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Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers

Ifiok Anthony Umoren
Muhammad Zeeshan Shakir
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Author to whom correspondence should be addressed.
Energies 2022, 15(19), 7207;
Submission received: 5 September 2022 / Revised: 25 September 2022 / Accepted: 27 September 2022 / Published: 30 September 2022


Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system.

1. Introduction

Due to climate change, fossil energy reserves and greenhouse gas (GHG) emission concerns, efforts are currently ongoing towards the transition to electric mobility [1]. There exist several kinds of government policies designed to reduce GHG emissions and promote the acceptance of electric vehicles (EVs), such as the UK’s Vehicle Scrappage Scheme (VSS) [2], Car Allowance Rebate System (CARS) in the US [3], Zero Emission Vehicle (ZEV) programs in California, China and the EU [4], and the Corporate Average Fuel Economy (CAFE) standards [5]. There are also government incentives designed to support policy-driven adoption of EVs, such as purchase rebates, tax credits, tax exemptions and waivers on charging and parking fees. However, due to social obstacles, technical limitations and cost premiums compared to conventional internal combustion engine (ICE) vehicles, EVs have not been widely adopted [6]. The main types of EVs on the market are battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEVs), extended range electric vehicle (EREVs) and fuel cell vehicles (FCVs) [7]. For the purpose of this article, all electric vehicles with plug-in capabilities are collectively referred to as “EVs”.
Range anxiety—fears over the distance EVs can travel between charges—is a major technological barrier to large-scale adoption of EVs [8]. One of the factors influencing range anxiety is the availability of EV charging points. Fear over a lack of charging points was identified in [9] as the biggest concern with regards to EV ownership. However, with several government policies across the globe supporting the increased penetration of EVs [10], there has been a rapid rise in the number of charge points. The remaining driving range (RDR)—the distance an EV can travel with the residual energy in the battery—is another factor influencing range anxiety. The RDR of EVs cannot be accurately estimated by current technologies; hence, drivers tend to reserve around 30% of battery capacity as an emergency buffer, to protect them from running out of power [11]. With accurate RDR estimation, drivers would be able to make efficient use of their limited battery capacity, thereby minimizing range anxiety considerably [12]
Known for their features of being energy-conserving, revenue generating and emission free, EVs have become the future trend. EVs have advantages over ICE vehicles, most notably the ability to be used as a service for the electricity grid—electric vehicle as a service (EVaaS). EVs can provide service individually or as part of an aggregation. In the latter case, EVs are selected into groups by aggregators to create a larger, more manageable generation capacity or load for the electricity grid [13]. EVs receive power from the grid to charge their batteries in grid-to-vehicle (G2V) mode, whereas in vehicle-to-grid (V2G) mode, EVs supply part of their stored energy back to the grid. We use the term “V2G” to broadly refer to both G2V (unidirectional) and V2G (bidirectional) energy flows in our article. Although the concept of V2G was introduced over two decades ago [14], it is still in the very early stages of development. There are different versions of V2G, characterized by their energy exchange processes such as vehicle-to-home (V2H), vehicle-to-building (V2B) and vehicle-to-vehicle (V2V). V2H is a small-scale version of V2G that allows an EV to supply homes with the energy stored in its battery [15]. Similar to V2H, V2B allows EVs to power buildings [16], whereas V2V involves energy exchange between EVs in social hotspots such as charging stations, parking lots and swapping stations [17].
In V2G, EVs are integrated into the electricity grid, where energy is first stored in the EV battery and then fed back into the grid. In [18], the technical, commercial and domestic proposition of V2G technology to the distribution network is evaluated through demonstrator projects. From a utility perspective, there are numerous economic benefits from V2G. These include ancillary services such as energy balancing, voltage and frequency regulation, active and reactive power support, current harmonic filtering, spinning reserves, valley filling, peak shaving and load following. V2G can also improve the technical performance of the electricity grid in areas such as stability, reliability, efficiency and resilience. V2G can further reduce emissions, replace large-scale energy storage systems, improve load factors, provide support for renewable energy sources (RESs) and, by contributing to local consumption, could reduce electricity transport losses in grids with high penetration of decentralized generation. Additionally, the savings in utility operations will minimize the overall service cost to customers, which will be reflected in energy prices. The aforementioned benefits are not specific to either G2V or V2G alone, but are true of V2G in general.
While the potential benefits of V2G transition have been widely recognized, they may not accrue without significant challenges. Impediments to V2G actualisation include resistance from automotive and oil sectors, communication infrastructure needed for information exchange, battery degradation, requirements for monetization of energy losses, and technical, political, social and cultural obstacles. EV battery degradation costs happens to be one of the main barriers to V2G transition. An additional issue is that energy flow will become bidirectional and increasingly complex. Since the distribution grid has not been designed for this purpose, service capabilities of V2G devices tend to be limited. Conversely, bidirectional communications implementation in V2G infrastructures unlocks new possible vulnerabilities.
The implementation impact of V2G technologies on the distribution system and strategies for V2G interfaces for individual and aggregated EVs were studied in [19]. The study in [20] discussed the operation of EVs and their impact on grid stability. A methodology adopted for power flow under a V2G scheme and challenges associated with the commercial-level adoption of V2G are described in [21]. The study in [22] inspects the implementation challenges of EV infrastructural and charging systems in conjunction with several international standards and charging codes. The ancillary service potential of V2G is presented in [23], and the potential impact, challenges and future market penetration capabilities of V2G technology are discussed. Several studies have been conducted to evaluate the impact of V2G integration on utility services. However, little attention has been given to maximizing the full potential of V2G from an EV-prosumer perspective. The technologies, technical components and system requirements needed for the deployment of EVs for grid-related services are reviewed in this article. System architecture and communication infrastructure for EV-enabled microgrids are introduced. The market design and various mechanisms that motivate EV owners to participate in energy trading are discussed. Optimization methods for EV charging/discharging, and benefits and barriers to the deployment of EVs are presented and evaluated.

2. Overview of EVaaS

2.1. EVaaS Framework

EVaaS describes a system in which heterogeneous electric vehicles communicate with the electricity grid to participate in grid-related services [24,25]. EVaaS exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. It provides the opportunity for EV owners to benefit from an additional revenue stream. EV owners can be incentivized to charge their EV batteries when the energy generated exceeds demand; for example, too much energy may be generated from RESs or during off-peak hours. By contrast, EV owners who self-generate electricity from RESs or connect to the grid to charge at low-demand, cheap tariff times can then market the excess or unneeded energy stored in their EV batteries when energy costs are higher or during peak demand. Thus, EVs can act as an energy reserve for the grid. The operation of an EVaaS system is a distinctive combination of EVs, an energy management system and a service contract so that one can deliver value by providing demand-response services.
EV owners can go into a contract or agreement with the utility provider to make charging and discharging controlled, coordinated and more predictable. The utility can offer lower energy prices to incentivize EV charging and battery insurance or maintenance to incentivize EV discharging in exchange for EV owners agreeing to charge and discharge the battery, respectively, to meet grid requirements. Based on this approach, the implementation of a centralized charging and discharging solution becomes feasible, as well as the possibility of maximizing system efficiency. Alternatively, EV owners can voluntarily participate in EVaaS without making a commitment with the utility provider. Different incentives can be offered by the utility provider to motivate EV owners to charge or discharge their batteries depending on the current demand and supply of the grid. EV owners will individually consider their charging or discharging options in a distributed fashion. Based on this approach, a decentralized charging and discharging solution can achieve maximum system efficiency.

2.2. EVaaS Architecture

The architecture of an EVaaS system with interactions among subsystems is shown in Figure 1. The EVaaS system typically consists of EVs, loads (critical and non-critical), charging stations, smart meters, power lines, communication infrastructure and a microgrid control centre or aggregators [13]. The system is remotely monitored and controlled by the microgrid control centre using a supervisory control and data acquisition (SCADA) system, while the subsystems communicate with each other through a communication network to effectively carry out tasks and collectively achieve an objective. The communication network facilitates the collection of necessary data from EVaaS subsystems and allows the aggregator to efficiently optimize EV charging and discharging. However, this is dependent on status monitoring and information updates of both parked and moving EVs. The information includes the current location of the EV or where it will be in the next time frame, battery capacity and state of charge (SOC). Using this information, the aggregator can forecast or estimate the energy demand or supply from EVs within a specific region.

3. System Requirements

3.1. EV Battery

While ICE vehicles get energy from burning fossil fuel, an EV is powered by a battery. Unlike the batteries used in mobile phones, laptops and other battery-powered electronic devices, EV batteries are designed to achieve prolonged running time with high power and energy capacity. Automakers have different EV passenger models, with battery capacities boasting up to 100 kWh. The study in [26] investigates larger battery capacities (200 kWh and above) for futuristic mobile usage and recommends subdividing the total battery unit into mechanically separated containers. When an EV is being charged, chemical reactions go one way, and the battery absorbs power, these reactions are then reversed to produce electricity when the EV is being discharged. Some of the EV battery technologies widely deployed in the real world include lithium–ion (Li-ion), nickel-metal hydride (NiMH) and lead acid [27,28]. Among them, Li-ion is the most common battery technology. Several potential technologies that might be able to achieve better or comparable performance to Li-ion batteries are in early stages of development. These include nickel–cadmium (NiCd), sodium–sulfur (NaS), zinc–bromid (Zn-Br) and aluminium–air (Al-air) [29]. The study in [30] details the expected development in battery technology by 2030. Li-ion is currently the most widely accepted battery technology in the EV market, as observed in Table 1. This can be attributed to its light weight, high density, low self-discharge rate and prolonged life. The risk of explosion from overcharged cells and life cycle reduction from undercharged cells are the disadvantages associated with Li-ion batteries.
The lifespans of Li-ion batteries are typically estimated through calendar life and cycle life. The calendar life is the retainable duration in terms of calendar years, independent of charge and discharge cycling [44]. The cycle life estimates the capacity retention during continuous charge and discharge cycling before significant degradation [45]. To predict battery life, study its behaviour and simulate its performance under dynamic conditions, a battery model is required. The types of battery models widely studied include electrochemical, experimental, mathematical and electric circuit models. The most accurate are electrochemical models; however, they require in-depth knowledge of the chemical reactions of batteries and a complex set of equations that govern battery behaviour [46,47]. Experimental models are based on experimentation to determine parameters associated with battery behaviour [48,49]. Mathematical models consist of stochastic approaches that capture battery behaviour [50,51]. Electric circuit models provide an equivalent representation of battery characteristics. The basic equivalent circuit model consists of an open-circuit voltage in series with a resistance and a parallel combination of resistance and capacitance [52,53,54].

