the state of charge of the vehicle, distance to charging station, and type of batteries that can be utilized in blockchain-based EV charging management [120,121]. Moreover, in the application layer, blockchain can be utilized for making contracts, payments, settlements, and so forth [119,122,123] [124,125]. ...
The average energy efficiency of internal combustion engine (ICE) automobiles for converting the fuel energy into forwarding motion, is between 10 and 20%. The rest of the fuel energy is dissipated in terms of heat or ejected into the air, which represents almost 80–90% of wasting fuel. Thus, more efficient vehicles with further developed performance are required to reduce fuel consumption and greenhouse gas emissions. Transportation electrification is a well‐accepted approach that can significantly decrease oil reliance, fuel dissipation, and environmental effects in transportation systems. Policymakers in numerous countries have been progressively building up targets for moving toward transportation electrification. This chapter presents an overview of the diffident aspects of the electrification of transportation with a focus on urban transport. The chapter includes the trend of electrification of transportation in the world and in particular, leading countries. The opportunities and challenges of transportation electrification are reviewed in the chapter from several viewpoints. Moreover, the key technological developments of electrified vehicles and transportation systems are discussed in this chapter.
... A robust, integrated, and resilient charging network is essential to the growth and deployment of the Internet of Electic Vehicles (IoEV) [11]. Javed et al. [12] proposed a solution for the secured scheduling of the charging system using blockchain technology. They introduced vehicle-to-vehicle and vehicle-to-grid charging strategies. ...
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The world is moving rapidly from carbon-producing vehicles to green transportation systems. Electric vehicles (EV) are a big step towards a friendly mode of transport. With the constant rise in the number of electric vehicles, we need a widespread and seamless charging infrastructure that supports seamless charging and billing. Some users generate electricity using solar panels and charge their electric vehicles. In contrast, some use charging stations, and they pay for vehicle charging. This raises the question of trust and transparency. There are many countries where laws are not strictly enforced to prevent fraud in payment systems. One of the preeminent problems presently existing with any of the trading systems is the lack of transparency. The service provider can overcharge the customer. Blockchain is a modern-day solution that mitigates trust and privacy issues. We have proposed a peer-to-peer energy trading and charging payment system for electric vehicles based on blockchain technology. Users who have excess electricity which they can sell to the charging stations through smart contracts. Electric vehicle users can pay the charging bills through electronic wallets. We have developed the electric vehicle’s automatic-payment system using the open-source platform Hyperledger fabric. The proposed system will reduce human interaction and increase trust, transparency, and privacy among EV participants. We have analyzed the resource utilization and also performed average transaction latency and throughput evaluation. This system can be helpful for the policymakers of smart cities.
Recently, the deployment of electric vehicles supply equipment (EVSE) and its market is expanding rapidly to support the massive penetration of electric vehicles (EVs). However, to accomplish an effective EV charging mechanism for urban prosumer communities, it is imperative to tackle the challenges of distinct energy generation among the communities, dependency of the total purchasable energy price of each EV based on the distance between EV and EVSE, and extreme uncertainty among the energy demand and generation. Therefore, in this paper, the problem of EV charging of urban prosumer communities is studied. In particular, a joint optimization problem is proposed to maximize both the social welfare and EV charging achieved rate of the considered urban prosumer communities. Consequently, the formulated problem is decomposed into 1) truthful double auction problem for determining the unit price and winners by maximizing social welfare, and 2) EV auction losers charging problem for improving EVs charging achieved rate by purchasing energy from the power grid. Then the breakeven-based double auction (BDA) mechanism is proposed to find the unit price and EV winners’ for charging. Sequentially, a multi-agent deep reinforcement learning-based asynchronous advantage actor-critic algorithm with a long short-term memory layer (A3C-LSTM) is adopted to achieve the optimal grid energy buying decision for ensuring the charging of the losers. Finally, the experimental results demonstrate the efficacy of the proposed model that can increase the number of EV charging up to 57.31%, and prosumer communities have gained 86.04% of their income compared to baseline methods.
The rapid emergence of the technologies has almost redesigned every industry including agriculture. Nowadays, agricultural practices are done by statistical and quantitative approaches. It is important to protect the data which are collected in the agricultural sector. Blockchain is one of the promising technologies that are used for the data encryption. Blockchain is used to store the transaction data. A huge amount of data is stored in IPFS, which ensures scalability and data confidentiality. It also ensures data privacy with the data sharing mechanism. The data collected from the agricultural sectors like moisture content of the soil, temperature, and crop status that are obtained from the IoT sensors on the agricultural land, other details such as previous year agricultural records, details of the agricultural land, yield, logistics, and so on are passed to the blockchain and so that the data cannot be used for malpractices. These data are helpful for the farmers to cultivate the crops according to their land conditions. This data can also be analyzed so that the farmer can get an estimate from the government and insurance organizations to meet his needs. Interplanetary File System (IPFS) is used for secure storage of this large quantity of information. This chapter presents a completely secure blockchain‐based framework for smart farming using IoT sensors and a farmer support system to meet certain necessities of the farmer.
