A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Abstract

Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. In recent years, privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. Therefore, in this article, a comprehensive literature review is proposed to analyze existing EVs energy management challenges and solutions and present potential future research directions for EVs charging/discharging coordination applications.

1. Introduction

The rapid growth of the number of moving vehicles has resulted in a drastic increase in air contamination in large-populated areas. According to a report by the Environment and Climate Change Canada (2022), the oil and gas and the transport sectors were responsible for the highest greenhouse gas emissions [1], as shown in Figure 1. EVs, in contrast, are considered a viable alternative for reducing carbon dioxide (CO₂) as well as other greenhouse gases. Therefore, the use of EVs should be encouraged by promoting the advantages over conventional vehicles, including zero emissions, a cleaner environment, lower fuel cost, comfort, and better driving experience, etc., to protect the environmental sustainability [2].

Building a suitable transportation infrastructure for EVs is one fundamental and conducive factor in enabling the all-embracing use of EVs, while the number of EV owners is growing, coordinating the charging station (CS) foundation with the continuous improvement of the EV industry is essential [3,4]. The development of EVs introduces a new research area to achieve effective solutions for finding proper places to install CSs and to explore convenient location planning [5]. Designing an optimal geographic distribution of CSs can minimize the traveling and queuing time [6]. The lack of an ideal distribution mechanism would decrease EV drivers’ satisfaction by facing the longer waiting-time experience in the CSs.

However, since improving the CS infrastructure and deploying new stations and chargers are time-consuming and high-priced, the key concern is to address EVs charging demands without waiting for future facilities. Therefore, to better guide and coordinate the frequent charging requirement of EVs, developing intelligent charging scheduling schemes can provide satisfaction for EV owners [7]. EVs might be utilized by the general public for personal driving or by commercial fleets, such as buses, taxis, and ridesharing. Furthermore, EVs need a longer time to become fully charged (0.5–10 h); thus, charging service congestion might occur at CSs while several EVs are queuing and waiting for a charging slot [8]. Taking into account, different usage charging demands, researchers should implement customized charging management strategies according to the purpose of travel. For personal EVs, factors including driving distance, EV charging capacity and remaining (State of Charging (SOC)), charging cost, etc., can have an impact on the allocation of efficient CSs [9,10]. On the other hand, scheduling strategies for commercial EVs should mainly focus on maximizing the on-road service times and driving cycles with continuous driving to prevent a reduction in the service profits and daily net revenue [11].

The inadequacy of the low growth rate of CSs, with respect to EVs, should be addressed before deploying large-scale EVs on the road. On the other hand, such a significant surge of EVs can result in power grid overloading and quality degradation [12]. To widen the customer adoption of EVs, potential EV buyers should be ensured not to end up fully discharged on the road with no CSs around them or in a long queue to get charged. As an alternative solution, researchers proposed a new approach of electric vehicle-to-vehicle (V2V) energy transferring, which is also termed Peer-to-Peer (P2P) charging, to mainly provide a feasible EV fueling method that will be beneficial for the utility grid and EV owners [13]. This indirect energy management strategy by the power grid can be a promising solution to accommodate recently added loads, especially during peak times [14].

In order to have an accurate energy management system, providing efficient communications to exchange more comprehensive and related features of EVs, energy suppliers, power distribution networks, and road networks as a whole is necessary. However, data owners are not comfortable exposing their personal data to the system to protect their privacy. To deal with this issue, newly proposed systems need to apply rigorous data security algorithms to keep the users’ information safe. Among several designed security algorithms, Federated Learning (FL) and the blockchain have gained considerable attention. The main idea of the FL, which was originally introduced to the world by Google Artificial Intelligence (AI) [15,16], is to build distributed paradigms based on machine learning techniques considering distributed data sets that are cross-platformed among multiple items (entities) whilst preventing information leakage. In other words, data owners can keep their private data yet still participate in collaborative learning strategies [17]. The other approach to reduce security threats to the private information of data owners is based on the blockchain technology, which uses a complex system to record information in an electronic public ledger consisting of transactions that are distributed and duplicated throughout the blockchain system. Every participating node inside the network receives a copy of the public ledger hence, making it impossible to penetrate the network and forge the stored data [18].

1.1. Research Motivations

Further achieving an intelligent transportation system with sufficient energy resources allocation and distribution considering user privacy requires reviewing the constantly-increasing literature studies. In

Table 1. Comparison of some of the existing literature reviews covering EV energy management mechanisms.

Ref.	Benefits	Limitations
[19]	A systematic review of on-the-move EVs charging issues Wireless charging transfer between EVs	No consideration of data security Steady distance limitation
[3]	Reviewing CSs infrastructure Studying the effects of fast CSs on the grid	Emphasizing on the CSs only
[20]	Summarizing existing methods for EV charging batteries	Not a comprehensive review
[4]	CSs infrastructure modeling with location optimization	Neglecting other factors impacting EVs energy management
[21]	Analyzing V2G and G2V electricity transferring concerns	No consideration of data security Focusing on V2G only to control the load balancing
[22]	Presenting an overview of current EVs charging and battery improvements and technologies	Various charging types and technologies are covered only Not discussing other important factors impacting EVs energy management
[23]	Summarizing EVs power trading challenges and methods by applying blockchain in P2P communication	Missing other security approaches, e.g., FL
[24]	Exploring security challenges in V2G communication	V2X security approaches are missing

, we present a summary to some extent from existing literature reviews in which different aspects of EV energy management systems are analyzed and reviewed.

