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... FCS under study (Fig. 1) consists of three 120 kW Fast Charging Units (FCU) and a BESS (Li-ion battery). The EVs can be charged by the grid or by the stationary BESS. In our study, FCS will be characterised by the contracted power and the capacity of the SB. The contracted power represents the maximum power that the FCS could receive from the power grid ...
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... Te fastest charging time is ofered by level 3, which is used economically and takes less than an hour [4]. In order to charge EVs in less than 30 minutes, FCS needs high power from the grid [28]. It may be deployed at rest areas along highways and at urban refueling stations and is identical to fuel stations. ...
Electric vehicles (EVs) have various advantages over traditional internal combustion engines (ICEs), including reduced carbon emissions, greater energy efficiency, and a lessened reliance on petroleum supplies. The use of EV charging infrastructure and power levels are reviewed in this article. Battery performance is affected by the design of the battery as well as the charger parameters and infrastructure. In this paper, the off-board and on-board charging methods with bidirectional and unidirectional power flow are compared. Hardware restrictions and connectivity concerns are eased with a unidirectional charger. The bidirectional charger enables both battery energy injection back into the grid and the vehicle. Power is constrained by the onboard charger due to its size, weight, and price. Both conductive and inductive onboard chargers are viable. For high current rates, which are not supported by EVs, it is feasible to develop an off-board charger. The time required for charging, amount of power, cost, equipment, location, infrastructure configurations, and other parameters are provided, compared, and reviewed for different power level chargers, such as level-1 (slow), level-2 (semi-fast), and level-3 (fast).
... In [17], the aging of the lead-acid batteries that compose the storage was taken into account to avoid overloads and overdischarges in a stand-alone configuration for the battery charger, although no issues related to the profitability of the investment were investigated. In [18], a hypothetical network of fast charging stations on highways is considered for a simulated charging request. The charging station consists of three 120 kW chargers and a BESS of two possible sizes: 250 and 650 kW. ...
The increasingly widespread use of electric vehicles requires proper planning of the charging infrastructure. In addition to the correct identification of the optimal positions, this concerns the accurate sizing of the charging station with respect to energy needs and the management of power flows. In particular, if we consider the presence of a renewable energy source and a storage system, we can identify strategies to maximize the use of renewable energy, minimizing the purchase costs from the grid. This study uses real charging data for some public stations, which include “normal” chargers (3 kW and 7 kW) and “quick” ones (43 kW and 55 kW), for the optimal sizing of a photovoltaic system with stationary storage. Battery degradation due to use is included in the evaluation of the overall running costs of the station. In this study, two different cost models for battery degradation and their influence on energy flow management are compared, along with their impact on battery life.
... The chargers have evolved from slow level-1 to fast level-2 ac chargers and now to the faster level-3 dc-dc chargers that deliver about 50-200 kW of power and can, thus, completely charge an EV in around 30 min [27]. Still, the time required to charge an EV is considerably more than refueling conventional gasoline-based vehicles. ...
This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging.
... Calendar life loss is defined by the loss of health in battery after storing energy for typically 6-10 months [27]. A semi empirical formula for total loss [28] is given below. ...
... The studies have assessed the integration of bidirectional DCFC with ESS (Gjelaj et al., 2017a) and use of ESS to ameliorate the impacts on the electric grid (Gjelaj et al., 2017b). The ESS degradation, trade-offs between the power rating of DCFC station, and size of the ESS have been studied recently (Richard and Petit, 2018b). Another study compared second life batteries (SLB) with new batteries (NB) of lithium-ion (Li-ion) to support EV fast-charging demand and reduce the electric grid load (Kamath et al., 2020). ...