Charging infrastructure is a key factor in successful electric vehicle adoption. Charging stations are still a fragmented market in terms of ownership, lack of standards, and charging protocols. The increasing decentralised grid has made energy and communication flow bi-directional. Challenges arise in maintaining the increasing decentralised structure, security, and privacy of the network. Blockchain facilitates the interconnectedness of such a distributed and decentralised network. Blockchain's versatility lies in its transparent and immutable decentralized architecture that enables direct transactions between users without the need of a middleman. It provides powerful safeguards against cyberattacks with its advanced cryptography enabling privacy-preserving authentication. This chapter presents a comprehensive review on the application of blockchain technology in EV charging infrastructure such as facilitating the peer-to-peer energy exchange, increased security and privacy, immutable transactions, and mitigating trust issues among the participants in the charging infrastructure.
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This thesis examines the use of blockchain technology with the Electric Vehicles (EVs) to tackle different issues related to the existing systems like privacy, security, lack of trust, etc., and to promote transparency, data immutability and tamper proof nature. Moreover, in this study, a new and improved charging strategy, termed as Mobile vehicle-to-Vehicle (M2V) charging strategy, is used to charge the EVs. It is further compared with conventional Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies to prove its efficacy. In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner to remove the intermediary parties and deal with the issues related to them. Moreover, to store the data related to traffic, roads and weather conditions, a Transport System Information Unit (TSIU) is used, which helps in reducing road congestion and minimizing road side accidents. In TSIU, InterPlanetary File System (IPFS) is utilized to store the data in a secured manner. Furthermore, mathematical formulation of the total charging cost, the shortest distance between EVs and charging entities, and the time taken to traverse the shortest distance and to charge the vehicles is done using real time data of EVs. The phenomena of range anxiety and coordination at the crossroads are also dealt with in the study. Moving ahead, edge service providers are introduced to ensure efficient service provisioning. These nodes ensure smooth communication with EVs for successful service provisioning. A caching system is also introduced at the edge nodes to store frequently used services. The power flow and the related energy losses for G2V, V2V and M2V charging strategies are also discussed in this work. In addition, an incentive provisioning mechanism is proposed on the basis of timely delivery of credible messages, which further promotes users’ participation. Furthermore, a hybrid blockchain based vehicular announcement scheme is proposed through which secure and reliable announcement dissemination is realized. In addition, IOTA Tangle is used, which ensures decentralization of the system. The real identities of the vehicles are hidden using the pseudo identities generated through an Elliptic Curve Cryptography (ECC) based pseudonym update mechanism. Moreover, the lightweight trustworthiness verification of vehicles is performed using a Cuckoo Filter (CF). It also prevents revealing the reputation values given to the vehicles upon information dissemination. To reduce the delays caused due to inefficient digital signature verification, transactions are verified in the form of batches. Furthermore, a blockchain based revocation transparency enabled data-oriented trust model is proposed. Password Authenticated Key Exchange by Juggling (J-PAKE) scheme is used in the proposed model to enable mutual authentication. To prevent collusion attacks, message credibility check is performed using Real-time Message Content Validation (RMCV) scheme. Furthermore, K-anonymity algorithm is used to anonymize the reputation data and prevent privacy leakage by restricting the identification of the predictable patterns present in the reputation data. To enable revocation transparency, a Proof of Revocation (PoR) is designed for the revoked vehicles. The vehicle records are stored in IPFS. To enhance the chances of correct information dissemination, incentives are provided to the vehicles using a reputation based incentive mechanism. To check the robustness of the proposed model, attacker models are designed and tested against different attacks including selfish mining attack, double spending attack, etc. To prove the efficiency of the proposed work, extensive simulations are performed. The simulation results prove that the proposed study achieves high success in making EVs energy efficient, secure and robust. Furthermore, the security analysis of the smart contracts used in the proposed work is performed using Oyente, which exhibits the secure nature of the proposed work.