As can be seen from the comparison table, there are some gaps in existing literature reviews as they focus on a single factor affecting EVs advancement and improvement (CS infrastructure, power grid load forecasting, etc.). Effectively, in the present paper, we aim to study various literature related to the most critical factors of enhancing EVs QoE by covering the missing scientific gaps that we explored from existing research. We present a comprehensive review of the energy and charging management for EVs to facilitate the substitution of standard internal combustion engine (ICE) vehicles with recent environment-friendly EVs. This article plans to study recent applications and research on EVs towards facilitating higher user satisfaction. We believe in order to widen the popularity of EVs among drivers, researchers can benefit from a systematic survey paper covering current challenges that EVs are encountering, such as driving range restrictions, insufficient number of CSs, data privacy, energy trading issues, and so on. This survey is focused on providing a review of the previous charging-demand-response approaches along with analyzing privacy-related features. This review also examines open research topics and issues existing in each of the energy management areas. In particular, the primary contributions introduced in this survey are outlined as follows:

• It delivers a summary and overview of the various EVs energy management approaches, including optimized CSs location indicators, EVs charging planning, charging point recommendation, and EV energy trading models.
• It discusses the significance of data security within EV networks and the energy suppliers to promote EV deployment and proposes FL and blockchain technology as effective approaches.
• It discusses the critical challenges for charging management applications of EVs by considering both EV owners’ benefits and the power grid capacity.
• It proposes a discussion about the benefits, limitations, and drawbacks of existing studies and provides guidelines for future investigations and areas of research.

1.2. Research Preparation Methodology

In this section, we provide some explanations to show the validity of our presented survey paper. For the purpose of developing a practical survey paper, we planned to concentrate on the year-of-publication criteria and included literature from 2018 to the current date. We believe due to the high-speed and dynamic technological advancements happening in the EVs development, previous studies might not have paid attention to some of the recent issues/solutions.

Another metric considered in this review paper is the inclusion of previous studies in the trending topics about EVs, while capturing eligible papers, we defined a clear research question and used different logical terms (AND, OR) to identify relevant papers. We intended to propose a systematic review paper seeking to investigate research gaps and conflicting results and summarizing recent practical studies to allow a more comprehensive understanding of the significance of EVs energy management systems.

The remaining sections of the paper are organized as follows. Section 2 covers a detailed review of existing strategies for optimally placing CSs to increase accessibility and profit. Section 3 broadly classifies different charging scheduling, and charging point recommendation approaches based on EV types. To better secure our system, Section 4 presents previous studies of enhancing the security of networks, including connected EVs to other entities in order to manage energy. Energy exchanging and trading approaches between EVs and other public/private power suppliers are reviewed in Section 5. In Section 6, secure payment techniques proposed for EVs P2P energy trading are described. Section 7 highlights the challenges and future areas of research. Lastly, this study is concluded by indicating the research outcomes in Section 8.

2. Charging Stations Distribution

Proper placement of CSs can improve station accessibility and, consequently, increase public acceptance of EVs. It can also mitigate the power grid instability issue with the current ever-escalating energy demand [25]. Although EVs charging stations are placed in the transportation network, optimal CS placement approaches should take into account not violating the safe limit of power distribution network parameters (without exceeding a given voltage drop) [26,27]. Survey [28] stated participants’ preferences for the distance between charging stations. Accordingly, the favored average distance was estimated to be 0.12 km lower than the placement distance between conventional gas points or, on average, 5 km. According to the report published on July 2021 from Virta.global company, in most countries, a passenger vehicle’s average electricity consumption is approximately 0.2 kWh per kilometer [29]. Thus, the coordination between CSs power capacity in the transportation sector and distribution network must be organized [30].

The aforementioned aspects of CSs placement make the formulation and optimization of the solutions a challenging task [31]. Successfully completing this task requires the collection of initial information, including energy distribution network data, transportation network data, road and energy sector limitation metrics, such as the quantity of fast/slow charging plugs installed at stations, the upper safe limit of newly added load to the energy network, etc. [32,33,34]. An optimal CSs distribution method should tactically formulate the distribution problem and efficiently employ functional algorithms. Many studies have been conducted to facilitate easy access to charging points for EV users by identifying the proper siting of charging points utilizing methods, such as genetic algorithm [35], fuzzy neural network [36], linear programming [6,37], and so on. Accordingly, one recently proposed solution tried to optimally combine Chicken Swarm Optimization along with Teaching Learning Based Optimization algorithms to exploit the essential properties of each algorithm in order to increase the introduced solution’s efficacy [38]. They framed the problem in a multi-objective model considering various factors, such as EV users’ satisfaction and convenience, road traffic, power grid voltage stability, reliability and power loss, economical and cost elements, etc.

