Electric Vehicles Battery Degradation Research Articles

Benefit to Harm (B2H) Ratio for Bulk Vehicle to Grid (V2G) Services Using Lifetime Degradation Digital Twin: Emulating Various Dominant Degradation Mechanism

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H). We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G.B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty.Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits.Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLICal). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking.We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAMNeg). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2].We show that, in cases with a higher contribution of calendar aging to degradation (LLICal/LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3].With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty.

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

Comprehensive techno-economic performance assessment of PV-building-EV integrated energy system concerning V2B impacts on both building energy consumers and EV owners

Deploying photovoltaics (PVs) for buildings is an effective solution to decrease carbon emissions but it also imposes a substantial risk of energy mismatch. Battery storage can mitigate this issue to a certain extent but its capacity is limited due to safety concerns and expensive investment. Energy boundary expansion from buildings to an integrated building-transportation is another valuable attempt. However, few studies have quantified the impacts of electric vehicles (EVs) on the PV-building-EV integrated system currently. This study, therefore, conducted a comprehensive techno-economic performance assessment of this integrated system. An energy-sharing mechanism considering EV charging-discharging and PV-building energy system integration is involved in the assessment. According to this energy-sharing mechanism, feasibility studies of this integrated system without stationary battery installation were conducted. An office building integrated with the EV parking area in Chengdu is selected in this study. Results show that the system's techno-economic performance as well as EV battery degradation were comprehensively quantified from two perspectives. From the perspective of the building consumers, renewable energy penetration is enhanced at 45.1% and the reduction of levelized cost of energy (LCOE) is about 38.9%. From the perspective of EV owners, the LCOE of EV charging can decrease by 18.1%. Extra annual EV battery degradation is quantified at 5.23% and an extra compensation of 10.45 CNY for one-time participation of EV owners is calculated. Reasonable compensation for EV owners can effectively increase their motivation to participate in the energy-sharing mechanism. The comprehensive techno-economic performance assessment can provide frontier guidelines for future work.

Commercial Electric Vehicle Battery Degradation modelling and charging assessing using a real driving cycle

This paper proposes a model to study the degradation of li-ion NMC batteries of commercial electric vehicles. The model takes into account operation variables such as operating C-Rate, Depth of Discharge (DoD), Number of Cycles and Temperature using a 4-D Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). Simulations have been done considering the Worldwide Harmonised Light Vehicles Test Procedure 3 (WLTP3) standard cycle. The model has been implemented in MATLAB. In addition, recommendations on charging procedures are given in order to reduce the degradation of batteries.

Electricity trading of multiple home microgrids through V2X based on game theory

Currently, a significant number of householders are contentedly participating in energy markets integrated with renewable energy sources (RES) to control global warming. Further, the high penetration of e-mobility influences the net zero concepts globally. However, uncoordinated scheduling of electric vehicles (EV) battery charging/ discharging parked in residential areas would result in critical issues in the power system in terms of energy availability. Thus, running intelligent charging/ discharging approaches can tackle these obstacles while managing green energy efficiently. This paper proposes a novel smart EV charging/ discharging strategy for multiple home microgrids (H-MG) based on user behavior analytics patterns with a non-cooperative game theory approach named Nikaido–Isoda/Relaxation algorithm (NIRA). In the proposed structure, EVs are active loads besides responsive loads in H-MGs and are involved in demand-side management (DSM) to achieve peak shaving. Furthermore, electricity retailers (ER) are encountered with distinct properties following opposite intentions and could participate in the competitive retail electricity market (REM) in residential areas. Therefore, households can maximize their profit by reducing varied costs, such as EV charging and battery degradation, while minimizing their carbon footprint. In addition, this study works on the distributed decision-making process that could provide optimal power-sharing and the best payoff in an unbiased procedure. After crucial investigations, the simulation outcomes demonstrate the effectiveness of the proposed approach in encouraging various households to participate actively in the REM with other agents to increase their profits. According to acquired results, active residents saved their budget 28%–45% through employing intelligent EV charging/ discharging strategies in the REM with dynamic pricing compared to the fixed tariff.