3.2. EV Charging Stations

An EV charging station, also called a charge/charging point, an electric recharging point and electric vehicle supply equipment (EVSE), is equipment that connects an EV to the electricity grid. When EVs are plugged into a charge point, they can behave as loads or generators to the electricity grid. The rapid growth in the global EV market has led to the proliferation of charger designs, charging strategies, charging techniques and charging networks. Techniques for charging and discharging of EVs, with emphasis on convenience, simplicity, flexibility and high efficiency, have become the motivation of current research in academic and industrial communities [55]. The two main charging solutions for EVs are conductive and inductive methods [56]. Conductive charging typically involves a hard-wired connection (electrical contact) between the EV and the source of electricity, or the EV and the load in a discharging scenario. Inductive charging works on the principle of inductive power transfer (IPT), where a magnetic field is used to transfer power across an air gap to a load. Here, no power cable, physical contact or human intervention is required. The exclusion of cables, relatively low maintenance and autonomy for the driver have improved IPT practicality in V2G systems [57]. Although there has been recent progress in inductive methods [58,59], conductive methods remains the most common solution [60].
The time it takes to charge EV batteries is currently longer than the refuelling time of ICE vehicles to satisfy the same driving demands. EV charging rate is determined by how many kilowatts the charge point can provide and the EV can accept—the higher the power output, the faster the charging. Currently, the three main types of EV charging—representing the power outputs and therefore charging speeds—are slow, fast, and rapid. Slow charging (level 1) can be done using existing electrical circuits. Level 1 chargers plug directly into a standard 120 volt AC outlet and are suitable for home or office use cases due to long charging sessions. Unlike the previous case, fast charging (level 2) requires installation of residential or public charging equipment. Level 2 chargers offer charging through a 240 volt AC plug and are largely deployed in public places such as park-and-ride facilities, shopping centres, car parks, airports and universities. While levels 1 and 2 charging are adequate to serve the day-to-day needs of EV owners, long-distance or unplanned trips in EVs need to be considered. Rapid charging (level 3) is available at much higher voltages, often charging using DC, and can achieve 80% of charge in about 30 min, depending on the capacity of the EV [20]. Level 3 chargers are often used as range extenders along major roads and in urban environments to support drivers in urgent need.
To achieve a refuelling time that is comparable to that of ICE vehicles, EVs would need charging stations with much higher power output. Extreme fast charging (XFC) is an emerging technology with potential to address the fast-charge barrier and be truly competitive with the ICE refuelling experience [61]. XFC stations should be able to support charging at 400 kW, recharging an EV in less than 10 minutes and providing up to 200 additional miles of driving [62]. However, there are still many barriers that need to be addressed towards the standardisation and successful implementation of XFC. The technology gaps in XFC topology are identified in [63]. The study in [62] investigates the XFC technology-based charging infrastructure that will be necessary to support current and future EV refuelling needs.
With EV batteries becoming cheaper, automakers are equipping new-model EVs with higher battery capacities. What used to be considered fast charging for a 24 kWh battery is no longer fast when the battery size reaches 60 kWh or more. To address the changing market environment and meet the expectations of EV stakeholders, CHAdeMO has developed an ultra-high-power charging protocol enabling 500 kW charging and allowing for a maximum current of 600 A [64]. This new DC charging standard aims to support shorter and safer charging using ultra-fast charging technology and is another step closer to achieving a refuelling time competitive with ICE refuelling. The background and technical challenges of harmonising this new DC charging standard and its impact on global EV charging infrastructure outlook is presented in [65]. EV charging station characteristics are presented in Table 2.
There is growing interest in the integration of solar photovoltaic into the EV charging system. Solar-powered EV charging stations can help reduce GHG emissions, charging costs and the impact of additional load on the grid. Different technologies for solar-powered EV charging and their deployment in the real world are discussed in [66]. While solar-powered charging stations bring opportunities for EVs, the environment and the grid, the uncertainty and intermittent nature of solar power raises challenges for timely utilization. The concentration of electricity output during the daytime limits the contribution of solar power in meeting a large fraction of typical energy demand. Thus, a grid connection or battery bank is necessary to guarantee effective operation of a solar-powered charging station.

3.3. Load

Load indicates an electrical component (device or machine) or a collection of equipment that consumes electrical energy. Based on demand response management, loads in a building can be divided into two categories—controllable and non-controllable. Non-essential loads that can be deferred or interrupted for a limited period of time with minimal effect on convenience are considered controllable loads. These include air conditioners, water heaters, dishwashers, clothes washers, clothes dryers and EVs. Loads such as lighting, cookers, microwave ovens and other plug loads are considered non-controllable. Building loads in a power system can be categorized into two groups: critical and non-critical. Critical facilities that need to operate during power outages include hospitals, care homes, residential houses with life-support equipment, water and communication infrastructure, control centres, data centres, evacuation centres, emergency shelters, police and fire stations, military bases and airports. Non-critical loads are not essential for human health and safety and are generally all loads not categorized as critical.
Load profile represents the pattern of energy usage of a consumer, both daily (on-peak and off-peak) and seasonally (summer and winter). Modern grids are usually known to be based on the behaviour of consumers to manage the load and supply in the distribution network, where reliable and efficient delivery of electric services are dependent on the load profile. Load forecasting is the predicting of power or energy needed to meet the short-term (up to a day), medium-term (a day up to a year) or long-term (over a year) demand. The load profile can be forecast using techniques such as similar-day, time-series, regression, neutral networks, fuzzy logic, knowledge-based expert systems, adaptive load forecasting, iterative reweighted least-squares and exponential smoothing [67,68]. Accurate forecasting is of significant importance for the planning and operation of electric utilities.

3.4. Advanced Metering Infrastructure

EVaaS applications require smart sensing systems that are able to get information in real-time on power consumption and power quality measurements to support energy management applications [69]. Advanced metering infrastructure (AMI), also known as smart metering, is an essential component in the realization of the smart grid vision [70]. AMI is a configured infrastructure that integrates smart meters, data management systems and communication networks to enable two-way communication between the utility and consumer [71]. AMI provides time-stamped information and establishes two-way communication between smart meters and the utility. With two-way communication, many services that were nearly impossible to implement without smart metering are now applicable. These services include power outage detection, power quality measurements and power flow monitoring. Power flow monitoring information is important as it enables the utility to react rapidly to changes in consumption levels.
Unlike traditional meters, smart meters are self-reading meters that give more detailed information on energy usage in near-real time. A smart meter stores various types of data, such as executed or received commands, event logs, time-of-use tariffs and the firmware. A smart meter has either an Ethernet interface to connect to wireline services, or a direct interface with a wireless service. Smart meter data are collected and transmitted to the utility using a wide area network (WAN) connection. Smart meter connections to a home area network (HAN) are fundamental to residential or building management and allow appliances to respond to time-based pricing signals or other triggers carried over the grid. Key features of smart meters include load limiting and balancing for demand response applications, remote command (turn on/off) operations, power outage detection, time-based pricing, power quality monitoring (active and reactive power, phase, voltage, current and power factor) and power consumption measurement for the utility provider and the consumer.

4. EVaaS Communications

EVaaS communication enables data- and information-sharing among EVaaS subsystems, and it consists of communication infrastructure, such as wired and wireless networks, and processing facilities, such as a data centre and cloud computing. A smart meter facilitates the transmission of data through commonly available fixed wired and wireless networks, such as Fixed Radio Frequency, Power Line Communication (PLC), Broadband over Power Line (BPL), as well as public networks such as cellular, landline and paging. Consumption data from the smart meters are received, stored and analysed to provide useful insights to the utility. A smart meter also responds to remote commands from the utility.

4.1. V2G Communications

V2G communications enable EVs and the grid to interact and exchange information. This is crucial to solve problems related to V2G management. By enabling real-time and reliable communication between EVs and the grid, energy resources distributed over large geographical areas can be managed effectively to enhance overall system performance. The communication network in V2G systems must be bidirectional to ensure substantial information exchange [72]. The system needs information control that is aware of EV location, battery capacity, battery efficiency, SOC, energy price and transportation cost. Transmitting this information and receiving commands over efficient bidirectional communication links is an essential requirement for successful V2G integration. Wireless communication is the ideal solution for V2G systems for various reasons, most notably because EVs are mobile and cannot connect to wireline services. Wireless communication enables the simultaneous transmission of data to dispersed EVs over a wide coverage area.
Different wireless communication technologies that have been implemented for short- and long-range data communication in V2G systems include Near Field Communication [73], Bluetooth [74], Zigbee [75], IEEE 802.11p [76] and WiMAX [77]. Bluetooth and ZigBee protocols are suitable for short-range data communication, such as between an EV and charging station, offering a coverage area of up to 100 m, while Near Field Communication suffers from very short communication range of up to 10 cm [78]. IEEE 802.11p and WiMAX technologies are the standard protocols for long-range communications. The study in [79,80] details the IEEE 802.11p standard and mobile WiMAX (based on IEEE 802.16e standard). IEEE 802.11p technology, which offers a coverage area of up to 1 km, data rates of up to 54 Mbps and latency as low as 50 ms, is the popular standard for vehicular networks. WiMAX technology, on the other hand, has similar features as IEEE 802.11p but offers longer range communication of up to 5 km, higher data transfer speed of up to 100 Mbps and very low delays between 25–40 ms.
Recent studies have investigated the use of wireless communications in V2G environments. The study in [81] details the communication requirements to gather data from various entities such as EVs, the grid and other grid resources, as well as to communicate with EVs for control purposes. The technologies, protocols and block components needed for enabling IP communications in mobile V2G environments are discussed in [82]. A smart charging system that acquires EV data and transmits control instructions to the charging station via GPRS and ZigBee is proposed in [83]. The study in [76] presents two IEEE 802.11p-based quality-of-service schemes that enable interaction between EVs and the grid for coordinated EV charging. The study in [84] modelled the average time delay for a group of charging EVs based on Markov chain representation for the wireless IEEE 802.11 MAC protocol and considered the impact of a lossy wireless link between EVs and the access point. The study in [85] proposed an EV charging management scheme utilizing vehicular communication between EVs and access points based on IEEE 1609 WAVE and IEC 61850 standards. A software-defined networking-based control scheme for vehicular communication networks is developed in [86]. An EV charging scheduling scheme that considers the impact of data communication unavailability on charging station scheduling performance is developed in [87]. A joint optimization model of energy cost and radio usage for discharging EVs in V2G communication networks is proposed in [88].