While designing an optimal algorithm to distribute CSs, other supplementary social and construction factors, such as investment costs, maintenance and operating expenditures, rural or urban areas placement, population density, etc., should also be considered to be able to prove that the introduced model is effective and also feasible to implement [39]. For instance, to address the need to establish service area CSs on the route of suburbs of cities, the combination of Asymmetric Nash Negotiation and Hybrid Binary Particle Swarm Optimization algorithms can determine regions’ EVs charging demands and CSs service range conditions [40].

In [41], a model for charging points spatial pattern investigation was proposed. This literature integrated the Bayesian spacial log-Gaussian Cox technique and intensity surface of charging point positioning prediction to formulate a maximize coverage location model for a two-step optimum charging point deployment. The necessity of developing an intelligent charging point placement is highlighted in [42,43] references, wherein the avoidance of power network grid instability problem is studied. The optimal charging point connections are modeled based on Monte Carlo simulation and multi-objective optimization algorithms, considering traffic and grid capacity, regulations, and costs.

3. Charging Scheduling and Charging Station Selection

Charging batteries embedded in EVs in uncontrolled manners can be detrimental to the battery life [44]. Furthermore, in addition to technological aspects, scheduling the correct time and amount of charging at the nearest available charging point can increase EVs Quality of Experience (QoE) among current, and future users [22]. There are many studies developing methods to estimate EVs driving range relying on details displaying the remaining amount of energy in the battery (state of charging), and external factors affecting the energy consumption, e.g., air conditioning. These studies can be organized into two primary groups of fact-based prediction, and paradigm-based prediction methods [45,46]. Historical energy consumption information captured from an EV characteristic from previous journeys or during a trip is used to estimate the range. History-based prediction models have limited accuracy as they only take into account the last-miles driving energy consumption data and neglect the influence of road and environment conditions and the driving styles on the energy consumption [47]. On the contrary, model-based predictions develop mathematical models to calculate the future energy consumption based on dynamic parameters of the vehicles (route information, speed limits, driving styles, etc.) while driving; hence, this estimated range value may change during the journey [48,49].

Therefore, developing advanced solutions to address charging services scheduling for parked/on-the-move EVs and optimally CS recommendations with the least queuing time will enable sustainable EV adoption by the public.

3.1. Personal Electric Vehicles

An effective CS suggestion model needs information from each individual parties including, EV charging status and location, some of the users’ personal data, and also details about CSs [50]. The majority of previous literature tried to solve the issue of finding the closest available CS based on the requester’s state of charging and location [51]. The research in [33] explored the time of charging for EVs that are located and parked in CSs, and they proposed optimal CSs with minimum service waiting time in order to increase the QoE. Another reference, [52] performed a decision-making composite model by integrating assessment theory for measurable factors, such as charging duration time, battery monitoring, etc.

Combining different sides (EVs, CSs) properties can also lead to more accurate recommendations. Several techniques have been proposed in applications where EVs users require notifications for the right time of charging, state of battery, nearest charging points, and so on [26,53]. However, they ask for users’ personal information from both sides. Prioritizing EVs demands for charging scheduling and refiling is another research topic wherein several mechanisms and algorithms have been designed to improve factors, including energy demand-respond balance [48].

3.2. Electric Taxis

Electric taxis, which are becoming more popular, have different demands than personal EVs. One primary metric for e-taxis is to locate a fast-charging station in the nearest location considering the profit maximization, especially during rush hours [11,54]. Zhang et al. [39] proposed a recommendation strategy to assign e-taxis the best charging location at the best time. For the charging-time modeling, they computed factors such as e-taxi unit time revenue, charging capacity, charging process duration, and time-of-use electricity cost. Charging location modeling, on the contrary, needed the computation of other factors, namely driving duration, queuing time, charging capacity, and charging time. A real-time e-taxis charging point locator model was proposed in [55] based on wide-ranging GPS data processing utilizing e-taxis’ history of recharging and real-time GPS directions. This reference aimed to minimize the recharging initialization period considering travel distances, charging cycles, and idleness in stations [56].

3.3. Electric Buses

Another category of EVs, which is e-buses, requires more technological advancements to become adopted widely by many governments. These public vehicles may encounter some prevailing issues, such as longer charging time, uneven and deficient spatial charging facilities distribution, highly dynamic operation factors, and so on. To address large-scale e-bus fleets further promotion, researchers need to investigate regional e-bus lines/stops networks to analyze operational and charging patterns for real-time charging scheduling development [57,58]. Currently, e-buses operating and charging schedules are managed with fixed timetables; however, such offline solutions might not always perform optimally. Dynamic factors, including unpredictable traffic congestion, changing weather/temperature, traffic-light conditions, etc., will affect e-buses performance and will make the optimal strategy creation challenging [59,60,61]. E-bus battery sizing, which is sensitive to its transit service type (duration and roads to take), is another challenging topic since it influences both the range and cost of driving a bus. For instance, findings from a case study in [62] reveal that designated batteries for electric city buses are unnecessarily oversized, considering the regional situations with mild temperatures and short trips.

