Hybrid Electric Vehicle Charging Stations Research Articles

An Enhanced MPPT Approach Based on CUSA for Grid-Integrated Hybrid Electric Vehicle Charging Station

Because of the fluctuating demands for electricity and the growing awareness of the need to protect the environment from global warming and the depletion of nonrenewable natural resources, battery-powered electric vehicles, or EVs, are being used in the transportation sector as an alternative to internal combustion engine vehicles. However, charging these EVs with conventional fossil fuels is neither economically sustainable nor structurally viable. Therefore, this manuscript proposes a renewable energy-powered EV charging station featuring a combination of solar energy, standby battery systems, sophisticated control techniques such as neural network-integrated grids, the enhanced Cuckoo Search Algorithm for Maximum Power Point Tracking, and the Proportional-Integral-Derivative controller. This idea beats current methods and presents a viable way to drive the EV revolution while lessening environmental effects. It maximizes energy management and guarantees a steady power supply even in erratic weather. Grid integration ensures the consistency of power supplies at charging terminals. When compared to other algorithms that have been investigated in the literature, the designed algorithm exhibits excellent performance. Grid integration, in addition to the standby battery, is essential in ensuring that the charging station has a constant power supply, even during unpredictable weather.

Revolutionizing Green Transport: An Extensive Review of Hybrid Electric Vehicle Charging Stations and Electric Microgrid Integration

Electric vehicles (EVs), recognized as a strategic approach to reducing oil consumption and greenhouse gas emissions, rely on electricity instead of traditional fuels like petrol or diesel for battery charging, positioning them as a significant player in future energy landscapes. The anticipated decline in oil demand aligns with the increasing prevalence of EVs, making attention to charging infrastructure crucial. This paper extensively explores charging infrastructure considerations, emphasizing their significance in both urban and rural contexts, especially in regions with unstable or absent power supplies. Examining off-grid, grid-connected, and hybrid charging modes, the research delves into various EV designs, including those utilizing fuel cells or batteries.
 A thorough understanding of energy-source-based charging techniques and diverse power-level charging stations is presented, catering to readers' interests. With a focus on enhancing the longevity and efficiency of electric vehicles, researchers are investigating innovative charging methods, including microgrid concepts within charging stations. Recognizing electric vehicles as multi-energy systems, the paper underscores the importance of effective power management and control for optimal energy utilization.
 Additionally, the review scrutinizes the impact of electric vehicles on utility grid infrastructure & maintenance, evaluating various power management and control systems. This comprehensive review serves as a valuable resource for electric vehicle operators and research engineers, offering insights into the current state of the field and potential avenues for future exploration.

Design of Solar and Battery Hybrid Electric Vehicle Charging Station

Microgrids have emerged as a new way to integrate and use large-scale distributed power to provide electricity to isolated locations, such as islands and villages. A utility engineering microgrid structure is designed to provide fast response and efficient operation by analyzing hybrid power systems and fuzzy-PI-based control techniques. This project aims to design a solar photovoltaic (PV)-battery-diesel generator-based hybrid power system employing a Sepic converter. The Perturb and Observe algorithm is used for maximum power extraction from solar panel. A lithium-ion battery is utilized as a storage battery in this system, which may be charged by the PV source. When the storage battery is depleted and PV generation is unavailable, the charging station uses the grid and a diesel generator. The generated voltage is synchronised at the Point of Common Coupling (PCC) to achieve continuous charging. The simulation is done in MATLAB/Simulink. The simulation output of using the PI control strategy is compared with the fuzzy-PI control strategy. This demonstrates the efficacy of the fuzzy-PI control method.

Simulation of hybrid electrical vehicle charging station in multimode operation

The use of electric vehicles is growing very rapidly, and more and more people are switching to electric vehicles as the problem of air pollution has become a concern in most parts of the world. In addition, the spike in fuel prices around the world has prompted people to opt for an alternative vehicle option other than conventional Internal Combustion Engine (ICE) vehicles. But as the number of electric vehicles increases, the need to install charging stations on a very large scale also increases. The main problem with conventional grid-based charging stations is that they increase the load on the main grid and affect the power quality of the local distribution network. This paper discusses the modeling and simulation of a hybrid electric vehicle charging station operating in multi-mode (i.e., grid-connected or Grid Power Connected Mode (GPCM) and solar-powered or Solar Power Connected Mode (SPCM). The Electric Vehicle (EV) charge controller automatically switches between GPCM and SPCM. With this hybrid electric vehicle charging station model, renewable energy can be integrated with the main grid to improve the efficiency and reliability of the electric vehicle charging station, so the Electric Vehicle Charging Station (EVCS) hybrid model can help to expand the electric vehicle charging network on a large scale. The simulation was performed using MATLAB 2016.