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The electric vehicle (EV) charging scheme can reduce the power generation costs and improve the smart grid resilience. However, huge penetrations of EVs can impact the voltage stability and operating costs. In this paper, a novel EV participation charging scheme is proposed for a decentralized blockchain enabled smart grid system. Our objectives are to minimize the power fluctuation level in the grid network and the overall charging cost for EV users. We first formulate the power fluctuation level problem of the smart grid system that take into accounts of EV battery capacities, charging rates and EV users charging behavior. And then we propose a novel adaptive blockchain-based electric vehicle participation (AdBEV) scheme that uses Iceberg order execution algorithm to obtain an improved EV charging and discharging schedule. The simulation results show the proposed scheme outperforms the scheme that applying Genetic Algorithm (GA) approach in term of lowering the power fluctuation level and overall charging costs.
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The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs) and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the LNSC. The overall model involves registration, scheduling, authentication and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.
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Upgrading the internal combustion engine (ICE) driven cars to the electric vehicles (EVs) offers the opportunity to reduce the fossil fuel consumption, emission rates and total driving costs. However, the large scale utilization of the EVs introduces a stochastic load demand to the power grid. The effect of EVs charging demand on the distribution network operation should be investigated properly. This paper proposed a novel model to study the effects of power exchange between the grid and EVs on the power system demand profile, the operation stability index, and the reliability indices. To this end, the operation instability indices are introduced by the range and standard deviation of the load factor of the network components to evaluate the system stability. Further, the CAIFI, 1 SAIDI, 2 SAIFI, 3 and ASAI 4 reliability indices are calculated for various vehicle-to-grid (V2G) and grid-to-vehicle (G2V) power level to estimate the impact of different level of power exchange on system reliability. We introduced an EV charging scheduling approach which considers the specification of Li-Ion battery and the limitations for increasing battery life. The power exchange profile for V2G is also calculated using the constant power method to discharge the energy at different levels for times which cars are parked at the workplace. Due to the stochastic nature of EVs, the minimal path method is used to compute the stability and reliability parameters and the backward–forward algorithm is used to load flow analysis. The proposed model is evaluated via modified IEEE 33-bus test system.
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With the rapid development of electric vehicles (EVs), the consequent charging demand represents a significant new load on the power grids. The huge number of high-rise buildings in big cities and modern technological advances have created conditions to mount on-site wind power generators on the buildings. Since modern buildings are usually equipped with large parking lots for EVs, it shows vital practical significance to utilize the on-site wind power generation to charge EVs parked in the buildings. In this paper, we first use a case study in Beijing to show that the on-site wind power generation of highrise buildings can potentially support all the EVs in the city. Considering that the charging demand of EVs usually doesn’t align with the uncertain wind power, the coordination of EV charging with the locally generated wind power in a microgrid of buildings is investigated and three main contributions are made. First, we investigate the problem and formulate it as a Markov decision process (MDP), which incorporates the random driving requirements of EVs among the buildings. Second, we develop a distributed simulation-based policy improvement (DSBPI) method, which can improve from heuristic and experiencebased policies. Third, the performance of the distributed policy improvement method is proved. We compare DSBPI with a central version method on two case studies. The DSBPI method demonstrates good performance and scalability.
“Range anxiety,” defined as the psychological anxiety a consumer experiences in response to the limited range of an electric vehicle, continues to be labelled and presented as one of the most pressing barriers to their mainstream diffusion. As a result, academia, policymakers and even industry have focused on addressing the range anxiety barrier in order to accelerate adoption. Much literature recognizes that range anxiety is increasingly psychological, rather than technical, in its nature. However, we argue in this paper that even psychological and technical explanations are incomplete. We examine range anxiety through Hirschman’s Rhetoric of Reaction, which supposes that conservative forces may oppose change by propagating theses related to jeopardy, perversity, and futility. To do so, we use three qualitative methods to understand the role of range anxiety triangulated via a variety of perspectives: 227 semi-structured interviews with experts at 201 institutions, a survey with nearly 5000 respondents, and 8 focus groups, all across 17 cities in the five Nordic countries. We find evidence where consumers and experts use and perpetuate the rhetoric of reaction, particularly the jeopardy thesis. We conclude with a reexamination of the policies geared to assuage range-based barriers, which a construction of range anxiety as a rhetorical excuse would render as ineffective or inefficient, as well as future implications for diffusion theory.