4. Electric Vehicles Data Security

Although previously reviewed studies have enhanced the functioning of the recommender model, they did not consider the data producers’ willingness to expose their personal information to other entities as there are some confidentiality concerns.

4.1. Federated Learning

Figure 2 illustrates the basic architecture for a FL system used in the EVs energy sector data exchange.

One of the essential features of FL is privacy. There are some privacy techniques used in FL that can provide meaningful privacy guarantees. Differential Privacy is one of the security models, which is also known as k-Anonymity. This method adds noise to the data to hide sensitive information from other entities to make them incapable of restoring the data [63]. Another line of work is Secure Multi-party Computation (SMC) which provides a data security framework to ensure complete zero-knowledge among parties except for input/output data. This model involves complicated computation protocols to guarantee high security with the cost of inefficiency [64]. Homomorphic Encryption is too adopted in FL to secure users’ private data by exchanging training model parameters under an encryption mechanism. In this model, neither data nor the training model itself are not transmitted. Homomorphic Encryption is widely used for training data on the cloud as it provides data-encryption for entities who wish to share information into the cloud environments for data refinement [17,65].

The federate Learning technique is classified into three groups in accordance with the data-division properties [66]. Horizontal Federated Learning, which is termed as sample-based FL, is used in cases of datasets where samples are different but they share the same feature space. For instance, two branches of an insurance company may have different users (sample ID space), but the features in the business are similar [67]. In applications where one entity (EV) produces different sample data with the same features (driving duration, GPS, acceleration), the HFL technique can use the data samples in supervised ML methods to predict driving behavior by keeping EV users’ data private and safe. Implementing an HFL model is straightforward and does not require a complex algorithmic process. However, HFL is unable to operate properly where there are collaborations of multiple entities (EV, CS, and power grid) from which similar sample data are produced, but each has distinct features. On the other hand, in scenarios where similar data samples share different feature scopes, Vertical Federated Learning or feature-based FL is applicable [68]. For instance, an insurance company and a car-rental company datasets may likely include similar users residing in an area; therefore, the two companies’ sample ID spaces may have a large intersection, however, their feature spaces differ [69]. In order to use both parties’ data to process a computation, we need to build a model to collaboratively aggregate different features for similar samples. As it seems the implementation of VFL models is not as easy as HFL since one extra step is required to perform entity alignment between participants. Therefore, more complex processes with higher computational complexity are included to integrate distinct entities in a ML model considering user data protection. The last category defines a scenario in which datasets are distinguished in both sample ID space and feature space. Federated Transfer Learning can be applied to an example of two different companies located in geographically distributed areas with a small intercession among user groups. FTL, which is inspired by the transfer learning model, aims to provide ML approaches in cases where entities suffer from insufficient data samples. For instance, some data are available from a domain (electric bus) that can be used in a prediction model in another EV domain with a limited amount of available data [70].

Optimizing the large-scale communication bandwidth between entities and the aggregator server is necessary among all FL models [71]. Furthermore, FL models are required to provide security for the central server to protect model parameter aggregation [72].

In [73], FL was used to predict EVs network energy demand. They proposed an energy-demand learning-based prediction from the CSs side consideration, in which one central CS provider collects all CSs information and performs the learning process. Their model is based on FL; therefore, no private information was shared. To improve their model performance, the learning model was founded on the CSs grouping algorithm, which could enhance the accuracy of prediction and minimize the communication overhead. Authors in [74] proposed a real-time FL to predict autonomous vehicles steering wheel angle prediction. They included a sliding training window to minimize communication overhead and maximize real-time streaming data rate.

4.2. Blockchain

The initial introduction of Blockchain technology was in 2009 to describe the basis of developing the Bitcoin digital currency [75]. This can be another approach to reduce security threats to the private information of data owners [18,76].

The blockchain structure links each block to the previous block under a cryptographic signature algorithm; therefore, a sequential series of blocks is generated comprising transactions. Figure 3 demonstrates a block data structure and stored information. Before granting permission to a node to attach a newly generated block in the chain, an agreement on the validity of data must be reached among other nodes, which requires a certain effortful mechanism and particular conditions [77]. The consensus is necessary to guarantee authenticity and to replicate other blocks, and also to avoid forking (i.e., the probability of originating similar blocks by disparate nodes) [78].