4.2. Data Analytics

EVs will be an integral part of the modern era of wireless communications that promises to provide low latency and ultra-reliable transmissions [89]; 5G networks aim to support the deployment of vehicles-to-everything (V2X) technologies, cater to explosive, ever-growing data traffic and enable users to indulge in gigabit-speed immersive services capable of extremely low response time regardless of geographical- and time-dependent factors. V2X technology will facilitate autonomous energy trading, where EVs in parking lots can autonomously charge and discharge their batteries, while self-driving EVs can be routed to appropriate charging stations to participate in EVaaS activities. EVaaS requires much shorter network response times and big-data analytics to enable rapid reactions and intelligence across the network. There is no doubt that large amounts of data will be generated by sensors, smart meters, cameras, maps, on-board electronic control units and battery management systems of EVs, databases and more.
EV data that can be used to monitor, analyse and make decisions relating to charging/discharging, energy trading and range estimation mostly come from the on-board electronic control units and the battery management system. EV data can be categorized into three types: standard, historical and real-time data. Standard data include technical specifications from the manufacturer and the usual driving time to destinations according to Google Maps. Historical data include battery management system logs showing start and end times of journeys, as well as SOC information such as connect and disconnect times of charges and discharges. Real-time data include SOC of the EV battery, GPS location of the EV and data closely related to emergency issues, such as unplanned road closures and real-time traffic/weather condition. Internet of things (IoT) enables the recording and transmitting of detailed EV data in on-board computers or cloud computing infrastructure. In the context of the smart grid, IoT is built by integrating internet connectivity into all grid subsystems, connecting them in intelligent networks and utilizing data analytics to extract meaningful and actionable insights from them [90]. Cloud computing provides the virtual infrastructure for data collection, analysis and visualization in the current architecture of IoT.
During mobility, autonomous EVs can generate data up to thousands of gigabytes, with the volume of data dependent on the variety of sensors and cameras used for autonomy. Data generation is not expected to be enormous during EVaaS, but the various sensors collecting data from the grid, EVs, smart meters, charging stations and drivers will need solutions from the big-data domain. Effective integration of data from different sources is possibly an enormous task; however, with the right tools and solutions from the big-data domain, valuable insights can be drawn [91]. Prioritizing the intercepted information is essential, and means of prioritization should be investigated, as decision-makers can only digest and draw insights from a certain amount of information. Furthermore, the processing of EV data by the aggregator makes EVs vulnerable to security and privacy concerns, which are yet to be addressed in the domain of big-data analytics for EVs.

5. Charging Strategies

Large-scale deployment of EVs will result in higher demands on distribution systems, which were not originally designed to withstand a high level of EV penetration [92]. With the expected rise in penetration levels, future EV charging scenarios could be accompanied by numerous challenges. EV charging profiles have an effect on the distribution system. An increasing number of EVs charging adds extra load on distribution systems, which can drastically impact electricity grid stability. These impacts include power quality issues, phase imbalance, transformer degradation and failure, higher system losses and increased operational cost [93]. We review different charging strategies and their impact on distribution systems. This review classifies EV charging strategies into uncoordinated and coordinated strategies.

5.1. Uncoordinated Charging

Uncoordinated charging describes a scenario where the EV batteries either start charging immediately when the EV is plugged into a charge point or after a user-adjustable fixed delay, and they continue charging until the batteries are completely charged or unplugged. In uncoordinated charging, EV charging is presumably at Level 1 with no coordinative control action. Thus, its impact on distribution systems is primarily driven by the stochastic behaviour of the EV user [94]. Load at peak hours tends to increase with uncoordinated charging operations. An increase in peak load can cause severe network stress and overloads in the local distribution grid. Random uncoordinated charging may lead to increased power losses, overloads in transformers and cables, poor voltage profiles, degraded power quality and an overall reduction in the reliability and economy of the distribution grid [95].
Analysis of the impacts of random uncoordinated EV charging on the performance of distribution transformers was carried out in [96]. Results revealed that even under low EV penetrations, transformer load surging and voltage deviations were significant. Load growth on transformers for a low penetration level of 17% to 31% showed a 37% to 74% increase in transformer load current. In [97], a test model using household load profiles for Belgium reports voltage deviations close to 10% during evening peak, for a penetration level of 30%. A typical UK distribution system is studied in [98] to determine the impact of uncontrolled domestic charging on the distribution system. Results show up to 17.9% increase in daily peak demand at 10% penetration rate of EVs, while the peak load would increase by 35.8% at 20% EV penetration. In [92], the impact of EV penetration on existing electricity distribution infrastructure was analysed using data for the Netherlands. Results show that at 30% EV penetration, uncoordinated charging would increase national peak load and household peak load by 7% and 54%, respectively, which may exceed the capacity of the distribution system. The utility operator will have to increase peak generation if the load exceeds peak capacity. The cost of additional generation capacity during peak period is then passed on to EV owners. In [99], uncontrolled charging was shown to cause a 22% increase in the monthly energy bill even at just 10% EV penetration.
Some energy suppliers in the UK offer EV tariffs to help reduce peak demand, redirecting it to off-peak times [100]. The two-rate tariff, which offers cheaper rates during off-peak times (overnight), is designed to encourage EV owners to charge when the energy demand is low and generation is mostly base load. The study in [98] showed that overnight charging increases off-peak energy consumption but had no impact on the daily peak load. In [92], off-peak charging at 30% penetration rate of EVs was reported to cause a 20% higher, more stable base load and no additional peak load on the national grid. Thus, with the introduction of off-peak charging, additional generation capacity would not be required for low EV penetrations.

5.2. Coordinated Charging

Coordinated charging is being investigated as an alternative and possible solution to random uncoordinated charging and its associated problems, respectively. EV charging is most likely at Levels 2 and 3 in coordinated charging [19]. By utilizing the control and bidirectional communications infrastructure of smart grids, smart coordinated charging and discharging of EVs can reduce transformer load surges, line currents, voltage deviations and daily energy costs [95,97,101]. It can also provide efficient energy usage [98] and flatten the voltage profile of a distribution node [102]. Incremental distribution network investment and energy loss costs can be avoided with the implementation of smart charging strategies. Results in [103] showed the possibility of avoiding up to 60–70% of required incremental investment with smart charging. The results of the study in [104] reveal that coordinated charging of EVs minimizes system losses and improves voltage regulation in the distribution grid.
Smart charging and discharging, where EVs charge their batteries from RESs and discharge them during peak demand, is reported to offer the best possible utilization of RESs for cost and emission reductions in the smart grid [105,106,107]. It can improve operational performance in stand-alone operation mode and increase the quantity of RESs installed in islanded microgrids [101]. A control strategy was implemented in [108] to coordinate the charging and discharging of EVs to support a grid with high penetration of wind energy. The obtained results showed that the total power imbalance in the system was significantly suppressed. In [109], coordinated EV charging and discharging was implemented on an Australian distribution grid with solar power generation. The proposed control method was able to cope with solar power uncertainty and efficiently improve grid performance, reducing energy cost and mitigating grid imbalance.
Coordinated charging can be categorized into two types, namely centralized and decentralized approaches. In centralized approaches, EV charging is directly controlled by a centralized unit (microgrid control centre or aggregator). Centralized approaches offer full support for ancillary services. However, only a limited number of charging EVs can be accommodated. Another drawback of this approach is that it involves higher-order complexity. In decentralized approaches, the power of decision-making with regards to EV charging is distributed among individual EVs. The charging behaviour of EVs can be directly influenced by a price signal. Decentralized approaches offer greater scalability and lesser computational complexity. Considering EVs only have to exchange limited information with the aggregator, their privacy is preserved [110]. A drawback to this approach is the need for EVs to collect and store their trip history [111]. Compared with centralized approaches, decentralized approaches are more scalable and flexible and enable EV owners to partake in the decision-making process of EV charging.

6. Energy Trading and Market Design

Advances in V2G promise unprecedented improvements to operational efficiency. This unlocks the possibility of prosumer and consumer participation in energy trading. Consumers equipped with rooftop solar power system can emerge as EV-prosumers and self-supply during peak periods or power outages using V2H integration [112]. This can lead to reduced household energy costs, maximum utilization of solar power generation and minimum dependency of domestic loads on the grid. In an EVaaS energy trading scenario, a grid manager (aggregator) has a demand target and manages individual or aggregated EVs to fulfil demand. Energy trading can be categorized according to the market design. We review two types of energy trading in V2G environments: traditional bilateral energy trading used in the conventional energy industry, and futuristic energy trading with increased distributed influence based on blockchain technology.

