Submitted Successfully!
Thank you for your contribution! You can also upload a video entry related to this topic through the link below:
Check Note
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 3463 2022-11-18 22:56:15 |
2 format Meta information modification 3463 2022-11-21 02:50:16 |
Intelligent Energy Management Systems for Electric Vehicle Transportation
Upload a video

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. 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. 

electric vehicles charging management systems smart cities
View Times: 47
Revisions: 2 times (View History)
Update Time: 22 Nov 2022
Table of Contents

    1. 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 [1]. 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) [2][3]. Survey [4] 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 company, in most countries, a passenger vehicle’s average electricity consumption is approximately 0.2 kWh per kilometer [5]. Thus, the coordination between CSs power capacity in the transportation sector and distribution network must be organized [6].
    The aforementioned aspects of CSs placement make the formulation and optimization of the solutions a challenging task [7]. 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. [8][9][10]. 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 [11], fuzzy neural network [12], linear programming [13][14], 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 [15]. 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 [16]. 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 [17].
    In [18], 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 [19][20] 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.

    2. Charging Scheduling and Charging Station Selection

    Charging batteries embedded in EVs in uncontrolled manners can be detrimental to the battery life [21]. 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 [23][24]. 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 [25]. 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 [26][27].
    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.

    2.1. 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 [28]. 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 [29]. The research in [9] 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, [30] 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 [2][31]. 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 [26].

    2.2. 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 [32][33]. Zhang et al. [16] 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 [34] 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 [35].

    2.3. 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 [36][37]. 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 [38][39][40]. 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 [41] reveal that designated batteries for electric city buses are unnecessarily oversized, considering the regional situations with mild temperatures and short trips.

    3. 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.

    3.1. Federated Learning

    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 [42]. 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 [43]. 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 [44][45].
    The federate Learning technique is classified into three groups in accordance with the data-division properties [46]. 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 [47]. 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 [48]. 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 [49]. 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 [50].
    Optimizing the large-scale communication bandwidth between entities and the aggregator server is necessary among all FL models [51]. Furthermore, FL models are required to provide security for the central server to protect model parameter aggregation [52].
    In [53], 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 [54] 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.

    3.2. Blockchain

    The initial introduction of Blockchain technology was in 2009 to describe the basis of developing the Bitcoin digital currency [55]. This can be another approach to reduce security threats to the private information of data owners [56][57].
    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 [58]. 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 [56]. 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 [59]. PoS is considered a cleaner and faster protocol than PoW since it requires lower computation power and higher transaction throughput [60]. 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 [61]. 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 [62][63].
    Alternative consensus protocols have been introduced, subject to each application criteria. However, all these protocols should be evaluated based on five key metrics [64][65], depicted in Figure 1:
    Figure 1. Important properties affecting a consensus protocol performance.
    • Scalability: Denotes to the capability of a consensus protocol to sustain the overall performance with the addition of nodes, transactions, and data [66].
    • 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 [67].
    • Throughput: Denotes the number of transaction verification and deployment to the blockchain per second (Tps) [68]. For instance, Bitcoin’s throughput is approximately 7 Tps [69].
    • 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 [70], 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 [71].
    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 [72]. 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 [73].
    Most of the previous works that utilized blockchain, tried to mitigate data leakage by saving local and global ML models in each active block [60][74]. This technique is believed to perform effectively as a safe information transfer solution [75]. 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.

    4. 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 [76]. 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 [77]. 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 [78].
    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 [61][79]. The P2P energy trading paradigm allows participants to trade electricity independent of the centralized institutions (e.g., utility companies) [80]. Researchers in [81] 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 [82] 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 [83] 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 [84] proposed a blockchain-based machine learning model to maximize the profitability of parked EVs based on a Game-theoretic stochastic bidding process. In [85], 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 [86] 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 [87] 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 [88] 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, [89] 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 [90] 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.