Three main consensus protocols have been introduced to facilitate agreement among fully decentralized nodes by considering the validity of transactions. Proof of Work (PoW) performs computationally complex operations on each newly added block [79]. Nodes compete with each other to solve these complex operations, which is a cryptographic puzzle, to attach a new block into the blockchain. The purpose of this puzzle is to generate a hash value with several leading zeros that is lower than a target for the hash. The PoW guarantees immutability for the blockchain as to alter a block, all subsequent blocks must be altered, which is computationally infeasible. However, due to enormous computing power, it requires vast energy consumption with low transaction throughput [18]. To address the non-scalability and energy-intensive issues of PoW, Proof of Stake (PoS) protocol was presented as a substitute solution. In the PoS consensus algorithm, validators lock up a stake and are randomly selected based on the staking amount of the participating validators to attach a new block into the blockchain network [80]. PoS is considered a cleaner and faster protocol than PoW since it requires lower computation power and higher transaction throughput [81]. A symbolic comparison between these two protocols is shown in Figure 4. The other consensus protocol is called Delegated Proof of Stake (DPoS) wherein delegates vote for their favorite validators to generate new blocks in a blockchain network [82]. As each representative has the power to vote proportional to the amount of the stake in the network, this protocol is less likely to become centralized, and it is considered as the democratic version of the PoS protocol. Accordingly, due to the fact that DPoS needs less number of trusted nodes to verify data in each new block of the network chain, it can handle a higher number of transactions with faster confirmation times than PoW and PoS [83,84].

Alternative consensus protocols have been introduced, subject to each application criteria. However, all these protocols should be evaluated based on five key metrics [23,85], depicted in Figure 5:

• Scalability: Denotes to the capability of a consensus protocol to sustain the overall performance with the addition of nodes, transactions, and data [86].
• Energy Consumption: This metric is the key issue of blockchain limited widespread applicability. As an example, PoW consensus protocol has high energy consumption as the block miner selection requires massive computational power [87].
• Throughput: Denotes the number of transaction verification and deployment to the blockchain per second (Tps) [88]. For instance, Bitcoin’s throughput is approximately 7 Tps [89].
• Security: Indicates a consensus protocol resistance to various attacks. For instance, PoW-based models can crash by two major attacks, namely Denial of Service (DoS) and Sybil [90], therefore such a system should present feasible solutions to prevent malicious intrusion.
• Finality: Defines the determinism of the blockchain by ensuring that blocks cannot be reversed or changed purposefully once they are added to the chain [91].

In order to automate the execution of an agreement to receive a certain outcome among all participants, Smart Contracts are embedded into the blockchain network as simple computer programs to be executed after certain terms and conditions are met [92]. Smart contracts are sets of IF/WHEN-THEN rules written in codes that require an exact sequence of actions to execute predefined agreements. Once a transaction is complete, the blockchain will be updated, and consequently, the transaction will become unalterable [93].

Most of the previous works that utilized blockchain, tried to mitigate data leakage by saving local and global ML models in each active block [81,94]. This technique is believed to perform effectively as a safe information transfer solution [95]. However, it should be noted that participating nodes need to become equipped with high-performance storage devices. Furthermore, with the increment in the number of users (EVs, CSs), the blockchain-based model might encounter an adverse impact on its performance, which results in the model’s impracticality for real-time applications, wherein the ML outcomes generations are needed rapidly.

5. Electric Vehicles Energy Trading

Considering the mass penetration of EV industry in the upcoming years, the contribution of the transport sector to greenhouse gas emission, which primarily derives from the extremely burning fossil fuels, will be sharply minimized [2]. However, this amount of power consumption by EVs can lead to another problem by establishing uncontrolled charging demands on the power grids. Neglecting this newly raised issue may cause significant power distribution performance loss, especially during peak hours. Hence, mitigating the EV-development adverse impact on the main grid by proposing solutions implementing power load-balancing techniques is essential [24]. One practical way to support power resources management is to provide a platform allowing energy transfer from EVs to the grids (known as V2G), as well. This concept enables the participation of EV end-users and consumers as prosumers (energy consumer who is also a producer) in a demand-response network communication [96].

EV collaboration with the grid (charging/discharging mechanism) advanced into another stage where not only can EVs provide a two-way energy transferring mechanism with the grid, but they can establish V2X (Vehicle-to-Anything) interactions to exchange energy. Charging trading options are widely attracting researchers’ attention, therefore, forming a reliable network with various consumers and prosumers to simply perform energy trading operations is essential [82,97]. The P2P energy trading paradigm allows participants to trade electricity independent of the centralized institutions (e.g., utility companies) [98]. Figure 6 distinguishes a centralized energy trading model from a fully-decentralized mechanism. Researchers in [99] proposed an efficient charging data transmission model for V2V communication and charging services. They minimized communication overhead by applying mobile edge computing and utilized a reinforcement learning approach to dynamically select the best data delivery routing path in large-scale vehicular ad hoc networks. A more recently proposed framework in [12] proposed an efficient V2V energy trading mechanism enabling charging price optimization and efficient consumer/prosumer matching. This framework optimized EVs charging scheduling based on the electricity prices prediction and maximized EVs owners’ rationality by finding the best match. Other literature [100] proposed an energy trading model for P2P networks in which prosumers are incentivized within a smart grid distributed system. They proved that this single-sided auction-based model can mitigate the overall power demand from the main grid by motivating small providers to participate and maximize their profits.

By Integrating the blockchain technology into the energy-sharing mechanism among connected vehicular networks, literature [101] proposed a blockchain-based machine learning model to maximize the profitability of parked EVs based on a Game-theoretic stochastic bidding process. In [102], an alternative consensus mechanism based on the Hashgraph algorithm was proposed to replace high memory/time-consumption issues of blockchain consensus protocols for computationally constrained EVs. This mechanism uses a gossip synchronization protocol to set up V2V communications in a lightweight and fast way. The study presented in [103] was based on the blockchain and FL to develop a secure energy trading model among energy consumers and prosumers. They also worked on profit maximization by consistent advertisement using clustering and lookup mechanisms.