6.1. Traditional Energy Trading

The traditional energy industry has operated on a centralized energy trading model for decades. In centralized approaches, one user or a centralized controller dictates to a group of users while acting collectively as one entity. The central controller, acting as an energy broker, is assumed to know all the information about trading entities and tries to match demand and supply. The appropriate framework to employ in a centralized market would be single-objective optimization models such as swarm, stochastic or convex optimization, or social-welfare maximization [113]. Decentralized energy markets enable scalability and competitiveness amongst self-interested EVs compared to their centralized counterparts. Thus, it is important to investigate distributed economic approaches that incentivize energy trading between EVs and the grid [114].
Auctions are a promising market mechanism used to sell (forward auction) or buy (reverse auction) energy in smart grids, with the aggregator acting as auctioneer. In a scenario where energy trade is incentivized, buyers pay a discount in forward auction while sellers receive a premium in reverse auction as compared to the clearing price. The amount of energy to be traded and the final price to be paid is the outcome of the auction. Based on the final payment, there are different auctions schemes, namely, first price auction, second price auction and uniform price auction [115]. Utility-maximizing bidders could misrepresent their valuations (individually or collusively) by not bidding truthfully, which could harm the fairness and efficiency of the trade. Vickrey–Clarke–Groves (VCG) auction is effective in ensuring the properties of truthfulness [116,117,118]. An auction scheme that enables EVs and batteries in swap stations to trade energy is proposed in [119]. A double auction-based approach for enabling EVs to trade their excess energy to the grid is studied in [120]. A double auction mechanism is also studied in [121,122] for energy trading in a two-layer V2G architecture made up of grid-aggregator and aggregator-EV layers. In [123], a group-selling strategy for V2G demand response management is implemented through a two-layer reverse auction.
Game theoretic approaches are another promising solution that have been used in numerous applications to study the interactions among self-interested and independent agents. A game is made up of three essential elements: a set of players, a set of actions (strategies) and a set of payoffs (utility functions). The payoff obtained by the players is the value of the game. One major strategy for game theory is the Nash equilibrium, where no player has any incentive by unilaterally deviating from his/her strategy. Based on players coordinating or competing with themselves, games can be categorized into two types, namely, cooperative games and noncooperative games. Noncooperative games are appropriate in distributed energy trading scenarios between competitive trading entities. Cooperative games are ideal in scenarios where trading entities cooperate with the aid of communication networks in order to optimize the efficiency or social welfare of the collaborators. In [124], an analytical framework that captures the interactions between a smart grid and EV groups is modelled using a noncooperative Stackelberg game. The interactions and energy trading decisions of geographically distributed storage units, such as EVs, are studied in [125] using a noncooperative game. An incentive-based V2V game theoretic approach that captures the coordination strategies of EVs and battery swapping station aggregators is modelled in [126]. In order to incentivize EV participation, each battery-swapping-station aggregator implements a noncooperative game among the EVs in its range through a smart pricing scheme. Collaborative and non-collaborative approaches that consider energy trading and residential load scheduling with EVs are proposed in [127]. The collaborative approach is based on social-welfare maximization, while the non-collaborative approach utilizes a noncooperative game. In addition to auctions and game theory, incentive-based approaches such as pricing, bargain and contract theories, which are able to study the interaction between self-interested participants and improve the efficiency of energy trade, have been widely deployed [114].

6.2. Blockchain-Based Energy Trading

With the rising penetration of EVs, satisfying the ever-increasing energy demand of V2G applications remains a challenge for the distribution system. To address this challenge, recent studies have exploited blockchain technology for energy trading in V2G environments. Blockchain technology enables increased distributed influence in the distribution system while preserving privacy and maintaining transparency and system security [128]. This will improve the flexibility of the conventional energy market, enable a consumer-centric energy market and support prosumer participation. EVs, acting as prosumers or consumers, will be able to trade energy in a peer-to-peer (P2P) manner without third-party intervention, as shown in Figure 2.
The application of blockchain technology for EV-enabled energy trading in smart grids is briefly discussed in [129]. A decentralized security model based on the lightning network and smart contracts is proposed in [130] to protect energy trading transactions between EVs and charging stations. A localized P2P energy trading model based on consortium blockchain is proposed in [131]. The model uses an iterative double auction mechanism to maximize social welfare of charging and discharging EVs. Consortium blockchain has also been exploited in [132] to propose an energy trading model applicable in general scenarios of P2P energy trading. The model uses the Stackelberg game to maximize economic benefits. Blockchain technology was applied in [133] to establish a trusted environment for energy trading between EVs and critical loads, and a prototype was developed for remotely monitoring energy trading activities.
While P2P energy trading is promising, one of its major impediments is regulation. Currently, decentralized energy trading is prohibited by regulation in the UK and some other EU countries; however, this could change in the future. Business owners or individuals who generate electricity are limited to using it on site or selling directly to the utility grid for a nominal price. This poses a major barrier to P2P energy trading, which enables direct trade between prosumers and consumers instead of selling to and buying from the utility grid, respectively. Ideally, the authorization of P2P energy trading will create a competitive energy market and allow prosumers to generate revenue on their excess energy and consumers to obtain cost-effective energy. It is expected that energy prices will drop as a result of eliminating the middle man and with more individuals incentivized to partake in microgeneration.

7. Benefits of V2G

7.1. Ancillary Services

V2G systems facilitate and encourage EVs participation in V2G, where EVs offer various ancillary services to the electric power grid. Ancillary services are essential for balancing demand and supply, maintaining grid reliability and supporting power transmission.

7.1.1. Reserve Power Supply

V2G systems can maintain the balance between demand and supply in electricity grids by injecting power. While the supply capacity of an individual EV is small, aggregated capacity can be significant to provide value to the grid. Aggregators are expected to collect EVs into a group to create a more desirable, larger electricity generation capacity for the utility. For example, by simultaneously discharging their batteries, aggregated EVs will be able to provide additional power required by a commercial building during peak demand, acting like a spinning reserve power generation source in the existing distribution system.

7.1.2. Voltage and Frequency Regulation

V2G systems are capable of regulating voltage and frequency in electricity grids. Frequency regulation provides active power support in the electricity grid. The exact amount of electricity being used needs to be matched by generation, if there is an imbalance, it can affect the frequency of the electricity grid. For example, if electricity demand is more than supply, frequency will fall. If there is too much power being generated in relation to demand, frequency will rise. The frequency will not stabilize until the system is balanced. In the UK, anything just 1% above or below the nominal frequency of 50 Hz risks damaging electrical equipment and infrastructure, including appliances of end users. Currently, frequency regulation is achieved mainly by turning on fast-responding generators to increase power generation, which is costly. Alternatively, fast charging and discharging rates of EV batteries can help to increase the load demand and generation, respectively. This makes V2G a promising alternative for frequency regulation [134,135]. Voltage regulation provides reactive power support in the electricity grid. Reactive power can be controlled by selecting the current phase angle to provide inductive or capacitive action. The consumption of reactive power is mostly through inductive load, which requires the addition of capacitive reactive power to balance the demand. Traditionally, reactive power support is injected at the transmission or distribution grid stage with no involvement from the end-users. However, with increased EV penetration, V2G can provide the necessary reactive power support to the grid.

7.1.3. Peak Shaving and Load Levelling

V2G systems are capable of levelling peak loads in electricity grids. Peak load shaving is achieved through a control strategy that manages EV charging and discharging. In this technique, controllable and aggregated EVs can charge when demand is low (off-peak hours or overnight) and discharge during high demand (peak hours). In scenarios where the generation capacity does not match the peak demand, several problems such as voltage fluctuation, instability and total blackout could possibly occur. Therefore, by shaving peak load, the reliability and stability of the grid is maintained and supply shortage is mitigated. Previous studies have proposed different peak shaving and valley filling techniques through V2G to alleviate the generation–demand imbalance [104,136,137]. This function of V2G can provide economic benefits, as it limits the need to use high-priced peak generators.

7.2. Mobile Backup Power Supply

V2G systems are capable of restoring supply during prolonged grid outages and can improve the grid capabilities to withstand unexpected contingencies. Power systems must not only operate reliably in response to foreseeable contingencies, but must also be resilient to high-impact, low-probability events [138]. Keeping critical loads operating during prolonged grid outages is a key resilience feature for mitigating consequences [139]. Thus, following an unexpected system failure, fast recovery is very essential to enhance grid resilience. EVs, as mobile power generation and storage resources, can distribute the existing energy produced or stored in the local region. Aggregated EVs will be able to provide backup power required by critical loads during a blackout. In a scenario where the distribution network is partly damaged during an extreme event and regular supply cannot reach critical loads, EVs can be deployed to individual locations of the critical load to restore supply [133].

7.3. Renewable Energy Support and Balancing

V2G systems can support intermittent renewable energy in electricity grids. Due to the intermittent nature of wind and solar plants, their large-scale integration into the current electricity grid requires a large-scale, high-capacity storage system [140,141,142]. For instance, peak solar radiation precedes peak demand by a few hours—solar peak power is at noon; peak demand is typically between mid-afternoon to late afternoon. On the other hand, the stochastic nature of wind power is due to unpredictable variations in wind speed. Wind generation fluctuates and cannot be turned up when energy demand increases, leading to imbalances. At low-scale penetration, existing mechanisms for managing supply and demand fluctuations can handle the intermittency of renewable energy. However, at high levels of penetration, additional resources are needed to match the fluctuating supply to the already fluctuating demand. If there is too much energy being generated from renewable sources, generation from conventional power plants must be curtailed to restore balance. EVs can help match generation and consumption by charging and discharging so the utility provider does not consider decreasing the power output. Thus, V2G increases the flexibility of the grid to support intermittent renewables.

7.4. Environmental Benefits

V2G systems can offer societal benefits regarding climate change, GHG emissions and air pollution. Climate change benefits come about via controlled charging (or peak shaving) to limit usage of high-carbon energy sources, by decarbonisation of the ancillary service market and through electrification of the transport sector. The carbon benefits of V2G are mostly dependent on the electricity generation mix of the grid. In electricity grids with high-polluting sources, V2G providing ancillary services has the potential to increase total carbon emissions [143]. EVs cannot guarantee decarbonisation since they do not perform generation. If EVs charge their batteries from a grid with a high penetration of coal in its generation mix, their environmental advantages are more limited. However, if EVs are powered by cleaner energy sources, they can help reduce GHG emissions [144,145]. From a transportation perspective, EV penetration possess potential to reduce air pollution compared to ICE vehicles [146]. Direct emissions from ICE vehicle activity have an effect on public health, agriculture and the natural environment. Thus, high penetration of EVs diminishes health and environmental costs.

8. Challenges to V2G

8.1. Battery Degradation

Despite the many benefits V2G offers, a major concern has been its impact on the degradation of EV batteries. V2G operation imposes more use (and stress) on EV batteries compared to daily driving, which likely accelerates the aging of EV batteries [147]. This can be associated with the increase in charge cycle, where a charge cycle is a complete charge and discharge of the battery. EV battery usage is limited to a fixed number of cycles; over time, the capacity (amount of energy that can be stored or extracted from the battery) degenerates significantly. Determination of V2G impact on EV battery degradation is still at the research stage, with recent studies arriving at contradictory conclusions. While some studies found degradation costs to be a substantial barrier to V2G [148,149], others found degradation to be minimal [150,151]. Nevertheless, even in the best-case scenario, participation in V2G operation accelerates battery capacity degradation beyond what occurs during driving [152]. Consequently, EVs may be expected to undergo battery replacement multiple times over their service life. Thus, V2G influences the frequency of battery replacement and associated costs.