    1. Al-Hanahi, B.; Ahmad, I.; Habibi, D.; Masoum, M.A. Charging infrastructure for commercial electric vehicles: Challenges and future works. IEEE Access 2021, 9, 121476–121492.
    2. Ma, T.Y.; Xie, S. Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment. Transp. Res. Part Transp. Environ. 2021, 90, 102682.
    3. Huang, Y.; Kockelman, K.M. Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium. Transp. Res. Part Transp. Environ. 2020, 78, 102179.
    4. Pevec, D.; Babic, J.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Ketter, W.; Podobnik, V. Electric Vehicle Range Anxiety: An Obstacle for the Personal Transportation (R)evolution? In Proceedings of the 4th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 18–21 June 2019; pp. 1–8.
    5. Kavianipour, M.; Fakhrmoosavi, F.; Singh, H.; Ghamami, M.; Zockaie, A.; Ouyang, Y.; Jackson, R. Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior. Transp. Res. Part Transp. Environ. 2021, 93, 102769.
    6. Guler, D.; Yomralioglu, T. Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS. Ann. GIS 2020, 26, 169–189.
    7. Yassine, A.; Hossain, M.S. COVID-19 Networking Demand: An Auction-Based Mechanism for Automated Selection of Edge Computing Services. IEEE Trans. Netw. Sci. Eng. 2022, 9, 308–318.
    8. Chen, Y.; Qin, Z.; Gan, X. Optimal EV Charging Network Design: When Users Have Choices. In Proceedings of the 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi’an, China, 23–25 October 2019; pp. 1–6.
    9. Vaidya, B.; Mouftah, H.T. Smart electric vehicle charging management for smart cities. IET Smart Cities 2020, 2, 4–13.
    10. Huang, D.; Chen, Y.; Pan, X. Optimal model of locating charging stations with massive urban trajectories. IOP Conf. Ser. Mater. Sci. Eng. 2020, 715, 012009.
    11. Liu, G.; Kang, L.; Luan, Z.; Qiu, J.; Zheng, F. Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs). Energies 2019, 12, 2595.
    12. Ramachandran, A.; Balakrishna, A.; Kundzicz, P.; Neti, A. Predicting electric vehicle charging station usage: Using machine learning to estimate individual station statistics from physical configurations of charging station networks. arXiv 2018, arXiv:1804.00714.
    13. Zeb, M.Z.; Imran, K.; Khattak, A.; Janjua, A.K.; Pal, A.; Nadeem, M.; Zhang, J.; Khan, S. Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network. IEEE Access 2020, 8, 68124–68134.
    14. Cui, S.; Zhao, H.; Wen, H.; Zhang, C. Locating Multiple Size and Multiple Type of Charging Station for Battery Electricity Vehicles. Sustainability 2018, 10, 3267.
    15. Deb, S.; Gao, X.Z.; Tammi, K.; Kalita, K.; Mahanta, P. A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem. Energy 2021, 220, 229645.
    16. Zhang, J.; Li, T.; Pan, A.; Long, X.; Jiang, L.; Liu, Z.; Zhang, Y. Charging Time and Location Recommendation Strategy Considering Taxi User Satisfaction. In Proceedings of the Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, 28–31 May 2020; pp. 257–264.
    17. Guo, Z.; Ou, D.; Xie, W.; Tan, Z. Analysis and Optimization to the Final Network of Charging Stations. In Proceedings of the IEEE 3rd Optoelectronics Global Conference (OGC), Shenzhen, China, 4–7 September 2018; pp. 209–213.
    18. Dong, G.; Ma, J.; Wei, R.; Haycox, J. Electric vehicle charging point placement optimisation by exploiting spatial statistics and maximal coverage location models. Transp. Res. Part Transp. Environ. 2019, 67, 77–88.
    19. Cui, Q.; Weng, Y.; Tan, C.W. Electric Vehicle Charging Station Placement Method for Urban Areas. IEEE Trans. Smart Grid 2019, 10, 6552–6565.