This report provides a framework to develop policies and infrastructure for supporting plug-in electric vehicles (EV) charging demand and grid integration through distributed energy resources (DER). The developed comprehensive approach is funded and supported by the Michigan Department of Environment, Great Lakes, and Energy (EGLE). Researchers at Michigan State University lead the modeling framework development and execution. The EV charging demand is predicted to increase the load on the electric grid. Hence, a modeling framework is required to predict the optimum investment technology supporting EV fast-charging demand and reducing the load on the grid. This study estimates the optimum size, type, and location of the DER to support the direct current fast charging (DCFC) demand in 2030. The study captures the existing load on the grid, and the capacity constraints of the grid network, while predicting the optimum investment technology. The potential load from DCFC is derived from the previous study on DCFC station locations for supporting urban trips across Michigan for the year 2030, conducted by the same research team at Michigan State University and supported by EGLE.
... Due to the fact that batteries have limited number of charge and discharge cycles, batteries degrade accumulatively during the operations, which should be accounted for in the operational planning problems. The battery degradation is analyzed based on the operation of the system and considered in cost analysis in [8], however, the battery degradation is not directly accounted for in the dispatch scheduling strategies. ...
This paper investigates the usage of battery storage systems in a fast charging station (FCS) for participation in energy markets and charging electrical vehicles (EVs) simultaneously. In particular, we focus on optimizing the scheduling strategies to reduce the overall operational cost of the system over its lifetime by combining the model of battery degradation and energy arbitrage. We implement the battery degradation as a penalty term within an energy arbitrage model and show that the battery degradation plays an important role in the optimal energy dispatch scheduling of the FCS system. In this case study, with different penalty coefficients for the battery degradation penalty term, it is found that including the penalty of battery usage in the scheduling model will reduce the number of small charging/discharging cycles, thereby prolonging the battery lifetime, while maintaining near optimal revenue from grid services.
... The Tesla Supercharger will only work for the Tesla Model S, providing half a charge in approximately 20 minutes [10]. Indian Car Manufactures follow Bharat EV Charger AC-001 and Bharat DC-001 specifications in comply with the China based GB/T connector standard [11]. ...
Sustainable transportation is a model of the personal transportation that meets the mobility needs of the society with less carbon footprint to the environment. There is great scope in developing new models to support sustainable transportation. Electric Vehicles (EVs) is the new trend and one of the best possible solutions with advantages like clean energy, zero pollution, no noise, saving energy and preserving fossil reserves for future generations. In Electric Vehicles (EVs), the main source of energy is batteries, but few vehicles with hybrid concept uses other alternate sources along with batteries. According to International Electrotechnical Commission (IEC), Electric Vehicles are classified as Battery Electric Vehicle (BEV), Plug-in-Hybrid Vehicle (PHEV) and Hybrid Electric Vehicle (HEV). Main Source of energy used in these types of vehicle is Batteries and it is very important to know the charging mechanisms necessary to recharge the batteries. Society of Automotive Engineers (SAE) and IEC has defined different charging levels and modes. This paper discusses more in detail the techniques used to meet the required specifications.
... Ahmadian et al. [21] presented a stochastic method for EVs' charging with considering the associated uncertainties and proposed a comprehensive model to study the impact of EVs' charging and discharging strategies on the battery degradation. Richard et al. [22] proposed a fast charging station model including grid services and studied the battery degradation cost under different conditions. Tan et al. [23] studied a charging scheduling problem of charging station batteries to minimize electricity cost with the constraint of fully charged EV batteries. ...
