Different Types Of Electric Vehicles Research Articles

Techno‐economic assessment of distribution system considering different types of electric vehicles and distributed generators

AbstractThe global warming crisis, together with environmental concerns, has already led governments to replace traditional fossil‐fuel cars with low‐emission Electric Vehicles (EVs). This replacement has resulted in the addition of a large number of EVs capable of connecting to the grid. A coordinated charging method is needed to promote EVs on a large scale while preventing grid overloading. In this work, EVs are categorized based on their battery capacity, as cars, trucks, and buses. A charging/discharging mechanism based on Real Time Pricing for each hour is developed for a 20‐min block that has been formed. The locations of EV charging stations have been identified using Voltage Stability Index.In the present work, the power loss when Distributed Generator (DG) units are deployed after the EVs are located in the system for charging and discharging is analyzed. The DGs are considered as solar‐based or wind‐based generators for studying the economic benefit of such an investment under two different weather scenarios and topologies on standard test systems. The results thus obtained give an insight into the various technical and economic benefits.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

E-mobility Concept implementation in the function of sustainable Tourism development managing

Planning as sustainable mobility function requires new approaches and new planning concepts that encourage the transition to cleaner and more sustainable transport modes such as walking, cycling, public transport, new patterns of car use and ownership, and the adoption and use of new technologies as indispensable support for sustainable mobility development. This approach aims to satisfy the mobility needs with a healthier environment and a better quality of life, since healthy and ecologically clean environments are becoming more attractive, both for the local population and for tourists, which can be especially important for those local communities with special tourist potential and developed tourist offer. Given the fact that electric mobility contributes significantly to the climate change mitigation through the reduction of pollutant emissions using different types of electric vehicles, it is necessary to consider the possibility of using e-mobility in order to strengthen the offer of tourist destinations while simultaneously protecting these areas from excessive noise, pollution, usurpation and deterioration of the quality and appearance of tourist areas. Considering that the city of East Sarajevo, as one of the tourist attractive local communities in terms of the diversity of the tourist offer with a scattered concentration of tourist destinations on predominantly hilly terrain, this paper will present the concept of introducing a bike-sharing system, an electric bus and an eco-taxi vehicle in the function of tourists transportation system with supporting infrastructure development plan, all based on tourist demand data analysis.

A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid

With the growing popularity of electric vehicles (EVs), it is a new challenge for the residential microgrid system to conduct charging scheduling to meet the charging demands of EVs while maximizing its profit. In this work, a safe reinforcement learning (RL)-based charging scheduling strategy is proposed to meet this challenge. We construct a complete microgrid system equipped with a large charging station and consider different types of EVs, as well as the vehicle-to-grid (V2G) mode and nonlinear charging characteristics of EVs. Subsequently, the charging scheduling problem is formulated as a constrained Markov decision process (CMDP) due to the various limitations of power and demands. To effectively capture the uncertainties of the supply side and demand side of the microgrid, a model-free RL framework is employed. However, the curse of dimensionality of the action space is inevitable as EVs increase. To solve this problem, a charging and discharging strategy based on a general ladder electricity pricing scheme is designed. Different EVs are divided into different sets according to their states under this strategy, and the agent gives control signals of different sets instead of controlling each EV individually, which effectively reduces the dimension of the action space. Subsequently, a constrained soft actor-critic (CSAC) algorithm is designed to solve the established CMDP, and a safety filter is introduced to ensure safety. In the end, a numerical case is conducted to verify the effectiveness of the proposed method.

Low-Carbon Charging Facilities Planning for Electric Vehicles Based on a Novel Travel Route Choice Model