    20. Tayyab, M.; Helm, S.; Hauer, I.; Brinken, J.; Schmidtke, N. Infrastructure linking for placement of Charging stations using Monte Carlo simulation. In Proceedings of the 6th IEEE Congress on Information Science and Technology (CiSt), Agadir, Morocco, 5–12 June 2020; pp. 436–441.
    21. Hu, X.; Xu, L.; Lin, X.; Pecht, M. Battery Lifetime Prognostics. Joule 2020, 4, 310–346.
    22. Sanguesa, J.A.; Torres-Sanz, V.; Garrido, P.; Martinez, F.J.; Marquez-Barja, J.M. A Review on Electric Vehicles: Technologies and Challenges. Smart Cities 2021, 4, 372–404.
    23. Saner, C.B.; Trivedi, A.; Srinivasan, D. A Cooperative Hierarchical Multi-Agent System for EV Charging Scheduling in Presence of Multiple Charging Stations. IEEE Trans. Smart Grid 2022, 13, 2218–2233.
    24. Wang, H.J.; Wang, B.; Fang, C.; Li, W.; Huang, H.W. Charging Load Forecasting of Electric Vehicle Based on Charging Frequency. IOP Conf. Ser. Earth Environ. Sci. 2019, 237, 062008.
    25. Wu, Z.; Chen, B. Distributed Electric Vehicle Charging Scheduling with Transactive Energy Management. Energies 2022, 15, 163.
    26. Long, T.; Jia, Q.S.; Wang, G.; Yang, Y. Efficient real-time EV charging scheduling via ordinal optimization. IEEE Trans. Smart Grid 2021, 12, 4029–4038.
    27. Liu, L.; Zhou, K. Electric vehicle charging scheduling considering urgent demand under different charging modes. Energy 2022, 249, 123714.
    28. Song, J.; Han, Z.; Wang, W.; Chen, J.; Liu, Y. A new secure arrangement for privacy-preserving data collection. Comput. Stand. Interfaces 2022, 80, 103582.
    29. Li, F.; Xu, L.; Zhang, M.; Tian, Y.; Wu, Y.; Guo, N. Charging Load Feature Extraction and Charging Optimization Recommendations Based on Shanghai Public Charging Station Operation Data. In Proceedings of the 10th International Conference on Power and Energy Systems (ICPES), Chengdu, China, 25–27 December 2020; pp. 338–344.
    30. Setiawan, A.D.; Hidayatno, A.; Putra, B.D.; Rahman, I. Selection of Charging Station Technology to Support the Adoption of Electric Vehicles in Indonesia with the AHP-TOPSIS Method. In Proceedings of the 3rd International Conference on Power and Energy Applications (ICPEA), Busan, Korea, 24–26 April 2020; pp. 85–88.
    31. Savari, G.F.; Krishnasamy, V.; Sathik, J.; Ali, Z.M.; Aleem, S.H.A. Internet of Things based real-time electric vehicle load forecasting and charging station recommendation. ISA Trans. 2020, 97, 431–447.
    32. Tu, W.; Mai, K.; Zhang, Y.; Xu, Y.; Huang, J.; Deng, M.; Chen, L.; Li, Q. Real-Time Route Recommendations for E-Taxies Leveraging GPS Trajectories. IEEE Trans. Ind. Inform. 2021, 17, 3133–3142.
    33. Wang, G.; Zhang, Y.; Fang, Z.; Wang, S.; Zhang, F.; Zhang, D. FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2020, 4, 1–25.
    34. Tian, Z.; Jung, T.; Wang, Y.; Zhang, F.; Tu, L.; Xu, C.; Tian, C.; Li, X.Y. Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis. IEEE Trans. Intell. Transp. Syst. 2016, 17, 3098–3109.
    35. Wang, E.; Ding, R.; Yang, Z.; Jin, H.; Miao, C.; Su, L.; Zhang, F.; Qiao, C.; Wang, X. Joint Charging and Relocation Recommendation for E-Taxi Drivers via Multi-Agent Mean Field Hierarchical Reinforcement Learning. IEEE Trans. Mob. Comput. 2020, 21, 1274–1290.
    36. Zhang, L.; Wang, S.; Qu, X. Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile. Transp. Res. Part Logist. Transp. Rev. 2021, 154, 102445.
    37. Wang, G.; Fang, Z.; Xie, X.; Wang, S.; Sun, H.; Zhang, F.; Liu, Y.; Zhang, D. Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses. ACM Trans. Intell. Syst. Technol. 2021, 12, 1–26.