Towards Nearly Zero-Energy Buildings: Smart Energy Management of Vehicle-to-Building (V2B) Strategy and Renewable Energy Sources

As countries worldwide strive for net-zero emissions, electric vehicles (EVs) have gained significant momentum. EVs, particularly through Vehicle-to-Everything (V2X) technology, including Vehicle-to-Building (V2B), are recognized as key enablers for achieving nearly-zero energy buildings (NZEB). This research presents an advanced Smart Energy Management System (SEMS) with a novel V2B strategy, offering precise one-hour resolution to reduce grid reliance and optimize peak load management. The SEMS coordinates building energy and EV charging while harnessing the potential of renewable energy resources and battery storage systems. By carefully scheduling EV charging and discharging activities, it maximizes the efficient use of available energy resources. Importantly, the algorithm integrates real-world EV parking patterns, accounting for factors such as fleet composition, parking durations, and charging priorities. Economic evaluation encompasses not only direct EV battery degradation costs but also external expenses associated with environmental impacts. Simulation outcomes underscore a remarkable reduction in building grid power demand (65%) and a substantial decrease in carbon emissions (64%). The PV+V2B Scenario emerges as the preferred choice, striking an optimal balance between initial investment costs, energy savings, and carbon footprint reductions. This study holds great academic and practical significance, contributing to the advancement of smart, sustainable, and economically viable net-zero cities.

Predictive control and coordination for energy community flexibility with electric vehicles, heat pumps and thermal energy storage

Electrification of private transportation and residential heating is a potential action to decrease significantly carbon emissions. However, the lack of coordination of such emerging loads can increase stress on the power distribution grid. To this end, this paper proposes a two-stage energy management strategy, to enhance the flexibility of energy communities. First, an optimization algorithm-based model predictive control (MPC) is developed for home energy management systems (HEMS) to schedule electric vehicles (EVs) for charging and discharging in cooperation with heat pump and thermal energy storages. The objective is to reduce electricity bills and EV battery degradation costs while maintaining thermal demand. Second, the predictive coordination based on the nonlinear AC optimal power flow approach is applied to enhance the grid flexibility under a community energy management system scheme. To validate the performance of the proposed method, we develop a co-simulation framework by implementing the optimization algorithm and simulation scenarios in a Python Environment and modelling the electricity grid in the PowerFactory-DIgSILENT, which are linked together. Extensive numerical simulations are carried out under the active demand response program-based hourly electricity pricing and different weather data in Sweden. The results show that the proposed method can reduce operating costs and enhance the energy flexibility of the energy community. Moreover, it can decrease the stress on the community power grid and prevent load-shedding events compared to the non-predictive coordinated method.

Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems

With the increasing penetration level of electric vehicles (EVs) in the transportation network, it is necessary to control the charging and discharging process of EVs to avoid operational issues in the power grid. Therefore, in this paper, a two-stage framework is presented for EVs optimal routing and their active and reactive power control in the distribution grid. In the first stage, the EVs mobility in the transportation system is taken into consideration. On this basis, EV daily trips in the transportation network are modelled using the trip chain method. Also, the Dijkstra algorithm is utilized for optimal routing with the shortest travel time between the origin and the destination node. Furthermore, the effects of ambient temperature, traffic congestion and road type on the EVs energy consumption are assessed. In the second stage, optimal active and reactive power exchange of EVs with the distribution grid is carried out. Thus, a mixed-integer linear programming (MILP) model is proposed that simultaneously considers EV owners’ and the distribution network operator's benefits as the optimization goals. The EV battery degradation cost due to the discharging of active power is also integrated into the objective function of the optimization problem. The proposed framework is implemented on a standard IEEE 33-bus system coupled with a 30-node transportation network. The simulation results show that the presented method can reduce the loss cost of the distribution grid and benefit EV owners compared to different charging strategies. In addition, with the battery technology development, the proposed framework can significantly improve the distribution system operation, decrease environmental issues, and reduce EV owner’s costs.

Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030

The energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation. We include both in-use and end-of-vehicle-life use phases and find a technical capacity of 32–62 terawatt-hours by 2050. Low participation rates of 12%–43% are needed to provide short-term grid storage demand globally. Participation rates fall below 10% if half of EV batteries at end-of-vehicle-life are used as stationary storage. Short-term grid storage demand could be met as early as 2030 across most regions. Our estimates are generally conservative and offer a lower bound of future opportunities.