8.2. Energy Conversion Losses

In V2G systems, energy losses occur between the grid connection point and the EV battery. Each time an EV is charged or discharged, energy losses occur in the EV and its supporting electrical infrastructure, such as the charging station, breakers and transformer. Each stage of storage, conversion and transmission contribute to the losses. This is based on the efficiencies of system components such as the EV battery, power electronic unit, charging station, breaker panel and transformer. The impact of different levels of EV penetration on the distribution network was studied in [103]. Under studied conditions, obtained results showed that energy losses could increase up to 40% with the respect to the level of EV penetration. In [153], energy losses from the electricity grid to the EV battery and back to the grid were measured experimentally. The measured total one-way losses were up to 36% under studied conditions. Although studies have reported round-trip losses for EVs and related V2G infrastructure, efficiency values are case-dependent and differ among EVs and electrical circuits. Nevertheless, they can serve as a reference point for future studies on economic analysis of V2G.

8.3. Effects on the Distribution System

The increasing penetration of EVs is likely to have considerable impact on the distribution system. Since the distribution grid is still focused on conventional design and operational rules, service capabilities of V2G devices tend to be limited. The charging and discharging of EVs introduces a change in the overall load profile of the distribution system. Uncontrolled EV charging adds to the pre-existing peak load, especially during fast charging. Load demand is centralized at the fast charging station, and fast charging mainly occurs during the daytime, allowing EVs to draw higher power than a regular household load [154]. The interconnection of fast charging stations with the grid might create negative impacts on the distribution system [155]. Fast charging of EVs could result in detrimental effects on distribution transformers, lower operational efficiency of the distribution network equipment and increase energy losses. Fast charging also has adverse effects on the voltage profile and power quality of the network. Additional EV load increases transformer temperatures, which contributes to insulation breakdown and may decrease the life expectancy of the transformer [156]. The impact of different penetrations of EVs on a residential distribution transformer was studied in [157]. This revealed that high penetration of EVs can have significant impact on the electricity grid, particularly in scenarios with uncoordinated charging. In [158], an investigation was carried out to evaluate some of the effects of EV deployment on an existing distribution network. This revealed that high deployment of EVs could result in supply-and-demand-matching and statutory voltage limit violations, as well as voltage imbalance and power quality problems. In order to help the distribution circuit to accommodate EV penetration, a demand-response strategy is proposed in the context of a smart distribution network in [159]. Thus, the effect of V2G on the distribution network is greatly influenced by the charging strategies and vehicle aggregation [160].

9. Conclusions

V2G technology enables EV deployment as loads to absorb excess production or as generators to feed back surplus energy to the distribution grid during peak demand or system failure. This paper has presented an EVaaS system where EVs, individually or as part of an aggregation, can provide services to the grid, individual customers or both. The EVaaS system architecture and interactions among EVaaS subsystems such as the EV battery, charging stations, loads and advanced metering infrastructure have been discussed. The infrastructure and processing facilities for bidirectional communications in V2G environments have been explained. Several potential battery technologies that might be able to match the widely accepted Li-ion batteries were highlighted. A methodology to enhance grid resilience through building-load categorization was introduced. The impact of coordinated and uncoordinated and fast charging on the distribution grid was discussed. The challenges associated with timely utilization of solar-powered EV charging stations was examined. The centralized structure of conventional energy markets does not allow the full potential of V2G to be realized. The current energy market is not consumer-centric and does not support prosumer participation. Policy change, supporting infrastructure and incentives would play a huge role in maximizing the market opportunities presented by V2G.