    38. Bie, Y.; Ji, J.; Wang, X.; Qu, X. Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption. Comput.-Aided Civ. Infrastruct. Eng. 2021, 36, 1530–1548.
    39. Yıldırım, Ş.; Yıldız, B. Electric bus fleet composition and scheduling. Transp. Res. Part Emerg. Technol. 2021, 129, 103197.
    40. Bie, Y.; Hao, M.; Guo, M. Optimal electric bus scheduling based on the combination of all-stop and short-turning strategies. Sustainability 2021, 13, 1827.
    41. Basma, H.; Mansour, C.; Haddad, M.; Nemer, M.; Stabat, P. Energy consumption and battery sizing for different types of electric bus service. Energy 2022, 239, 122454.
    42. Geyer, R.C.; Klein, T.; Nabi, M. Differentially Private Federated Learning: A Client Level Perspective. arXiv 2018, arXiv:1712.07557.
    43. Mohassel, P.; Rindal, P. ABY3: A Mixed Protocol Framework for Machine Learning. In Proceedings of the 2018 ACM SIGSACConference on Computer and Communications Security, Toronto, ON, Canada, 15–19 October 2018; pp. 35–52.
    44. Yang, Q.; Liu, Y.; Chen, T.; Tong, Y. Federated Machine Learning: Concept and Applications. arXiv 2019, arXiv:1902.04885.
    45. Abdulrahman, S.; Tout, H.; Ould-Slimane, H.; Mourad, A.; Talhi, C.; Guizani, M. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond. IEEE Internet Things J. 2021, 8, 5476–5497.
    46. Du, Z.; Wu, C.; Yoshinaga, T.; Yau, K.L.A.; Ji, Y.; Li, J. Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues. IEEE Open J. Comput. Soc. 2020, 1, 45–61.
    47. Zhao, J.; Zhu, X.; Wang, J.; Xiao, J. Efficient client contribution evaluation for horizontal federated learning. In Proceedings of the ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Virtual, 6–11 June 2021; pp. 3060–3064.
    48. Teimoori, Z.; Yassine, A.; Hossain, M.S. A Secure Cloudlet-based Charging Station Recommendation for Electric Vehicles Empowered by Federated Learning. IEEE Trans. Ind. Inform. 2022, 18, 6464–6473.
    49. Wu, Z.; Li, Q.; He, B. Exploiting Record Similarity for Practical Vertical Federated Learning. arXiv 2021, arXiv:2106.06312.
    50. Zhang, Z.; He, N.; Li, D.; Gao, H.; Gao, T.; Zhou, C. Federated transfer learning for disaster classification in social computing networks. J. Saf. Sci. Resil. 2022, 3, 15–23.
    51. Zheng, Z.; Zhou, Y.; Sun, Y.; Wang, Z.; Liu, B.; Li, K. Applications of federated learning in smart cities: Recent advances, taxonomy, and open challenges. Connect. Sci. 2022, 34, 1–28.
    52. Pandey, S.R.; Tran, N.H.; Bennis, M.; Tun, Y.K.; Manzoor, A.; Hong, C.S. A Crowdsourcing Framework for On-Device Federated Learning. IEEE Trans. Wirel. Commun. 2020, 19, 3241–3256.
    53. Saputra, Y.M.; Hoang, D.T.; Nguyen, D.N.; Dutkiewicz, E.; Mueck, M.D.; Srikanteswara, S. Energy Demand Prediction with Federated Learning for Electric Vehicle Networks. arXiv 2019, arXiv:1909.00907.
    54. Zhang, H.; Bosch, J.; Olsson, H.H. Real-time End-to-End Federated Learning: An Automotive Case Study. In Proceedings of the IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 12–16 July 2021; pp. 459–468.
    55. Nakamoto, S. Bitcoin: A Peer-to-Peer Electronic Cash System. Decentralized Business Review. 2008. Available online: (accessed on 18 September 2022).
    56. Frauenthaler, P.; Sigwart, M.; Spanring, C.; Sober, M.; Schulte, S. ETH Relay: A Cost-efficient Relay for Ethereum-based Blockchains. In Proceedings of the IEEE International Conference on Blockchain (Blockchain), Rhodes, Greece, 2–6 November 2020; pp. 204–213.