The author in [104] provided an energy sharing mechanism among EVs founded on the blockchain to allow a reliable and transparent model for a network including various connected agents, such as the power grid, charging point, energy maintenance institutes, etc. They implemented the Practical Byzantine Fault Tolerance (PBFT) agreement schema to lower the system complexity and enable it for real-world environments. The work in [105] presented a secure power trading transaction and communication mechanism among EVs based on the blockchain and smart contract properties. They designed an efficient agreement protocol using Elliptic Curve Cryptography (ECC) and also implemented a two-way power transferring mechanism among EVs and the smart grid to more efficiently manage the energy demand-response balance.

In addition to the previous studies, [106] introduced a blockchain-enabled system with a secure, automated, and transparent energy trading mechanism between EVs using Ethereum smart contracts. This system mainly focuses on increasing the fairness and competitiveness factors by designing a reversed auctioning mechanism between energy consumers/prosumers. They analyzed their security performance over the Ropsten dataset, which is the Ethereum official test network. Last but not least, the research in [107] offered a V2V power exchanging model utilizing the blockchain and fog computing technologies to maximize EVs users’ social welfare factor. To enhance their proposed model’s operation, they enhanced the PBFT and DPoS consensus protocols and, accordingly, designed the DoPSP agreement protocol with more efficient operation.

6. Blockchain-Based Payment Systems for Electric Vehicle Charging

The energy trading concept among EVs and other energy providers raises trust and fraud concerns in payment systems. In addition, proposing Peer-to-Peer (P2P) trading models might be restricted by some technical and regular barriers. One technical barrier can be the power grid hierarchical distribution network structure which flows from the main power facilities to the central division center and then moves to the low-voltage delivery points. Moreover, the district power network and energy industry regulations must be followed by all users who might affect P2P transactions [108,109]. Therefore, to mitigate overcharging, regulations, and privacy issues and to provide a transparent solution, P2P trading and payment systems based on blockchain are considerably practical to develop automatic auction, bidding, and payment systems through smart contracts [110].

Blockchain smart contracts (explained in more detail in Section 4.2) can provide a safe and practical platform to facilitate blockchain-based smart charging payment systems. They can be set between two or more participants, and they have the ability to be executed automatically when certain conditions and agreements meet [111]. Figure 7 illustrates the general process flow existing among energy prosumers who can purchase/offer energy through blockchain smart contracts after agreements on the requirements and rates. Due to smart contracts’ simple, safe, lightweight features, they can be implemented in a blockchain-enabled network of peers (energy producers in our scenario) to expedite secure billing mechanisms.

Aside from requirement setting and meeting, blockchain can assure billing information accuracy and consistency as it prevents fraud and manipulation issues [112]. Particularly, based on the block hash value and duplicated public ledger for all nodes contained in the chain, the blockchain can avoid forgery in an anonymous trust-less relationship by easily being able to detect the forged ledger/information by a certain node [113]. To sum up, blockchain can play an important role in enhancing conventional payment systems by providing secure, trusted connections among participants with smart contracts that automate transaction processes. Furthermore, the reliability of the billing system improves as middle agents’ presence is not required anymore, and all the information is stored and reported inside a public, transparent ledger (as illustrated in Figure 8) [114].

7. Challenges and Open Opportunities

On the basis of the above-presented survey, several future research directions and promising opportunities are presented in this section. In recent years, the advancement and evolution of EVs have experienced huge progress, however, some aspects are still unresolved and can be captivating to explore to offer improving solutions.

The following section classifies the promising opportunities into four categories: (1) the optimization of the charging points placements in urban/rural areas, (2) the use of AI in the improvement of charging time/amount planning and charging point selection, (3) EV users’ data security issues in cross-platform data exchange frameworks, and finally, (4) the emerging energy trading paradigm improvements among any individual electricity supplier.

7.1. Challenges in EV Infrastructure Systems

One of the primary components of EVs is the rechargeable Lithium-ion batteries in which electricity is stored to provide power for the electric motors. The main idea of developing a replacement for conventional vehicles is based on first having a safe and reliable rechargeable battery [115,116]. Since battery capacity plays an important role in estimating the driving range, technological infrastructure advancements in battery size and health can accelerate the EV popularity trend. Studying battery life cycles in more detail is needed in order to propose solutions that address battery management and safeguarding. Monitoring battery health-condition (state-of-health) will improve the EV performance and enables more accurate and efficient SOC prediction based on the battery’s physical components measurements, including voltage, temperature, etc. (known as Direct Techniques). Due to the easiness of measurements in the direct method, researchers tend less to consider other methods, namely, Impedance Spectroscopy and Degradation techniques, which require more complex computation but provide more estimation accuracy. Further studies in this area can potentially help improve EVs technological-infrastructure conditions, leading to advanced battery management systems featuring optimized health and capacity determination and simulation [117,118].