Author Contributions

Conceptualization, M.Z.S.; Supervision, M.Z.S.; Writing—original draft, I.A.U.; Writing—review & editing, I.A.U. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Lieven, T. Policy measures to promote electric mobility—A global perspective. Transp. Res. Part A Policy Pract. 2015, 82, 78–93. [Google Scholar] [CrossRef]
  2. Harari, D. SN/EP/5177 Vehicle Scrappage Scheme; Briefing Paper; House of Commons Library: London, UK, 2009. [Google Scholar]
  3. Lenski, S.; Keoleian, G.; Bolon, K. The impact of ‘Cash for Clunkers’ on greenhouse gas emissions: A life cycle perspective. Environ. Res. Lett. 2010, 5, 044003. [Google Scholar] [CrossRef]
  4. Rokadiya, S.; Yang, Z. Overview of Global Zero-Emission Vehicle Mandate Programs; Briefing Paper; International Council on Clean Transportation: San Francisco, CA, USA, 2019. [Google Scholar]
  5. Corporate Average Fuel Economy. NHTSA. Available online: (accessed on 8 April 2021).
  6. Lee, H.; Clark, A. Charging the Future: Challenges and Opportunities for Electric Vehicle Adoption; Harvard Kennedy School: Cambridge, MA, USA, 2018. [Google Scholar]
  7. Healy, J. Guide to the Different Types of Electric Vehicles. 2019. Available online: (accessed on 14 March 2020).
  8. Hirst, D. Electric Vehicles and Infrastructure; Briefing Paper; House of Commons Library: London, UK, 2020. [Google Scholar]
  9. What’s Stopping the EV Revolution? 2017. Available online: (accessed on 2 March 2021).
  10. Der Steen, M.V.; Schelven, R.V.; Kotter, R.; van Twist, M.; van Deventer MPA, P. EV Policy compared: An international comparison of governments’ policy strategy towards E-Mobility. In E-Mobility in Europe; Springer: Cham, Switzerland, 2015; pp. 27–53. [Google Scholar]
  11. Birrell, S.A.; McGordon, A.; Jennings, P.A. Defining the accuracy of real-world range estimations of an electric vehicle. In Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8–11 October 2014; pp. 2590–2595. [Google Scholar]
  12. Wang, J.; Liu, K.; Yamamoto, T. Improving electricity consumption estimation for electric vehicles based on sparse GPS observations. Energies 2017, 10, 129. [Google Scholar] [CrossRef]
  13. Guille, C.; Gross, G. A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy 2009, 37, 4379–4390. [Google Scholar] [CrossRef]
  14. Kempton, W.; Letendre, S. Electric Vehicles as a New Power Source for Electric Utilities. Transp. Res. Part D Transp. Environ. 1997, 2, 157–175. [Google Scholar] [CrossRef]
  15. Shin, H.; Baldick, R. Plug-In Electric Vehicle to Home (V2H) Operation Under a Grid Outage. IEEE Trans. Smart Grid 2017, 8, 2032–2041. [Google Scholar] [CrossRef]
  16. Pang, C.; Dutta, P.; Kezunovic, M. BEVs/PHEVs as Dispersed Energy Storage for V2B Uses in the Smart Grid. IEEE Trans. Smart Grid 2012, 3, 473–482. [Google Scholar] [CrossRef]
  17. Koufakis, A.; Rigas, E.; Bassiliades, N.; Ramchurn, S. Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer. IEEE Trans. Intell. Transp. Syst. 2019, 21, 2128–2138. [Google Scholar] [CrossRef]
  18. Everoze. V2G Global Roadtrip: Around the World in 50 Projects. 2018. Available online: (accessed on 13 April 2020).
  19. Yilmaz, M.; Krein, P. Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces. IEEE Trans. Power Electron. 2013, 28, 5673–5689. [Google Scholar] [CrossRef]
  20. Painuli, S.; Rawat, M.; Rayudu, D.R. A comprehensive review on electric vehicles operation, development and grid stability. In Proceedings of the 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), Greater Noida, India, 13–14 April 2018; pp. 106–110. [Google Scholar]
  21. Shariff, S.; Iqbal, D.; Alam, M.S.; Ahmad, F. A state of the art review of electric vehicle to grid (V2G) technology. IOP Conf. Ser. Mater. Sci. Eng. 2019, 561, 012103. [Google Scholar] [CrossRef]
  22. Habib, S.; Khan, M.M.; Abbas, F.; Sang, L.; Shahid, M.U.; Tang, H. A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles. IEEE Access 2018, 6, 13866–13890. [Google Scholar] [CrossRef]
  23. Ravi, S.S.; Aziz, M. Utilization of electric vehicles for vehicle-to-grid services: Progress and perspectives. Energies 2022, 15, 589. [Google Scholar] [CrossRef]
  24. Umoren, I.; Shakir, M. EVaaS: A novel on-demand outage mitigation framework for electric vehicle enabled microgrids. In Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, 9–13 December 2018; pp. 1–6. [Google Scholar]
  25. Aujla, G.; Jindal, A.; Kumar, N. EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system. Comput. Netw. 2018, 143, 247–262. [Google Scholar] [CrossRef]
  26. Weiss, H.; Winkler, T.; Ziegerhofer, H. Large lithium-ion battery-powered electric vehicles—From idea to reality. In Proceedings of the 2018 ELEKTRO, Mikulov, Czech Republic, 21–23 May 2018; pp. 1–5. [Google Scholar]
  27. Burke, A. Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles. Proc. IEEE 2007, 95, 806–820. [Google Scholar] [CrossRef]
  28. BU-1003: Electric Vehicle (EV). Available online: (accessed on 15 June 2020).
  29. Hannan, M.; Wali, S.B.; Ker, P.J.; Abd Rahman, M.S.; Mansor, M.; Ramachandaramurthy, V.K.; Muttaqi, K.; Mahlia, T.; Dong, Z. Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues. J. Energy Storage 2021, 42, 103023. [Google Scholar] [CrossRef]
  30. Ahmad, F.; Khalid, M.; Panigrahi, B. Development in energy storage system for electric transportation: A comprehensive review. J. Energy Storage 2021, 43, 103153. [Google Scholar] [CrossRef]
  31. Nissan. Nissan Price & Specifications | Electric Cars | Nissan UK. 2022. Available online: (accessed on 19 September 2022).
  32. BMW. The All-New BMW i4: Engine & Technical Data. 2022. Available online: (accessed on 19 September 2022).
  33. Audi. Audi e-tron 55 Quattro (300 kW) Data Sheet. 2022. Available online: (accessed on 19 September 2022).
  34. Chevrolet. Chevrolet Bolt EV-2021. 2021. Available online: (accessed on 19 September 2022).
  35. Hyundai. Discover the Hyundai IONIQ Electric-Specs & Colours | Hyundai UK. 2022. Available online: (accessed on 19 September 2022).
  36. Volkswagen. New Volkswagen e-Golf | Volkswagen UK. 2022. Available online: (accessed on 19 September 2022).
  37. Mercedes Benz. Charging and Range. 2022. Available online: (accessed on 19 September 2022).
  38. Kia. Kia Soul EV Specifications & Features | Kia UK. 2022. Available online: (accessed on 19 September 2022).
  39. Jaguar. Pricing & Specifications | Jaguar I-PACE | Jaguar UK. 2022. Available online: (accessed on 19 September 2022).
  40. Tesla. Model S. 2022. Available online: (accessed on 19 September 2022).
  41. Renault. ZOE—Driving Range, Battery & Charging—Renault UK. 2022. Available online: (accessed on 19 September 2022).
  42. Peugeot. Peugeot E-208, 100% Electric. 2022. Available online: (accessed on 19 September 2022).
  43. Vauxhall. Corsa Electric. 2022. Available online: (accessed on 19 September 2022).
  44. Ecker, M.; Nieto, N.; Käbitz, S.; Schmalstieg, J.; Blanke, H.; Warnecke, A.; Sauer, D.U. Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. J. Power Source 2014, 248, 839–851. [Google Scholar] [CrossRef]
  45. Zhou, C.; Qian, K.; Allan, M.; Zhou, W. Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems. IEEE Trans. Energy Convers. 2011, 26, 1041–1050. [Google Scholar] [CrossRef]
  46. Doyle, M.; Fuller, T.; Newman, J. Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell. J. Electrochem. Soc. 1993, 140, 1526–1533. [Google Scholar] [CrossRef]
  47. Rao, R.; Vrudhula, S.; Rakhmatov, D. Battery modeling for energy-aware system design. Computer 2003, 36, 77–87. [Google Scholar]
  48. Dogger, J.; Roossien, B.; Nieuwenhout, F. Characterization of Li-ion Batteries for Intelligent Management of Distributed Grid-Connected Storage. IEEE Trans. Energy Convers. 2011, 26, 256–263. [Google Scholar] [CrossRef] [Green Version]
  49. Liu, Y.; Hsieh, C.; Luo, Y. Search for an Optimal Five-Step Charging Pattern for Li-ion Batteries Using Consecutive Orthogonal Arrays. IEEE Trans. Energy Convers. 2011, 26, 654–661. [Google Scholar] [CrossRef]
  50. Gomadam, P.; Weidner, J.; Dougal, R.; White, R. Mathematical modeling of lithium-ion and nickel battery systems. J. Power Source 2002, 110, 267–284. [Google Scholar] [CrossRef]
  51. Thirugnanam, K.; Reena, E.; Singh, M.; Kumar, P. Mathematical Modeling of Li-ion Battery Using Genetic Algorithm Approach for V2G Applications. IEEE Trans. Energy Convers. 2014, 29, 332–343. [Google Scholar]
  52. Liaw, B.; Nagasubramanian, G.; Jungst, R.; Doughty, D. Modeling of lithium ion cells—A simple equivalent-circuit model approach. Solid State Ionics 2004, 175, 835–839. [Google Scholar]
  53. Kroeze, R.; Krein, P. Electrical battery model for use in dynamic electric vehicle simulations. In Proceedings of the 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, 15–19 June 2008; pp. 1336–1342. [Google Scholar]
  54. Kumar, P.; Bauer, P. Parameter extraction of battery models using multiobjective optimization genetic algorithms. In Proceedings of the 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010, Ohrid, Macedonia, 6–8 September 2010; pp. 106–110. [Google Scholar]
  55. Khalid, M.; Ahmad, F.; Panigrahi, B.; Al-Fagih, L. A comprehensive review on advanced charging topologies and methodologies for electric vehicle battery. J. Energy Storage 2022, 53, 105084. [Google Scholar] [CrossRef]
  56. Khaligh, A.; Dusmez, S. Comprehensive Topological Analysis of Conductive and Inductive Charging Solutions for Plug-In Electric Vehicles. IEEE Trans. Veh. Technol. 2012, 61, 3475–3489. [Google Scholar] [CrossRef]
  57. Madawala, U.; Thrimawithana, D. A Bidirectional Inductive Power Interface for Electric Vehicles in V2G Systems. IEEE Trans. Ind. Electron. 2011, 58, 4789–4796. [Google Scholar] [CrossRef]
  58. Egan, M.; O’Sullivan, D.; Hayes, J.; Willers, M.; Henze, C. Power-Factor-Corrected Single-Stage Inductive Charger for Electric Vehicle Batteries. IEEE Trans. Ind. Electron. 2007, 54, 1217–1226. [Google Scholar] [CrossRef]
  59. Musavi, F.; Edington, M.; Eberle, W. Wireless power transfer: A survey of EV battery charging technologies. In Proceedings of the 2012 IEEE Energy Conversion Congress and Exposition (ECCE), Raleigh, NC, USA, 15–20 September 2012; pp. 1804–1810. [Google Scholar]
  60. Gautam, D.; Musavi, F.; Edington, M.; Eberle, W.; Dunford, W. An Automotive Onboard 3.3-kW Battery Charger for PHEV Application. IEEE Trans. Veh. Technol. 2012, 61, 3466–3474. [Google Scholar] [CrossRef]
  61. Howell, D.; Boyd, S.; Cunningham, B.; Gillard, S.; Slezak, L. Enabling Fast Charging: A Technology Gap Assessment; Technical Report; U.S. Deptartment Energy, Office of Energy Efficiency and Renewable Energy (EERE): Washington, DC, USA, 2017. [Google Scholar]
  62. Tu, H.; Feng, H.; Srdic, S.; Lukic, S. Extreme fast charging of electric vehicles: A technology overview. IEEE Trans. Transp. Electrif. 2019, 5, 861–878. [Google Scholar] [CrossRef]
  63. Deb, N.; Singh, R.; Brooks, R.; Bai, K. A review of extremely fast charging stations for electric vehicles. Energies 2021, 14, 7566. [Google Scholar] [CrossRef]