    57. Jain, S.; Ahuja, N.J.; Srikanth, P.; Bhadane, K.V.; Nagaiah, B.; Kumar, A.; Konstantinou, C. Blockchain and Autonomous Vehicles: Recent Advances and Future Directions. IEEE Access 2021, 9, 130264–130328.
    58. Oyinloye, D.P.; Teh, J.S.; Jamil, N.; Alawida, M. Blockchain Consensus: An Overview of Alternative Protocols. Symmetry 2021, 13, 1363.
    59. Moniruzzaman, M.; Khezr, S.; Yassine, A.; Benlamri, R. Blockchain for smart homes: Review of current trends and research challenges. Comput. Electr. Eng. 2020, 83, 106585.
    60. Kim, H.; Park, J.; Bennis, M.; Kim, S.L. Blockchained On-Device Federated Learning. IEEE Commun. Lett. 2020, 24, 1279–1283.
    61. Huang, Z.; Li, Z.; Lai, C.S.; Zhao, Z.; Wu, X.; Li, X.; Tong, N.; Lai, L.L. A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations. Electronics 2021, 10, 307.
    62. Lu, Y.; Huang, X.; Zhang, K.; Maharjan, S.; Zhang, Y. Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles. IEEE Trans. Veh. Technol. 2020, 69, 4298–4311.
    63. Khoumsi, A. An Efficient Blockchain-based Electric Vehicle Charging Management System. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Athens, Greece, 5–8 September 2021; pp. 1–7.
    64. Thukral, M.K. Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: A review. Clean Energy 2021, 5, 104–123.
    65. Gamage, H.T.M.; Weerasinghe, H.D.; Dias, N.G.J. A Survey on Blockchain Technology Concepts, Applications, and Issues. SN Comput. Sci. 2020, 1, 114.
    66. Bao, J.; He, D.; Luo, M.; Choo, K.K.R. A Survey of Blockchain Applications in the Energy Sector. IEEE Syst. J. 2021, 15, 3370–3381.
    67. Lepore, C.; Ceria, M.; Visconti, A.; Rao, U.P.; Shah, K.A.; Zanolini, L. A Survey on Blockchain Consensus with a Performance Comparison of PoW, PoS and Pure PoS. Mathematics 2020, 8, 1782.
    68. Feng, L.; Zhang, H.; Tsai, W.T.; Sun, S. System architecture for high-performance permissioned blockchains. Front. Comput. Sci. 2019, 13, 1151–1165.
    69. Transaction Rate Per Second. Available online: (accessed on 12 June 2022).
    70. Baza, M.; Nabil, M.; Mahmoud, M.M.E.A.; Bewermeier, N.; Fidan, K.; Alasmary, W.; Abdallah, M. Detecting sybil attacks using proofs of work and location in vanets. IEEE Trans. Dependable Secur. Comput. 2020, 19, 39–53.
    71. Zhang, S.; Lee, J.H. Analysis of the main consensus protocols of blockchain. ICT Express 2020, 6, 93–97.
    72. Agung, A.A.G.; Handayani, R. Blockchain for smart grid. J. King Saud Univ. Comput. Inf. Sci. 2022, 34, 666–675.
    73. Liu, Z.; Wang, D.; Wang, J.; Wang, X.; Li, H. A Blockchain-Enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks. IEEE Access 2020, 8, 177745–177756.
    74. Toyoda, K.; Zhao, J.; Zhang, A.N.S.; Mathiopoulos, P.T. Blockchain-Enabled Federated Learning With Mechanism Design. IEEE Access 2020, 8, 219744–219756.
    75. ur Rehman, M.H.; Salah, K.; Khaled, E.; Svetinovic, D. Towards Blockchain-Based Reputation-Aware Federated Learning. In Proceedings of the IEEE INFOCOM 2020—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada, 6–9 July 2020; pp. 183–188.
    76. García, J.S.; Morcillo, J.D.; Redondo, J.M.; Becerra-Fernandez, M. Automobile Technological Transition Scenarios Based on Environmental Drivers. Appl. Sci. 2022, 12, 4593.
    77. Rajasekaran, A.S.; Azees, M.; Al-Turjman, F. A comprehensive survey on security issues in vehicle-to-grid networks. J. Control Decis. 2022, 1–10.
    78. Elkasrawy, A.; Venkatesh, B. Demand Response Cooperative and Demand Charge. IEEE Trans. Smart Grid 2020, 11, 4167–4175.