Analysis and Impacts of Grid Integrated Photo-Voltaic and Electric Vehicle on Power Quality Issues

Electric vehicles (EVs) and photovoltaic (PV) systems have been progressively incorporated into the grid in recent years principally due to two factors: reduced energy costs and lower pollutants. Numerous studies have investigated how integrating PV and EVs into the grid may affect specific people. It is crucial to understand that the electricity grid will experience the combined effects of PV–EV integration as PV and EV penetration increases. The primary motivation for PV’s integration with Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) services is the aim to reduce charging costs from discharging; moreover, another prerequisite must be satisfied before PV arrays will be able to provide V2G services. The range between the driving limit and EV battery degradation should be reasonable. The way EVs charge and discharge will be impacted by these factors. Numerous analyses are required in order to control the power between various source and load scenarios. In order to balance grids and manage frequency, controllers such as Improved Particle Swarm Optimization (IPSO), Improved Ant Colony Optimization (IACO), and Improved Mayfly Optimization (IMO) are used. As a result, V2G/G2V helps feed electricity back into the grid. By providing the proper duty cycle ratio, the proposed controller regulates converter switching. This study allowed for the performance analysis and operation simulation of a grid-connected PV/EV/Grid system. The purpose of this system was to maximize PV self-consumption while maintaining power quality characteristics like harmonics, grid voltage/current, and power factor.

VCG-Based Auction for Incentivized Energy Trading in Electric Vehicle Enabled Microgrids

Under vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed to meet additional energy demand of critical load (CL) in a microgrid. In this article, an incentivized energy trading approach is introduced to study the interaction between EVs and CL. EV mobility and battery degradation are studied to ensure they do not deter EV participation. Bidder satisfaction is introduced which allows EV owners to enforce their energy trading conditions. EV-CL association and discharging scheduling are considered in a two-phase model. In the first phase, EV-CL association is modeled as a single auction to determine the winning bids and corresponding payments. Successful bidders are determined by solving a mixed integer non-linear programming (MINLP) problem, while Vickery-Clarke-Groves (VCG) payment rule is applied to pay the auction winners. In the second phase, EV discharging scheduling determines the operating cost and discharging power of associated EVs at each time slot. Simulation results show that the proposed approach achieves comparable performance with reference schemes and guarantees bidder satisfaction. Theoretical analysis on economic properties of truthfulness and individual rationality are verified as well.

Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems

To fully exploit the potential of decarburization in the transport sector (e.g., electric vehicles (EV)) and energy sector (e.g., building energy system), this paper proposes a new concept of ‘Vehicle-to-Micro-Grid (V2μG) network’ that incorporates the off-grid building energy system with flexible power storage/supply provided by battery EVs (BEVs) and fuel cell EVs (FCEVs). The work is conducted with three main contributions: 1) a rule-based energy management strategy is proposed to study the impact of the V2μG interactions on the EV battery degradation; 2) a scenario analysis based on four working modes is conducted to evaluate the energy efficiency, costing, environmental impacts, and component ageing of the proposed V2μG network; 3) the optimum settings for system configuration, capacity, and operation strategy of the V2μG network are obtained by the NSGA-II algorithm. The study suggested that the degradation of lithium-ion batteries in BEV can be reduced by 13% compared to the network without FCEVs. In a community with 160 households and 200 EVs, the optimal V2μG network can reduce carbon dioxide emissions by 515.56 tons annually compared to the conventional off-grid building energy system powered by internal combustion engines.

Hierarchical Economic Model Predictive Control Approach for a Building Energy Management System With Scenario-Driven EV Charging

To handle the increasing number of electric vehicles (EVs) and their effect on the infrastructure, intelligent and coordinated charge management is necessary. In this paper, this problem is considered in the context of a commercial building energy management system (EMS) with vehicle-to-grid capable employee EV charging stations (EVCSs). We propose a hierarchical economic model predictive control (EMPC) scheme for the operation of the EMS with EV charge management. An aggregator plans the operation of the EMS and an aggregated perspective of the EVCSs. A distributor then allots the aggregated charge power to the individual EVCSs. Both layers employ EMPC to jointly consider the objectives of monetary costs, building temperature comfort, EV charge satisfaction and battery degradation. For the predictions of EV usage behavior, scenario generation based on usage data and user input is employed. The main contributions of this paper are the symmetric consideration of EV charging objectives using EMPC in both levels of the hierarchy, as well as the introduction of a scalable and adaptable averaged scenario generation approach.

Unifying Criteria for Calculating the Levelized Cost of Driving in Electro-Mobility Applications

The Levelized Cost of Driving (LCOD) calculates how much it costs to drive a vehicle, per kilometer, over the vehicle lifespan, and it is typically measured in USD/km. LCOD has been widely applied to assess and compare the performance of Electric Vehicles (EVs) and Internal Combustion engine Vehicles (ICVs). Unfortunately, there is no common methodology to establish how to compute LCOD. As authors use different criteria to determine which elements are relevant in LCOD, it is difficult to compare results. To overcome this issue, we re-introduce the LCOD concept, where the most relevant expenditures and incomes are included. This novel LCOD definition also incorporates key elements that are sometimes neglected, such as the second life of batteries in EVs, EV battery degradation models, and stochastic models, to characterize daily operation. The proposed methodology is applied to assess the cost of using EVs as taxis in the city of Quito, Ecuador.