  64. CHAdeMO Association. First Next Generation, Ultra High-Power Charging Protocol Test/Demo Successfully Completed. 2020. Available online: (accessed on 15 September 2022).
  65. Blech, T. Project Chaoji: The background and challenges of harmonising DC charging standards. In Proceedings of the 33rd Electric Vehicles Symposium (EV33), Portland, OR, USA, 14–17 June 2020; pp. 1–12. [Google Scholar]
  66. Khan, S.; Ahmad, A.; Ahmad, F.; Shemami, M.S.; Alam, M.S.; Khateeb, S. A comprehensive review on solar powered electric vehicle charging system. Smart Sci. 2017, 6, 54–79. [Google Scholar] [CrossRef]
  67. Alfares, H.; Nazeeruddin, M. Electric load forecasting: Literature survey and classification of methods. Int. J. Syst. Sci. 2002, 33, 23–34. [Google Scholar] [CrossRef]
  68. Taylor, J.; de Menezes, L.; McSharry, P. A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int. J. Forecast. 2006, 22, 1–16. [Google Scholar] [CrossRef]
  69. Morello, R.; Mukhopadhyay, S.; Liu, Z.; Slomovitz, D.; Samantaray, S. Advances on Sensing Technologies for Smart Cities and Power Grids: A Review. IEEE Sens. J. 2017, 17, 7596–7610. [Google Scholar] [CrossRef]
  70. Ghosal, A.; Conti, M. Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey. IEEE Commun. Surv. Tuts. 2019, 21, 2831–2848. [Google Scholar] [CrossRef]
  71. Mohassel, R.; Fung, A.; Mohammadi, F.; Raahemifar, K. A survey on Advanced Metering Infrastructure. Int. J. Electr. Power Energy Syst. 2014, 63, 473–484. [Google Scholar] [CrossRef]
  72. Tuttle, D.; Baldick, R. The Evolution of Plug-In Electric Vehicle-Grid Interactions. IEEE Trans. Smart Grid 2012, 3, 500–505. [Google Scholar] [CrossRef]
  73. Steffen, R.; Preißinger, J.; Schöllermann, T.; Müller, A.; Schnabel, I. Near Field Communication (NFC) in an automotive environment. In Proceedings of the 2010 Second International Workshop on Near Field Communication, Monaco, Monaco, 20 April 2010; pp. 15–20. [Google Scholar]
  74. Conti, M.; Fedeli, D.; Virgulti, M. B4V2G: Bluetooth for electric vehicle to smart grid connection. In Proceedings of the 9th International Workshop on Intelligent Solutions in Embedded Systems, Regensburg, Germany, 7–8 July 2011; pp. 1–6. [Google Scholar]
  75. Lam, K.; Ko, K.; Tung, H.; Tung, H.; Tsang, K.; Lai, L. ZigBee electric vehicle charging system. In Proceedings of the 2011 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 9–12 January 2011; pp. 507–508. [Google Scholar]
  76. Al-Anbagi, I.; Mouftah, H. WAVE 4 V2G: Wireless access in vehicular environments for Vehicle-to-Grid applications. Veh. Commun. 2016, 3, 31–42. [Google Scholar] [CrossRef]
  77. Jatav, V.; Singh, V. Mobile WiMAX network security threats and solutions: A survey. In Proceedings of the 2014 International Conference on Computer and Communication Technology (ICCCT), Allahabad, India, 26–28 September 2014; pp. 135–140. [Google Scholar]
  78. Hoang, D.; Wang, P.; Niyato, D.; Hossain, E. Charging and discharging of plug-in electric vehicles (PEVs) in vehicle-to-grid (V2G) systems: A cyber insurance-based model. IEEE Access 2017, 5, 732–754. [Google Scholar] [CrossRef]
  79. Msadaa, I.; Cataldi, P.; Filali, F. A comparative study between 802. 11p and mobile WiMAX-based V2I communication networks. In Proceedings of the 2010 4th International Conference on Next Generation Mobile Applications, Services and Technologies, Amman, Jordan, 27–29 July 2010; pp. 186–191. [Google Scholar]
  80. Ribeiro, C. Bringing the wireless access to the automobile: A comparison of Wi-Fi, WiMAX, MBWA, and 3G. In Proceedings of the 21st Computer Science Seminar; 2005; pp. 1–7. Available online: (accessed on 1 June 2022).
  81. Jansen, B.; Binding, C.; Sundstrom, O.; Gantenbein, D. Architecture and communication of an electric vehicle virtual power plant. In Proceedings of the 2010 1st IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, 4–6 October 2010; pp. 149–154. [Google Scholar]
  82. Cespedes, S.; Shen, X. A framework for ubiquitous IP communications in vehicle to grid networks. In Proceedings of the 2011 IEEE Globecom Workshops (GC Wkshps), Houston, TX, USA, 5–9 December 2011; pp. 149–154. [Google Scholar]
  83. Yuan, Z.; Xuand, H.; Han, H.; Zhao, Y. Research of smart charging management system for electric vehicles based on wireless communication networks. In Proceedings of the 2012 IEEE 6th International Conference on Information and Automation for Sustainability, Beijing, China, 27–29 September 2012; pp. 242–247. [Google Scholar]
  84. Bilh, A.; Naik, K.; El-Shatshat, R. Evaluating electric vehicles’ response time to regulation signals in smart grids. IEEE Trans. Ind. Inf. 2018, 14, 1210–1219. [Google Scholar] [CrossRef]
  85. Hussain, S.; Ustun, T.; Nsonga, P.; Ali, I. IEEE 1609 WAVE and IEC 61850 standard communication based integrated EV charging management in smart grids. IEEE Trans. Veh. Technol. 2018, 67, 7690–7697. [Google Scholar] [CrossRef]
  86. Huang, W.; Ding, L.; Meng, D.; Hwang, J.; Xu, Y.; Zhang, W. QoE-based resource allocation for heterogeneous multi-radio communication in software-defined vehicle networks. IEEE Access 2018, 6, 3387–3399. [Google Scholar] [CrossRef]
  87. Dong, Q.; Niyato, D.; Wang, P.; Han, Z. An adaptive scheduling of PHEV charging: Analysis under imperfect data communication. In Proceedings of the 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, BC, Canada, 21–24 October 2013; pp. 205–210. [Google Scholar]
  88. Umoren, I.; Shakir, M.; Tabassum, H. Resource Efficient Vehicle-to-Grid (V2G) Communication Systems for Electric Vehicle Enabled Microgrids. IEEE Trans. Intell. Transp. Syst. 2021, 22, 4171–4180. [Google Scholar] [CrossRef]
  89. Shah, S.; Ahmed, E.; Imran, M.; Zeadally, S. 5G for Vehicular Communications. IEEE Commun. Mag. 2018, 56, 111–117. [Google Scholar] [CrossRef]
  90. Donitzky, C.; Roos, O.; Sauty, S. Digital Energy Network: The Internet of Things and the Smart Grid. Intel. 2014. Available online: (accessed on 30 May 2020).
  91. Li, B.; Kisacikoglu, M.; Liu, C.; Singh, N.; Erol-Kantarci, M. Big Data Analytics for Electric Vehicle Integration in Green Smart Cities. IEEE Commun. Mag. 2017, 55, 19–25. [Google Scholar] [CrossRef]
  92. Van Vliet, O.; Brouwer, A.; Kuramochi, T.; van den Broek, M.; Faaij, A. Energy use, cost and CO2 emissions of electric cars. J. Power Source 2011, 196, 2298–2310. [Google Scholar] [CrossRef]
  93. Liu, R.; Dow, L.; Liu, E. A survey of PEV impacts on electric utilities. In Proceedings of the ISGT 2011, Anaheim, CA, USA, 17–19 January 2011; pp. 1–8. [Google Scholar]
  94. Galus, M.; Zima, M.; Andersson, G. On integration of plug-in hybrid electric vehicles into existing power system structures. Energy Policy 2010, 38, 6736–6745. [Google Scholar] [CrossRef]
  95. Masoum, M.; Moses, P.; Hajforoosh, S. Distribution transformer stress in smart grid with coordinated charging of plug-in electric vehicles. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012; pp. 1–8. [Google Scholar]
  96. Moses, P.; Masoum, M.; Hajforoosh, S. Overloading of distribution transformers in smart grid due to uncoordinated charging of plug-In electric vehicles. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012; pp. 1–6. [Google Scholar]
  97. Clement-Nyns, K.; Haesen, E.; Driesen, J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans. Power Syst. 2010, 25, 371–380. [Google Scholar] [CrossRef]
  98. Qian, K.; Zhou, C.; Allan, M.; Yuan, Y. Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans. Power Syst. 2011, 26, 802–810. [Google Scholar] [CrossRef]
  99. Halbleib, A.; Turner, M.; Naber, J. Control of battery electric vehicle charging for commercial time of day demand rate payers. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012; pp. 1–5. [Google Scholar]
  100. What are the Best EV Energy Tariffs in the UK? 2022. Available online: (accessed on 1 June 2022).
  101. Lopes, J.; Soares, F.; Almeida, P. Integration of electric vehicles in the electric power system. Proc. IEEE 2011, 99, 168–183. [Google Scholar] [CrossRef]
  102. Singh, M.; Kar, I.; Kumar, P. Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss. In Proceedings of the 14th International Power Electronics and Motion Control Conference EPE-PEMC 2010, Ohrid, Macedonia, 6–8 September 2010; pp. 196–203. [Google Scholar]
  103. Fernandez, L.P.; Roman, T.G.S.; Cossent, R.; Domingo, C.M.; Frias, P. Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Trans. Power Syst. 2011, 26, 206–213. [Google Scholar] [CrossRef]
  104. Sortomme, E.; Hindi, M.; MacPherson, S.; Venkata, S. Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses. IEEE Trans. Smart Grid 2011, 2, 198–205. [Google Scholar] [CrossRef]
  105. Saber, A.; Venayagamoorthy, G. Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions. IEEE Trans. Ind. Electron. 2011, 58, 1229–1238. [Google Scholar] [CrossRef]
  106. Al-Awami, A.; Sortomme, E. Coordinating vehicle-to-grid services with energy trading. IEEE Trans. Smart Grid 2012, 3, 453–462. [Google Scholar] [CrossRef]
  107. Umoren, I.; Shakir, M. Combined Economic Emission Based Resource Allocation for Electric Vehicle Enabled Microgrids. IET Smart Grid 2020, 3, 768–776. [Google Scholar] [CrossRef]
  108. Nguyen, H.; Zhang, C.; Mahmud, M. Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy. IEEE Trans. Transp. Electrif. 2015, 1, 188–195. [Google Scholar] [CrossRef]
  109. Islam, M.; Lu, H.; Hossain, M.; Li, L. Coordinating electric vehicles and distributed energy sources constrained by user’s travel commitment. IEEE Trans. Ind. Inf. 2022, 18, 5307–5317. [Google Scholar] [CrossRef]
  110. Xing, H.; Fu, M.; Lin, Z.; Mou, Y. Coordinating electric vehicles and distributed energy sources constrained by user’s travel commitment. IEEE Trans. Power Syst. 2016, 31, 4118–4127. [Google Scholar] [CrossRef]
  111. Sundstrom, O.; Binding, C. Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans. Smart Grid 2012, 3, 26–37. [Google Scholar] [CrossRef]
  112. Shamami, M.; Alam, M.; Ahmad, F.; Shariff, S.; AlSaidan, I.; Rafat, Y.; Asghar, M. Artificial intelligence-based performance optimization of electric vehicle-to-home (V2H) energy management system. SAE J. STEEP 2020, 1, 115–125. [Google Scholar] [CrossRef]
  113. Bayram, I.; Shakir, M.; Abdallah, M.; Qaraqe, K. A survey on energy trading in smart grid. In Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, USA, 3–5 December 2014; pp. 258–262. [Google Scholar]
  114. Zhang, K.; Mao, Y.; Leng, S.; Maharjan, S.; Zhang, Y.; Vinel, A.; Jonsson, M. Incentive-Driven Energy Trading in the Smart Grid. IEEE Access 2016, 4, 1243–1257. [Google Scholar] [CrossRef]
  115. Klemperer, P. Auction theory: A guide to the literature. J. Econ. Surv. 1999, 13, 227–286. [Google Scholar] [CrossRef]
  116. Vickrey, W. Counterspeculation, auctions, and competitive sealed tenders. J. Financ. 1961, 16, 8–37. [Google Scholar] [CrossRef]
  117. Clarke, E. Multipart pricing of public goods. Public Choice 1971, 11, 17–33. [Google Scholar] [CrossRef]
  118. Groves, T. Incentives in teams. Econometrica 1973, 41, 617–631. [Google Scholar] [CrossRef]