    79. El-Sayed, I.; Khan, K.; Dominguez, X.; Arboleya, P. A Real Pilot-Platform Implementation for Blockchain-Based Peer-to-Peer Energy Trading. In Proceedings of the IEEE Power & Energy Society General Meeting (PESGM), Virtual, 2–6 August 2020; pp. 1–5.
    80. Bulut, E.; Kisacikoglu, M.C.; Akkaya, K. Spatio-Temporal Non-Intrusive Direct V2V Charge Sharing Coordination. IEEE Trans. Veh. Technol. 2019, 68, 9385–9398.
    81. Li, G.; Gong, C.; Zhao, L.; Wu, J.; Boukhatem, L. An Efficient Reinforcement Learning based Charging Data Delivery Scheme in VANET-Enhanced Smart Grid. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea, 19–22 February 2020; pp. 263–270.
    82. Shurrab, M.; Singh, S.; Otrok, H.; Mizouni, R.; Khadkikar, V.; Zeineldin, H. RAn Efficient Vehicle-to-Vehicle (V2V) Energy Sharing Framework. IEEE Internet Things J. 2022, 9, 5315–5328.
    83. Pankiraj, J.S.; Yassine, A.; Choudhury, S. Double-Sided Auction Mechanism for Peer-to-Peer Energy Trading Markets. In Proceedings of the IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China, 17–19 December 2021; pp. 443–452.
    84. Said, D. A Decentralized Electricity Trading Framework (DETF) for Connected EVs: A Blockchain and Machine Learning for Profit Margin Optimization. IEEE Trans. Ind. Inform. 2021, 17, 6594–6602.
    85. Bansal, G.; Bhatia, A. A Fast, Secure and Distributed Consensus Mechanism for Energy Trading Among Vehicles using Hashgraph. In Proceedings of the International Conference on Information Networking (ICOIN), Barcelona, Spain, 7–10 January 2020; pp. 772–777.
    86. Otoum, S.; Ridhawi, I.A.; Mouftah, H. A Federated Learning and Blockchain-enabled Sustainable Energy-Trade at the Edge: A Framework for Industry 4.0. IEEE Internet Things J. 2022.
    87. Wang, J. A novel electric vehicle charging chain design based on blockchain technology. Energy Rep. 2022, 8, 785–793.
    88. Kaur, K.; Kaddoum, G.; Zeadally, S. Blockchain-Based Cyber-Physical Security for Electrical Vehicle Aided Smart Grid Ecosystem. IEEE Trans. Intell. Transp. Syst. 2021, 22, 5178–5189.
    89. El Houda, Z.A.; Hafid, A.S.; Khoukhi, L. Blockchain-based Reverse Auction for V2V charging in smart grid environment. In Proceedings of the ICC 2021—IEEE International Conference on Communications, Montreal, QC, Canada, 14–23 June 2021; pp. 1–6.
    90. Sun, G.; Dai, M.; Zhang, F.; Yu, H.; Du, X.; Guizani, M. Blockchain-Enhanced High-Confidence Energy Sharing in Internet of Electric Vehicles. IEEE Internet Things J. 2020, 7, 7868–7882.
    Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : ,
    View Times: 47
    Revisions: 2 times (View History)
    Update Time: 22 Nov 2022
    Table of Contents


      Are you sure to Delete?

      Video Upload Options

      Do you have a full video?
      If you have any further questions, please contact Encyclopedia Editorial Office.
      Teimoori, Z.; Yassine, A. Intelligent Energy Management Systems for Electric Vehicle Transportation. Encyclopedia. Available online: (accessed on 04 December 2022).
      Teimoori Z, Yassine A. Intelligent Energy Management Systems for Electric Vehicle Transportation. Encyclopedia. Available at: Accessed December 04, 2022.
      Teimoori, Zeinab, Abdulsalam Yassine. "Intelligent Energy Management Systems for Electric Vehicle Transportation," Encyclopedia, (accessed December 04, 2022).
      Teimoori, Z., & Yassine, A. (2022, November 18). Intelligent Energy Management Systems for Electric Vehicle Transportation. In Encyclopedia.
      Teimoori, Zeinab and Abdulsalam Yassine. ''Intelligent Energy Management Systems for Electric Vehicle Transportation.'' Encyclopedia. Web. 18 November, 2022.