A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios

Integrating fleets of electric vehicles (EVs) into industrial applications with smart grids is an emerging field of important research. It is necessary to get a comprehensive overview of current approaches and proposed solutions regarding EVs with vehicle-to-grid and smart charging. In this paper, various approaches to battery modeling and demand response (DR) of EV charging in different decentralized optimization scenarios are reviewed. Modeling parameters of EVs and battery degradation models are summarized and discussed. Finally, optimization approaches to simulate and optimize demand response, taking into account battery degradation, are investigated to examine the feasibility of adapting the charging process, which may bring economic and environmental benefits and help to alleviate the increasing demand for flexibility. There is a lack of studies that comprehensively consider battery degradation for EV fleets in DR charging scenarios where corresponding financial compensation for the EV owners is considered. Therefore, models are required for estimating the level of battery degradation endured when EVs are utilized for DR. The level of degradation should be offset by providing the EV owner with subsidized or free electricity provided by the company which is partaking in the DR. This trade-off should be optimized in such a manner that the company makes cost savings while the EV owners are compensated to a level that is at least commensurate with the level of battery degradation. Additionally, there is a lack of studies that have examined DR in smart grids considering larger EV fleets and battery degradation in multi-criteria approaches to provide economic and environmental benefits.

Real-time joint regulating reserve deployment of electric vehicles and coal-fired generators considering EV battery degradation using scalable approximate dynamic programming

This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs’ fast regulation speed and CGs’ large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wise linear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the real-time RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm.

Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk

With the global net-zero strategy implementation, decarbonisation of transport by massive deployment of electric vehicles (EVs) has been considered to be an essential solution. However, charging EVs and integration into electricity grids is going to be a fundamental challenge to future electricity systems. Hence, in this situation, how to effectively deploy massive numbers of EVs, and in the meantime what can be developed to deliver vehicle-to-grid (V2G) services, become a fundamental yet interesting tech-economical issues. Furthermore, uncertainty in lack of vehicle availability and EV battery degradation could lead to revenue loss when using EVs as ancillary services aggregators. With such considerations, this paper presents a new optimised V2G aggregator scheduling service that has taken into consideration of a number of risks, including EV availability and battery degradation through conditional value-at-risk. The proposed method for V2G scheduling service, as an independent aggregator, is formulated as a bi-level optimisation problem. The performance of the proposed method is to be evaluated through case studies on the Birmingham International Airport parking lot with onsite renewable generation. Uncertainties of EVs and the differences in weekdays and weekends are also compared.

Integration of Electric Vehicles in Home Energy Management Considering Urgent Charging and Battery Degradation

With the absence of renewable energy sources (RES) and energy storage systems (ESS), home energy management systems (HEMS) suffer more difficulties to schedule the household demand without affecting the homeowner's lifestyle or exceeding distribution transformer maximum loading. The problem is more complicated when considering other practical situations like multi-trips and urgent charging activities of electric vehicles (EV) during the peak periods. In this paper, a HEMS strategy is proposed to coordinate the operation of the household load demand, including charging/discharging activities of EVs batteries in homes that are not integrated with RES nor ESS. The proposed strategy is intended to reduce the daily energy cost, peak-to-average ratio (PAR), and alleviate stresses on the distribution transformer while maintaining the homeowner's convenience. Unlike most previous studies, the proposed strategy considers EV multi-trips and battery degradation associated with home discharging activities. The proposed strategy coordinates the operation of various household appliances while the charging algorithm tackles the problem of urgent charging related to multi-trips requirements. Furthermore, by using battery degradation cost and energy tariff, the proposed strategy investigates the economic feasibility of home discharging activities of EVs. The strategy is applied to a residential neighborhood with three houses with various numbers of residents and various load profiles. The results proved the proposed strategy's effectiveness in reducing the energy cost and PAR while maintaining the transformer loading limit even if there are charging activities during peak or high tariff periods.

Combined economic emission based resource allocation for electric vehicle enabled microgrids

As electric vehicles (EVs) are currently under-utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi-objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi-objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade-off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.

Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits

With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.