In addition, proposing optimized infrastructure architectures will ease EVs driving range, and convenience on the road [119]. There are a number of charging point placement mechanisms with various optimization objectives, however, designing an optimization technique to achieve the electricity demand-supply balance in a cost-effective manner is still a need [120]. The challenging prospect is developing an algorithm that considers the grid pressure reduction, electricity distribution frequency, and costly regulations. Multi-stage geometry/grid-based heuristic approaches can be used to identify charging demands, locations of charging sites clusters, and capacity [21,121].

Another type of infrastructure improvement is to design and place some wireless charging transmitters in designated areas of the driving roads so that passing e-cars can receive energy while traveling by to help them get charged on the move [20,122]. This topic is considered as dynamic wireless energy transfer, and it is neglected in most studies as it comes with a big and unique challenge of electromagnetic coil alignment between dynamic charging distances to increase the transmission power [123]. However, the issue is when multiple vehicles, driving closely, reach a specific transmitter [124]. Future research can focus on studying metering and identification equipment with charge control units to record the electricity consumption of each EV and set the amount of power transmitted from the inductive energy transfer tools to the nearby vehicles [4].

7.2. Improvement in Charging Scheduling and CS Selection

As mentioned earlier in Section 1, an EV needs at least half an hour to become fully charged, or it may take as long as half a day based on the charging technology implemented in the vehicle. On the other hand, a fully-charged EV has a driving range of approximately between 180 and 663 km [125]. By considering this wide range of driving, developing a practical charging points placement algorithm can become unpredictable. Therefore, in order to mitigate the insufficiency in the coverage of charging infrastructure, researchers can focus on the other fields of studies to propose a solution for EVs charging while on a trip or parked without relying on an expensive change in infrastructure [19].

It is inevitable that EVs will rapidly replace traditional vehicles in the near future, therefore, the current struggling factor, which is the inconvenience caused by the uncertain time to find a charging point and refuel, should be addressed [45]. In fact, the findings from [123] showed that EV energy consumption is mainly affected by route driving and situations. For instance, driving on highways with high speeds or urban driving with frequent braking and acceleration states can cause considerable growth in the use of power/energy. Therefore, suggesting the most optimal route to an EV by considering all dynamic road and vehicle parameters significantly improves the driving cycles.

Flexible charging scheduling models facilitate peak-period power demand reduction while providing QoS and EVs electricity requirements. Therefore, the idea of developing multi-level hierarchical optimization frameworks consisting of distributed agents will satisfy the optimal scheduling by cooperatively calculating a set of optimization problems to reduce charging costs, and peak power-grid flows, and to increase QoS in a computationally efficient manner [126,127]. In this regard, research areas, such as energy-efficient route planning, intelligent battery management, and energy consumption prediction, charging site recommendations will result in a better EV user experience, broader driving range, and stable energy supply [96,128,129].

7.3. Data Security

To make EVs the predominant mean of transport, users’ private information preservation factor needs to be proved. This can create a smart city wherein the privacy of offered service to EV users is improved as well as administrative costs reduced [130]. In fact, the use of centralized data management services might enable an issue of data leakage. As stabilizing energy supply and demand requires accurate energy consumption and distribution predictions, maximizing EVs users’ satisfaction by providing constant access to energy under the limited data sharing and anonymous information remains as a challenging problem. [131] Contemporary approaches, such as federated learning and blockchain, can provide assurance for secure information sharing and trust management in an energy management network [24].

FL technique has shown significant abilities to prevent some security attacks, such as model poisoning threats, data poisoning threats, data exploiting threats, and free-riding threats [132,133]. However, there exists a significant deficiency of real model-implementations across smart cities, and due to the inexperienced architectural designs, it is still vulnerable to some potential security attacks of intercepting and analyzing the training models through the exchanging phase to restore some sensitive information [134,135]. In this regard, lightweight authentication-based models utilizing blockchain during the data exchange and aggregation phase can increase the model’s safety.

7.4. Necessary Considerations for Energy Trading

One of the existing challenges in energy trading is among moving vehicles to transfer/receive energy from other moving vehicles. Equipping EVs with programmed devices to safely identify, navigate, and transmit electricity in the vehicular network considering the dynamic distance, is an opening issue [136]. Future studies should propose alternative solutions that provide charging approaches for EVs with the minimum infrastructure required and EVs stop-times, such as wireless energy transmission en route. Although this technique can resolve many existing issues, it can cause security concerns. To further improve the future systems with energy trading safety considerations, one can apply the blockchain technology, which provides high-quality personal data protection in a transparent, tamper-proof environment [137].

The implementation of blockchain technology in the energy trading domain is something new and little explored. The adoption of smart contracts enables auction-based systems with secure payments through encrypted immutable transactions, however, such contracts should also consider environmental laws and regulations [23,113]. Furthermore, influential efforts are required to form the blockchain-based electricity-marketing framework adequately extensible and scalable [137]. Moreover, if blockchain is applied in the energy trading sector, then designing an effective consensus algorithm among participating nodes with low-computational complexity is in need. This algorithm is believed to require less electrical-energy consumption and to have good execution speed in terms of transactions per second [138,139].