Autonomous electric vehicles (AEVs) will become an inevitable trend in the future transportation network and have an important impact on the power grid. It is difficult to find the optimal distributed charging solution for AEVs to minimize the system cost with some uncertainties. In this paper, we investigate an AEVs charging and discharging problem with vehicle-to-grid (V2G) services. We aim to minimize the total electricity cost and battery degradation cost of AEVs and charging station batteries with V2G services, which takes the random arrival and departure of AEVs into account. We first propose a distributed charging framework of AEVs and charging stations by clustering method with the constraint of limited AEVs for each charging station in a region and formulate a distributed offline optimization problem. Then we formulate a distributed online charging optimization problem and propose a distributed online AEV charging scheduling (DOAS) algorithm to get an optimal charging solution. To study a more practical case, we reformulate the distributed online optimization problem with the uncertainties from base loads, renewable energy and charging demands. Furthermore, to improve the time efficiency of DOAS algorithm, we reduce the dimension of the distributed problem and design a dimension-reduction DOAS (DDOAS) algorithm. To seek a robust solution with some uncertainties, we propose a DDOAS algorithm with DRO based on Wasserstein distance (DDODW). Simulation results show that DOAS and DDOAS algorithms can have a close-to-optimal charging cost and a significantly less battery degradation cost of charging stations, compared with centralized online charging scheduling algorithm and DDOAS algorithm is more time-efficient than DOAS algorithm. The proposed DDODW algorithm can provide a robust solution for the energy schedule
... Energy storage systems (ESSs), on the other hand, contribute to increased flexibility in the power system [11,24], while allowing uncontrolled EV charging without interventions in daily life. Besides developing suitable operation-and control strategies [22,[25][26][27][28][29][30][31][32], the integration of ESSs Abbreviations: EV, Electric vehicle; EB, Electric bus; HFC, Highway fast-charging; ELDT, Electric last-mile delivery truck; CP, Charging point; ESS, Energy storage system; FESS, Flywheel energy storage system; BESS, Battery energy storage system; GRF, Grid relief factor. into future planning processes certainly requires detailed knowledge about required ESS specifications regarding various high-power charging EV use cases. ...
... Besides the considered EV use case and the applied design criteria, state-of-the-art research deviates as well in terms of the analyzed ESS technology. While some studies consider ESS in general [33,36,39] or FESSs [22,29,34,40], the majority of recent studies focuses on BESS in general [23,35,38] or lithium-based BESS [11,12,[26][27][28]31,37] in particular. When it comes to the short-term supply of high-power EV charging, FESSs certainly show several advantages compared to BESSs: High life cycle numbers [29,34,[40][41][42], high power density [29,34,41,42], short access time [34,41], low maintenance effort [34,41], small environmental impact [34,[40][41][42] as well as the independency of power and energy content [40,42]. ...
The trend towards increasing the charging power of future e-mobility will challenge existing distribution power systems and raise grid utilization- and connection costs. Flywheel energy storage systems (FESSs) may reduce future power grid charges by providing peak shaving services, though, are characterized by significant standby energy losses. On this account, this study evaluates the economic- and technical suitability of FESSs for supplying three high-power charging electric vehicle use cases. Therefore, we initially investigate the impact of individual charging patterns on the required FESS capacity, the annualized costs, and the FESS efficiency. Based on these correlations, the economic and technical optima of FESS applications are identified for each use case: The supply of electric buses enables a cost-efficient operation at the technical optima of FESSs. In contrast, the economic suitability of FESSs considering electric last-mile delivery trucks or highway fast-charging is restricted to low recharging energy demands and high charging power of electric vehicles. Furthermore, a cost-efficient operation of FESSs at the technical optima requires either a reduction of flywheel costs or an increase of power-based grid utilization charges in the upcoming years.
... Ref. [7] proposed a coordinated control of photovoltaic (PV) and BESS integrated in an FCS to avoid transformer overloading. Ref. [8] described a case study of a FCS with BESS to reduce the grid connection fees and the contracted power of the FCS. The BESS operation was simulated with a control strategy including degradation for peak shaving and providing frequency control. ...
The electrification of the energy sector challenges the conventional methods to meet the increased load demand. The rapid increase of electric vehicles and the desire for shorter charging time at fast charging stations (FCS) contributes to higher power peaks in local distribution grids. This may lead to capacity issues, where a battery storage can be considered as an alternative to reinforcing the grid. This paper proposes a novel optimisation model including degradation that minimises operational costs for an actual FCS in Norway with a battery system. A case study is performed, where installing a battery system is compared to traditional grid reinforcement. The result of the case study shows that the total cost was 0.9 million NOK 1 higher for installing a BESS than reinforcing the grid, which corresponds to 44 % of the battery investment cost. Sensitivity analyses are done on time step, grid tariffs and degradation. The sensitivity analysis on degradation shows that calendar aging dominates battery degradation.