An Optimized PV based Plug-in Hybrid Electric Vehicle Charging Station Using Fuzzy-PI Controller Logic

An Optimized PV based Plug-in Hybrid Electric Vehicle Charging Station Using Fuzzy-PI Controller Logic

Renewable Energy Tracking and Optimization in a Hybrid Electric Vehicle Charging Station

The increasing electric vehicle fleet requires an upgrade and expansion of the available charging infrastructure. The uncontrolled charging cycles greatly affect the electric grid, and for this reason, renewable energy sources and battery storage are getting incorporated into a hybrid charging station solution. Adding a renewable source and a battery to the charging station can help to “buffer” the power required from the grid, thus avoiding peaks and related grid constraints. To date, the origin of the energy coming from the battery has not been traced. In this paper, a solution of the hybrid electric vehicle charging station coupled with the small-scale photovoltaic system and battery energy storage is proposed to eliminate the adverse effects of uncontrolled electric vehicle charging, with the exact calculation of renewable energy share coming from energy stored in the battery. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and electric vehicle charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy (from both the solar plant and the battery), and the minimization of battery degradation. The problem is solved using a genetic algorithm optimization procedure adapted to the multicriteria optimization function. The methodology is tested on an illustrative example, and it is proven that the decision-maker’s preferences greatly affects the choice of the optimal strategy and the optimal battery capacity.

Sizing of a solar-wind hybrid electric vehicle charging station by using HOMER software

Although, fossil fuels are still the main source that used to balance the global demand/supply dynamics of energy; most countries have embarked on a quest to reach a solution and started changing their direction from fossil resources (especially oil and gas) to other resources by creating new opportunities with a focus meeting the increasing energy demand by reducing it via energy efficiency. “Transportation” is one of the biggest items of global energy consumption with 24% usage percentage and most of this amount is supplied by conventional energy resources. However, worldwide supporting policies and regulations are positively affected the starting period of environment-friendly use of energy resources. It expedited the process of new technological developments that minimize waste, lower the level of air pollution caused by fossil fuel-powered internal combustion engines, conserve forests and decrease the emissions of greenhouse gases. Worldwide extension of “Electric Vehicle Development and Production” is one of the main concern of these regulations. However, increasing the number of electric vehicles also brings sustainability problems such as; supplying the electricity from renewable resources in an efficient and sustainable way, handling the electricity load on the grid, and establishment of new charging stations. Therefore, in this study a wind solar hybrid energy charging station designed and optimized via HOMER software. The sizing methodology is suitable to apply anywhere around the worldwide. The optimal solution for the hybrid system consists of 44.4% wind energy and 55.6% solar energy and the annual electricity production is 843150 kWh with the 0.064 $/kWh production cost.

Novel standalone plug-in hybrid electric vehicle charging station fed by solar energy in presence of a fuel cell system used as supporting power source

A novel standalone charging station (CS) fed by solar energy and a supporting fuel cell (FC) stack has been built to charge electric vehicles (EVs). In addition to this, a novel maximum power point tracking (MPPT) technique has been utilized to maximally extract electric power from photovoltaic (PV) array used in the constructed CS. Battery bank is the important unit of a conventional standalone CS that absorbs/produces extra electric power available/required in the CS by charging/discharging itself. This is the main shortcoming of conventional standalone CSs because battery bank is an expensive device that provides a lifetime shorter than normal when it is utilized in a CS. This is because of the huge number of charge/discharge cycle that should be conducted by it. In this work, to overcome this issue, the battery bank has been replaced with a proposed FC system comprising a FC stack, an electrolyzer, two hydrogen tanks and a FC controller. At any time when charge demand is greater than PV output power, the rest of demand is supplied by the FC system. Vice versa, the electrolyzer absorbs extra PV output power and produces hydrogen by extracting it from water when PV output power is greater than charge demand. Experimental results and data recorded under normal operation of the built CS are given. Compared to conventional standalone CSs, the contributions of the built CS are as follows. First, the proposed FC system with permanent lifetime and a price of $39800 has been used as substitute for battery banks with limited lifetime and being significantly more expensive ($140000 over 30 years). Second, a novel MPPT technique with adaptive step-size has been used in the CS to extract maximum PV power. Third, the CS built in this research work not only is standalone but also is only fed by solar energy, so it can be utilized in absence of electric grids and negative effects such as air pollution.

Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations

There is a knowledge gap to introduce a comprehensive simultaneous optimal allocation of protective devices, distributed generation units, and electric vehicle/plug-in hybrid electric vehicle charging stations. This paper tries to fill such a knowledge gap by proposing a method that simultaneously considers all discussed optimization variables. In the proposed hierarchical optimization method, three stages and seven scenarios are introduced. Because of the complexity increment of optimization problem as well as the number of the optimization variables, it is useful to have the criteria for the results cross-checking. In the proposed hierarchical optimization method, the third stage optimization results which consist of all three types of optimization variables are examined with those of first and second stages.To illustrate the advantages of the proposed method, it applies to the IEEE 33-bus and IEEE 69-bus test systems in the widespread presence of non-renewable and renewable distributed generation units, e.g. wind turbine and photovoltaic power plants, and electric vehicles. The hierarchical optimization method comparison results of different stages determine the importance of any variable type on the system reliability. By getting insight into how any variable affects the system reliability, it is possible to investigate the most effective corrective and improving solutions.

Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach

As Plug-in Hybrid Electric Vehicles are environment friendly, it is envisioned as an alternative solution for the existing transportation system. However, simultaneous charging of electric vehicles may stimulate increase in peak load, causing voltage deviation and power loss issues. Different from other approaches, this paper presents an alternative way to tackle these issues. A novel two area distribution system, each following a different mobility pattern is presented. This model considers various sites namely optimal, midst, and unfit site for locating the charging stations in both the areas simultaneously. Moreover, the scheduling of the plug-in Hybrid Electric Vehicle is addressed using a meta-heuristic solving tool as it involves simultaneous scheduling of vehicles in both areas. To illustrate the cost reduction obtained with this model, the performance is evaluated in a standard test system namely IEEE 69-bus radial distribution system. The test results support the efficiency of the proposed two area framework and the new solving tool. The economic and the stability aspect of the system are addressed under various scenarios and several important findings are revealed.

Smart management of distinct plug-in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Summary Management of plug-in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2-area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta-heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.

Optimal planning of plug‐in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties

This study presents a mathematical model to determine the optimal site and the size of plug-in hybrid electric vehicles charging stations (CSs) in the distribution networks. The presented optimal planning is done along with considering the rate of customers' participation in demand response programs (DRPs) at the presence of some uncertainties associated with the load values and electricity market price. In order to model the load, time-of-use DRP is used. The proposed model involves distribution system manager (DSM) benefit maximising derived from the appropriate use of CS for charging and discharging vehicle batteries, reliability improvement and supplying network's load demand at peak times as the objective function, technical limitation as constraint and the siting and sizing of electric vehicles CS as optimisation variables. The genetic algorithm method with embedded Monte Carlo simulation is used in order to solve the optimisation problem and is applied to the 9-bus and 33-bus networks. The test results indicate that not only does the appropriate planning have an economic benefit for DSM, but also voltage profile and power supply reliability for customers can be improved and active power losses can also be reduced.

Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system.

Parzen Window Density Estimator-Based Probabilistic Power Flow With Correlated Uncertainties

This paper presents a numerical-based algorithm to solve the probabilistic power flow problem. Parzen window density estimator is used to efficiently estimate probabilistic characteristics of power flow outputs. Correlations between wind generation, load, and plug-in hybrid electric vehicle charging stations are taken into account. The proposed algorithm works properly for random variables with various probability distribution functions and is very useful when limited information is available for each random variable. The algorithm is tested on the IEEE 14-bus and IEEE 118-bus systems considering correlated and uncorrelated conditions. Comparison between the proposed algorithm with 2n, $\text{2n} + 1$ point estimation methods as well as Monte Carlo simulation and linear diffusion method are provided. In addition, probability density and cumulative distribution functions are determined using the proposed algorithm, diffusion method, and the combined Cumulants and Gram-Charlier for $\text{2n} + 1$ point estimation method. Error indices are introduced to evaluate all random variables in a single benchmark. Simulation results show the effectiveness of the proposed algorithm to provide complete statistical information for probabilistic power flow outputs.

Application of data envelopment analysis theorem in plug‐in hybrid electric vehicle charging station planning

In the near future, millions of electric vehicles (EVs), especially plug-in hybrid EVs (PHEVs), will be integrated into the electrical system. Such an increase in the demand for energy will require optimised management of distribution networks. This study presents a new formulation to determine the optimal size and location of PHEV charging stations to maximise the distribution system manager (DSM) benefit. A two-stage procedure has been considered to optimise PHEV charging station planning. In the first stage, the candidate locations and their size are determined based on: (i) the sum of benefits from the appropriate use of charging station for charging and discharging of vehicles and (ii) the benefit of providing spinning reserve and supplying the network's load demand at peak times by DSM. In the next stage, an improved framework is proposed to classify charging stations into efficient and inefficient sets characterised by non-financial indices. The proposed method is data envelopment analysis efficiency and three indices, including active power loss, voltage profile improvement and CO2 emission. The suggested model is successfully applied to a test system with nine buses. Simulation results show that appropriate planning results in economic benefits as well as improvements in active power losses and the voltage profile.