In this paper, we propose an optimal charging scheduling algorithm for hybrid vehicle charging scenarios. Unlike traditional charging scheduling algorithms, which only consider the vehicle-to-vehicle (V2V) and grid-to-vehicle (G2V) scenarios, the new and hybrid charging scenario including the emerging mobile charging vehicles (MCV), i.e. mobile charging vehicle-to-vehicle (MCV2V), G2V and V2V is considered in this paper. Moreover, the proposed optimal charging scheduling framework based on consortium blockchains ensures the security and privacy of electricity trading. The proposed scheduling algorithm is based on a double-objective optimization model aiming at maximizing user's satisfaction and minimizing users’ cost, while considering diverse metrics like location of charging and discharging entities, the time of waiting, and driving speed of EVs, etc. In order to solve the optimization model, an improved Non-dominated Sorting Genetic Algorithm (NSGA) is proposed. Experiments based on the real map of Beijing is done to evaluate the performance of proposed scheduling algorithm. The results show that the proposed algorithm can achieve better performance in terms of user's satisfaction and user's cost comparing with V2V based algorithm and G2V based algorithm.
Intelligent vehicle (IV) is an internet-enabled vehicle, commonly referred to as a self-driving car, which enables vehicles-to-everything communications. This communication environment is not secure and has several vulnerabilities. The major issues in IV communication are trustworthiness, accuracy, and security of received and broadcasted data in the communication channel. In this article, we introduce blockchain technology to build trust and reliability in peer-to-peer networks with topologies similar to IV communication. Further, we propose a blockchain-technology-enabled IV communication use case. Blockchain technology is used to build a secure, trusted environment for IV communication. This trusted environment provides a secure, distributed, and decentralized mechanism for communication between IVs, without sharing their personal information in the intelligent transportation system. Our proposed method comprises of a local dynamic blockchain (LDB) and main blockchain, enabled with a secure and unique crypto ID called intelligent vehicle trust point (IVTP). The IVTP ensures trustworthiness among vehicles. Vehicles use and verify the IVTP with the LDB to communicate with other vehicles. For evaluation, we simulated our proposed blockchain technology-based IV communication in a common intersection deadlock use case. The performance of the traditional blockchain is evaluated with emphasis on real-time traffic scenarios. We also introduce LDB branching, along with a branching and un-branching algorithm for automating the branching process for IV communication.
Significant increase in the installation and penetration of Renewable Energy Resources (RES) has raised intermittency and variability issues in the electric power grid. Solutions based on fast-response energy storage can be costly, especially when dealing with higher renewable energy penetration rate. The rising popularity of electric vehicles (EVs) also brings challenges to the system planning, dispatching and operation. Due to the random dynamic nature of electric vehicle charging and routing, electric vehicle load can be challenging to the power distribution operators and utilities. The bright side is that the load of EV charging is shiftable and can be leveraged to reduce the variability of renewable energy by consuming it locally. In this paper, we proposed a real-time system that incorporates the concepts of prioritization and cryptocurrency, named SMERCOIN, to incentivize electric vehicle users to collectively charge with a renewable energy-friendly schedule. The system implements a ranking scheme by giving charging priority to users with a better renewable energy usage history. By incorporating a blockchain-based cryptocurrency component, the system can incentivize user with monetary and non-monetary means in a flat-rate system. The effectiveness of the system mechanism has been verified by both numerical simulations and experiments. The system experiment has been implemented on campus of the University of California, Los Angeles (UCLA) for 15 months and the results show that the usage of solar energy has increased significantly.
Plug-in electrical vehicles (PEVs) are introduced as a compatible transportation system for the environment. Manufacturing technology of electric vehicles (EVs) could bring an opportunity to implement them as energy storages. By expanding the use of renewable energy sources (RESs), the role of energy storage system is highlighted to overcome power generation fluctuations. Integrating PEVs and RESs could be profitable for both PEV and RESs owners. In this paper, a structure of renewable energy sources based micro grid (RMG) is considered. The proposed RMG has been equipped with a parking lot in order to control and aggregate PEVs. This paper investigates the optimal energy management problem of the RMG with the presence of PEVs. The objective of the RMG owner is to minimize the cost through generating power with its local generators and trading energy with the power market considering the market price. Also, the RMG could incentive PEV owners to take part in the demand response (DR) programs as a flexible load. It could bring profit for both PEVs and RMG owners. The existence uncertainties are modeled in the scenario-based framework. Three case studies are analyzed to display the effectiveness of the proposed model. As a result, utilization of the parking lot has decreased the cost of RMG about 40%. Additionally, implementing DR program during the charging process of PEVs could bring extra profit for both RMG and PEVs owners. The results have shown the efficiency of the proposed method.
We propose a localized Peer-to-Peer (P2P) electricity trading model for locally buying and selling electricity among Plug-in Hybrid Electric Vehicles (PHEVs) in smart grids. Unlike traditional schemes, that transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.