7.5. Payment Systems Problems and Open Issues

The association of EVs smart charging and blockchain technology will create more business opportunities in order to achieve decentralization and effectiveness. Some of the main challenges existing in P2P EVs energy trading billing systems based on blockchain are:

• Safety and security: Maximum transparency, which is offered by blockchain in an energy trading system, is a perfect feature, however, future payment applications should apply strict security and data integrity mechanisms, such as zero-knowledge proof, to ensure user data protection fully [114]. More importantly, the system must be secure against some serious attacks (e.g., stolen coins) and fake payments.
• Technical standards: Before being able to apply the blockchain technology globally to payment systems, some challenges which may hinder its functionality, such as increased costs, standardization failure, and lack of interoperability, should be addressed. Hence, some advanced smart contract-based charging schemes can be proposed to securely study and form charging prices. Many contract conditions should be analyzed to satisfy EV users’ preferences for power consumption and optimize the operators’ services and benefits.
• Regulation: Sensitive information about money and payments is being recorded inside the billing system, which is distributed, and participants are anonymous. Therefore, all mandatory regulatory compliance necessarily should be followed to avoid violation charges or penalties according to the geographical region and respective government guidelines.
• Governance: Transactions are recorded repeatedly among all nodes, which makes them immutable. However, this feature raises issues, namely, the inability to reverse transactions or cancel payments and find a proper mechanism to assign and control blockchain ledger responsibility and accountability. Setting governance standards and deploying optimized solutions to facilitate cancellation might address the above-mentioned issues.

8. Discussion and Conclusions

The technical outcomes of the conducted comprehensive review revealed that in the past several years, EVs had been promoted by many governments to allow the reduction of carbon emissions and air quality improvements. However, this policy raised the main challenge related to the imbalance between electricity demand and energy production, especially during peak hours. To achieve the planned net-zero emission goals and promote EVs adoption, energy congestion issues should be addressed to increase EVs popularity among the public. Another challenging factor is the unbalanced number of charging facilities and EVs charging location demands. As the improvement of the number of CSs infrastructure is considerably time and money-consuming, researchers are trying to propose practical solutions for EVs owners’ range anxiety concern mitigation. In this case, addressing the problems, such as how to efficiently manage EVs energy consumption and demands, how to optimally schedule EVs charging time, and how to accurately locate ideal CSs for EVs in need of electricity can significantly enhance the power demand pressure on smart grids. Mainly, the presented research paper tried to provide a wide and clear overview of current challenges regarding CS architectural infrastructure, EV charging technologies, and energy monitoring/management applications in the first half of the paper.

Then, it was revealed that in accordance with existing literature, an accurate energy management system needs to aggregate users’ data to provide a plan for EVs energy scheduling, demand estimation, and CSs recommendation. However, with the extreme growth in data security supervision and user data-confidentiality protection requirements, entities and different institutes can not provide data to each other. In this regard, an energy management model is not able to operate well unless it develops a mechanism to ensure users’ data privacy protection. Among previously introduced security settings, FL provides more promising features which can enable the development of a cross-platform joint data analysis model establishment However, due to some technical challenges of scatter distribution of available data samples, possible solutions of transferring learning models from one data sample domain to another were discussed.

Moreover, as newly proposed models are trying to lower the load pressure on the power facilities, especially during rush hours, an alternative approach of exchanging electricity between EVs should be taken into consideration to guarantee low-range anxiety among EVs owners. Enabling energy delivery from V2G discharging or from anything-to-vehicle (X2V) can economically coordinate energy distribution. However, developing a secure connection to optimally increase participants’ profits and finding the best-matched profiles is challenging. Blockchain, as the most well-known secure end-to-end transaction trading, can provide a robust platform to allow safe end-consumer/prosumer energy trading, remote and transparent monitoring, and trustworthiness network establishment. Although this technology seems an effective solution, it should be noted that future studies should mitigate its high computational complexity by proposing lightweight consensuses for both energy trading and billing systems.

To sum up, this paper investigated opportunities and disputable challenges in the EVs energy management research area by acquiring a number of articles between 2018 and 2022. Initially, the article reviewed the prevailing debates in the area of charging station placement, EVs charging scheduling, and CS recommendation concluded that the major concerns are energy demand response, EVs users’ range anxiety, and users’ data privacy. After that, the authors explored federated learning and blockchain applications as promising solutions to increase system security and scalability. Furthermore, P2P energy trading applications among electricity providers were reviewed to suggest several potential approaches to manage energy requests by representing end-to-end direct supply-demand balance.

The authors believe that the presented literature review could facilitate the technological advancement of EV and its user-data privacy. For future work, addressing some challenges in the area of available data sample collection and utilization regardless of the amount of data is planned. In other words, to enhance an EV energy management system performance, an in-depth analysis of EVs battery and driving behavior is required. However, due to the insufficient obtainable EV data, we believe that applying an advanced Deep Learning model can solve this issue.