  119. Zhai, H.; Chen, S.; An, D. ExPO: Exponential-based privacy preserving online auction for electric vehicles demand response in microgrid. In Proceedings of the 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, 13–14 August 2017; pp. 126–131. [Google Scholar]
  120. Saad, W.; Han, Z.; Poor, H.; Basar, T. A noncooperative game for double auction-based energy trading between PHEVs and distribution grids. In Proceedings of the 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 17–20 October 2011; pp. 267–272. [Google Scholar]
  121. Lam, A.; Huang, L.; Silva, A.; Saad, W. A multi-layer market for vehicle-to-grid energy trading in the smart grid. In Proceedings of the 2012 IEEE INFOCOM Workshops, Orlando, FL, USA, 25–30 March 2012; pp. 85–90. [Google Scholar]
  122. Zhong, W.; Xie, K.; Liu, Y.; Yang, C.; Xie, S. Efficient auction mechanisms for two-layer vehicle-to-grid energy trading in smart grid. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6. [Google Scholar]
  123. Zeng, M.; Leng, S.; Maharjan, S.; Gjessing, S.; He, J. An Incentivized Auction-Based Group-Selling Approach for Demand Response Management in V2G Systems. IEEE Trans. Ind. Inf. 2015, 11, 1554–1563. [Google Scholar] [CrossRef]
  124. Tushar, W.; Saad, W.; Poor, H.V.; Smith, D. Economics of Electric Vehicle Charging: A Game Theoretic Approach. IEEE Trans. Smart Grid 2012, 3, 1767–1778. [Google Scholar] [CrossRef]
  125. Wang, Y.; Saad, W.; Han, Z.; Poor, H.; Basar, T. A Game-Theoretic Approach to Energy Trading in the Smart Grid. IEEE Trans. Smart Grid 2014, 5, 1439–1450. [Google Scholar] [CrossRef]
  126. Ahmad, F.; Alam, M.; Shariff, S. A cost-efficient energy management system for battery swapping station. IEEE Syst. J. 2019, 13, 4355–4364. [Google Scholar] [CrossRef]
  127. Kim, B.; Ren, S.; van der Schaar, M.; Lee, J. Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid. IEEE J. Sel. Areas Commun. 2013, 31, 1219–1234. [Google Scholar] [CrossRef]
  128. Aitzhan, N.; Svetinovic, D. Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams. IEEE Trans. Depend. Sec. Comput. 2018, 15, 840–852. [Google Scholar] [CrossRef]
  129. Musleh, A.; Yao, G.; Muyeen, S. Blockchain Applications in Smart Grid-Review and Frameworks. IEEE Access 2019, 7, 86746–86757. [Google Scholar] [CrossRef]
  130. Huang, X.; Xu, C.; Wang, P.; Liu, H. LNSC: A Security Model for Electric Vehicle and Charging Pile Management Based on Blockchain Ecosystem. IEEE Access 2018, 6, 13565–13574. [Google Scholar] [CrossRef]
  131. Kang, J.; Yu, R.; Huang, X.; Maharjan, S.; Zhang, Y.; Hossain, E. Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains. IEEE Trans. Ind. Inf. 2017, 13, 3154–3164. [Google Scholar] [CrossRef]
  132. Li, Z.; Kang, J.; Yu, R.; Ye, D.; Deng, Q.; Zhang, Y. Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things. IEEE Trans. Ind. Inf. 2018, 14, 3690–3700. [Google Scholar] [CrossRef]
  133. Umoren, I.; Jaffary, S.; Shakir, M.; Katzis, K.; Ahmadi, H. Blockchain-Based Energy Trading in Electric Vehicle Enabled Microgrids. IEEE Consum. Electron. Mag. 2020, 9, 66–71. [Google Scholar] [CrossRef]
  134. Han, S.; Han, S.; Sezaki, K. Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation. IEEE Trans. Smart Grid 2010, 1, 65–72. [Google Scholar]
  135. Liu, C.; Chau, K.; Wu, D.; Gao, S. Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies. Proc. IEEE 2013, 101, 2409–2427. [Google Scholar] [CrossRef]
  136. Deilami, S.; Masoum, A.; Moses, P.; Masoum, M. Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile. IEEE Trans. Smart Grid 2011, 2, 456–467. [Google Scholar] [CrossRef]
  137. Gan, L.; Topcu, U.; Low, S. Optimal Decentralized Protocol for Electric Vehicle Charging. IEEE Trans. Power Syst. 2013, 28, 940–951. [Google Scholar] [CrossRef]
  138. Panteli, M.; Mancarella, P. Modeling and evaluating the resilience of critical electrical power infrastructure to extreme weather events. IEEE Syst. J. 2017, 11, 1733–1742. [Google Scholar] [CrossRef]
  139. Che, L.; Khodayar, M.; Shahidehpour, M. Only connect: Microgrids for distribution system restoration. IEEE Power Energy Mag. 2014, 12, 70–81. [Google Scholar]
  140. Kempton, W.; Tomić, J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. J. Power Sources 2005, 144, 280–294. [Google Scholar] [CrossRef]
  141. Birnie, D. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. J. Power Sources 2009, 186, 539–542. [Google Scholar] [CrossRef]
  142. Pillai, J.; Bak-Jensen, B. Integration of Vehicle-to-Grid in the Western Danish Power System. IEEE Trans. Sustain. Energy 2010, 2, 12–19. [Google Scholar] [CrossRef]
  143. Hoehne, C.; Chester, M. Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions. Energy 2016, 115, 646–657. [Google Scholar] [CrossRef]
  144. Sioshansi, R.; Denholmn, P. Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services. Environ. Sci. Technol. 2009, 43, 1199–1204. [Google Scholar] [CrossRef]
  145. Saber, A.; Venayagamoorthy, G. Intelligent unit commitment with vehicle-to-grid—A cost-emission optimization. J. Power Sources 2010, 195, 898–911. [Google Scholar] [CrossRef]
  146. Buekers, J.; Holderbeke, M.V.; Bierkens, J.; Panis, L.I. Health and environmental benefits related to electric vehicle introduction in EU countries. Transp. Res. Part D Transp. Environ. 2004, 33, 26–38. [Google Scholar] [CrossRef]
  147. Jafari, M.; Gauchia, A.; Zhao, S.; Zhang, K.; Gauchia, L. Electric Vehicle Battery Cycle Aging Evaluation in Real-World Daily Driving and Vehicle-to-Grid Services. IEEE Trans. Transport. Electrific. 2018, 4, 122–134. [Google Scholar] [CrossRef]
  148. Dubarry, M.; Devie, A.; McKenzie, K. Intelligent unit commitment with vehicle-to-grid—A cost-emission optimization. J. Power Sources 2017, 358, 39–49. [Google Scholar] [CrossRef]
  149. Hu, X.; Martinez, C.; Yang, Y. Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: A unified cost-optimal approach. Mech. Syst. Signal Process. 2017, 87, 4–16. [Google Scholar] [CrossRef]
  150. Wang, D.; Coignard, J.; Zeng, T.; Zhang, C.; Saxena, S. Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services. J. Power Sources 2016, 332, 193–203. [Google Scholar] [CrossRef] [Green Version]
  151. Uddin, K.; Jackson, T.; Widanage, W.; Chouchelamane, G.; Jennings, P.; Marco, J. On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system. Energy 2017, 133, 710–722. [Google Scholar] [CrossRef]
  152. Bishop, J.; Axon, C.; Bonilla, D.; Tran, M.; Banister, D.; McCulloch, M. Evaluating the impact of V2G services on the degradation of batteries in PHEV and EV. Appl. Energy 2013, 111, 206–218. [Google Scholar] [CrossRef]
  153. Apostolaki-Iosifidou, E.; Codani, P.; Kempton, W. Measurement of power loss during electric vehicle charging and discharging. Energy 2017, 127, 730–742. [Google Scholar] [CrossRef]
  154. Wang, L.; Qin, Z.; Slangen, T.; Bauer, P.; van Wijk, T. Grid impact of electric vehicle fast charging stations: Trends, standards, issues and mitigation measures—An overview. IEEE Open J. Power Electron. 2021, 2, 56–74. [Google Scholar] [CrossRef]
  155. Khan, W.; Ahmad, A.; Ahmad, F.; Alam, M.S. A comprehensive review of fast charging infrastructure for electric vehicles. Smart Sci. 2018, 6, 256–270. [Google Scholar] [CrossRef]
  156. Farmer, C.; Hines, P.; Dowds, J.; Blumsack, S. Modeling the impact of increasing PHEV loads on the distribution infrastructure. In Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, Honolulu, HI, USA, 5–8 January 2010; pp. 1–10. [Google Scholar]
  157. Gong, Q.; Midlam-Mohler, S.; Marano, V.; Rizzoni, G. Study of PEV Charging on Residential Distribution Transformer Life. IEEE Trans. Smart Grid 2012, 3, 404–412. [Google Scholar] [CrossRef]
  158. Putrus, G.; Suwanapingkarl, P.; Johnston, D.; Bentley, E.; Narayana, M. Impact of electric vehicles on power distribution networks. In Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA, 7–10 September 2009; pp. 827–831. [Google Scholar]
  159. Shao, S.; Pipattanasomporn, M.; Rahman, S. Grid Integration of Electric Vehicles and Demand Response with Customer Choice. IEEE Trans. Smart Grid 2012, 3, 543–550. [Google Scholar] [CrossRef]
  160. Habib, S.; Kamran, M.; Rashid, U. Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks—A review. J. Power Source 2015, 277, 205–214. [Google Scholar] [CrossRef]
Figure 1. EVaaS system architecture.
Figure 1. EVaaS system architecture.
Energies 15 07207 g001
Figure 2. Types of energy trading in V2G environments.
Figure 2. Types of energy trading in V2G environments.
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Table 1. Overview of different EV passenger models considering battery technology, capacity and charge times.
Table 1. Overview of different EV passenger models considering battery technology, capacity and charge times.
EV ModelBattery TechnologyCapacityCharge Times
Nissan Leaf e+ Tekna [31]Li-ion59 kWh100% charge in 11.5 h
on 6.6 kW AC
80% charge in 1.5 h
on 50 kW DC
BMW i4 [32]Li-ion80.7 kWh100% charge in 8.25 h
on 11 kW AC
80% charge in 31 min
on 205 kW DC
Audi e-tron [33]Li-ion86 kWh100% charge in 9.25 h
on 11 kW AC
80% charge in 30 min
on 150 kW DC
Chevrolet Bolt [34]Li-ion66 kWh100% charge in 10 h
on 7.2 kW AC
80% charge in 1 h
on 50 kW DC
Hyundai Ioniq Electric [35]Li-ion Polymer38.3 kWh100% charge in 6 h
on 7.2 kW AC
80% charge in 57 min
on 50 kW DC
Volkswagen e-Golf [36]Li-ion35.8 kWh100% charge in 5.15 h
on 7.2 kW AC
80% charge in 45 min
on 50 kW DC
Mercedes–Benz EQC [37]Li-ion80 kWh100% charge in 8 h
on 11 kW AC
80% charge in 40 min
on 110 kW DC
Kia e-Soul [38]Li-ion Polymer64 kWh100% charge in 9.35 h
on 7.2 kW AC
80% charge in 54 min
on 100 kW DC
Jaguar I-Pace [39]Li-ion90 kWh100% charge in 12.7 h
on 7 kW AC
80% charge in 40 min
on 100 kW DC
Tesla Model S [40]Li-ion100 kWh100% charge in 9 h
on 10 kW AC
80% charge in 30 min
on 150 kW DC
Renault Zoe [41]Li-ion52 kWh100% charge in 9.25 h
on 7 kW AC
80% charge in 1 h
on 50 kW DC
Peugeot e-208 [42]Li-ion50 kWh100% charge in 7.5 h
on 7 kW AC
80% charge in 30 min
on 100 kW DC
Vauxhall Corsa-e [43]Li-ion50 kWh100% charge in 7.5 h
on 7.4 kW AC
80% charge in 30 min
on 100 kW DC
Table 2. EV charging station characteristics and charging power levels [20,22,30,55,63,64].
Table 2. EV charging station characteristics and charging power levels [20,22,30,55,63,64].
Types of EV ChargingDescriptionTypical UsageInterface for Energy SupplyPower Capacity (kW)Voltage
Level 1
charger (any
available outlet)
Home or office
base charging
Any convenient
Level 2
Private and
public base
Electric vehicle
Level 3
fast charger
charging stations
Electric vehicle
Extreme fast
Electric vehicle
Electric vehicle
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Umoren, I.A.; Shakir, M.Z. Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers. Energies 2022, 15, 7207.

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Umoren IA, Shakir MZ. Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers. Energies. 2022; 15(19):7207.

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Umoren, Ifiok Anthony, and Muhammad Zeeshan Shakir. 2022. "Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers" Energies 15, no. 19: 7207.

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