Promotion of charging facilities (CFs) can ameliorate range anxiety and facilitate long-distance travel by electric vehicles (EVs). In this context, a multistage low-carbon EV CFs planning model is proposed in this paper for the coupled transportation and power systems. This model not only takes into account the construction costs of newly-built CFs and their adverse impacts on the distribution system, but also considers the carbon emissions of CFs and the penalty due to the inconvenience for EVs to be recharged. In addition, a rational travel route choice model is significantly important to accurately evaluate the service capability of CFs to be constructed and thereby to obtain an optimal CF planning result. Therefore, a novel travel route choice model developed in this work allows EV drivers to take detours according to the CF locations to charge their EVs multiple times and then finish their trips. Carbon emission flow (CEF) model is innovatively employed to precisely calculate CFs’ carbon emission amount from the perspective of consumption side. Subsequently, uncertainties involved in CF planning, i.e., distribution and growth rate of traffic flow and electric load, popularity of different types of EVs, as well as location of the connected node of clean electricity, are fully considered to obtain a robust CF planning scheme that can achieve a good performance in the current stage and exhibit robustness for the uncertainties in the future stage. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The impacts of future carbon price and EV cruising range on the planning results are also comprehensively evaluated.

Energy management of green charging station integrated with photovoltaics and energy storage system based on electric vehicles classification

As the number of electric vehicles (EVs) increases, EV charging demand is also growing rapidly. In the smart grid environment, there is an urgent need for green charging stations (GCS) to effectively manage the internal photovoltaic (PV), energy storage system (ESS), charging behaviors of EVs and energy transactions with entities. In this paper, a novel EV classification approach was proposed for GCS, of which the objective is to minimize the total cost of energy trading between charging station and entities. Based on EV charging requirements and risk preference for participating in demand response (DR), EVs are classified into three categories: (1) regular; (2) conservative; and (3) vehicle to grid(V2G). For different types of EVs, charging price schemes are proposed for GCS to stimulate more EVs to interact with the grid and duly dis-charging their power in the capacity of V2G runners. In dealing with the uncertainty issues caused by photovoltaic generation, chance-constrained and probabilistic sequence methods are employed to solve the proposed framework with satisfactory computational efficiency. Case studies based on genuine PV generation, load and pricing data are carried out to verify the proposed framework. It is demonstrated V2G EVs can dramatically reduce the total cost to the GCS. Besides, V2G EVs also can greatly reduce the charging cost compared to regular and conservative EVs.

Solar based EV Charging Station using RFID

A new and emerging technology in the electricity and transportation industries, electric vehicles offer numerous advantages from both an economic and environmental standpoint. This study provides a thorough analysis of different types of electric vehicles, as well as the accessories that go with them, including battery chargers and charging stations. Comparing the electric range, battery size, charger power, and charging time of commercial and prototype electric vehicles is done. The different kinds of electric car charging stations and industry standards have been described, and the effects of electric vehicle charging on utility distribution networks have also been covered.

The GaN Breakthrough for Sustainable and Cost-Effective Mobility Electrification and Digitalization

Vehicle electrification and digitalization are even more and more increasingly pushed by several aims, such as the improvement in sustainability, wellness, safety, and reliability. The need for onboard power electronics is a common factor for the different types of electric vehicles. Similarly, the increasing presence of onboard Internet of Things items as well as systems implementing artificial intelligence asks for additional power electronics that ensure supplying them according to their rating plate. Additionally, power converters have to perform this task also for traditional loads (infotainment; climate control, and so on) and new ones (USB and wireless chargers; Light detection and ranging, LiDAR applications, and so on). The transaction towards full electrification and digitalization requires highly efficient and compact power electronics converters. In this perspective, the Gallium nitride (GaN) high-electron-mobility transistors (HEMTs) are the best enabler device technology. This paper analyses the current and prospective improvement in sustainability and cost achievable with GaN in the automotive sector. Then, the advantages of the system in package GaN devices in power converter supporting this transaction, such as 48 V–12 V converters, onboard charges, and cell phone wireless chargers are experimentally investigated. The results have highlighted that, already in 2023, the use of Si devices in these applications does not enable further a lower overall converter cost with respect to GaN-based solutions. The reduction of passive costs enabled by using higher frequency thanks to the GaN features compensates for its higher cost. On the other hand, GaN HEMT enables high efficiency in all the aforesaid automotive applications as also experimentally proved in this paper, making it the best solution in terms of sustainability.

Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning

When a large-scale random charging load is connected to the regional power grid, it can negatively affect the safe and stable operation of the power grid. Therefore, we need to study its charging load and response potential in advance so that electric vehicles can interact well with the grid after being connected to the regional grid. Firstly, after analyzing the influencing factors of regional electric vehicle ownership, an electric vehicle ownership prediction model based on the sparrow search algorithm-improved BP neural network (SSA-BPNN) is established. On this basis, an electric vehicle charging load prediction model is established based on the sparrow search algorithm-improved BP neural network and Monte Carlo algorithm (SSA-BPNN-MC). Secondly, the charging behavior of different types of electric vehicles is analyzed and modeled, and the data from a certain area are taken as an example for the prediction. Then, according to the load forecasting results, the potential of electric vehicles participating in demand response in the region in the future is deeply analyzed using the scenario analysis method. Finally, with the aim of resolving the problems of massive multi-source heterogeneous data processing and the management of electric vehicles participating in the regional power grid demand response, a basic framework of electric vehicles participating in the regional power grid demand response is developed, which provides effective support for promoting electric vehicles to participate in regional grid demand response.

Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt

Due to the exponential expansion of the global fleet of electric vehicles (EVs) in the utility grid, the vehicle-to-grid paradigm is gaining more attention to alleviate the pressure on the grid. Therefore, an EV aggregator acts as a resilient load to enhance the power deficiency in the electrical grid. This paper proposes the vital development of a central aggregator to optimize the hierarchical bi-directional technique throughout the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This study was implemented using three different types of EVs that are assumed to penetrate the utility grid throughout the day in an organized pattern. The aggregator determines the number of EVs that would participate in the electric power trade during the day and sets the charging/discharging capacity level for each EV. In addition, the proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner using the V2G technology and ensuring a sufficient grid peak load demand shaving based on the genetic algorithm (GA). Three case studies were investigated based on the parking interval time where the battery degradation cost was minimized to reach approx. 82.04%. However, the revenue of the EV owner increased when the battery degradation cost was ignored. In addition, the load demand decreased by 26.5%. The implemented methodology ensured an effective grid stabilization service by shaving the load demand to identify the average required power throughout the day. The efficiency of the proposed methodology is ensured since our output findings were in good agreement with the literature survey.

CO2 Emission Analysis for Different Types of Electric Vehicles When Charged from Floating Solar Photovoltaic Systems

Renewable energy and electric vehicle technology are the two pillars for achieving a sustainable future. Floating solar power plants use PV modules on water infrastructure to save the land and increase module efficiency. Furthermore, the reduction in evaporation saves water. Electric vehicles are one of the fastest-growing markets and the most successful technologies to combat the problem of energy and climate change. This research aims to construct a floating PV system on the lake of the Vellore Institute of Technology (VIT), to analyze electric vehicle performance and greenhouse gas (GHG) emissions when charged using the installed floating PV system. To address this, a 1.5 MWP floating PV system was simulated and analyzed using Helioscope software. When charged from the proposed floating PV plant, electric bikes, scooters, and cars saved CO2 emissions. When charged from a floating PV, E-bike, E-scooter, and E-car Net CO2 emissions became zero in 25.5, 12.1, and 7.7 months, respectively. After the aforementioned time periods, all three electric vehicle types were zero-emission vehicles. The required charge for all three types of vehicles (1,000,000 km) was analyzed using a floating PV system. E-bike, E-scooter, and E-car CO2 emission savings were −8,516,000 g/kWh, −328,000 g/kWh, and 525,600,000 g/kWh, respectively. All three types of electric vehicles can reduce CO2 emissions for nations that rely on renewable energy, but only electric cars save carbon emissions over fixed distances. Through this research, we finally conclude that electric cars reduce CO2 emissions the most compared to other electric vehicles.

Electric Bike Range Estimation using Fuzzy Logic Controller

Electric Vehicles (EV) are the prompt solution to significantly lowering the use of fossil fuels and CO2 emissions from the transport industry. There is a continuing growth in the number of EVs in use, but their huge acceptance by customers is associated to the quality they can provide. Nowadays, different types of electric vehicles are moving toward green awareness, and one among them is the electric motorcycle which is a considerable vehicle in India. Though there are many benefits of driving electric motorcycles, because of the limited driving range and inadequate charging stations, it is still not generally accepted in the industry. Range anxiety is the major market concern that is solved by the implementation of an additional range estimating technique that can ease the "range anxiety" caused by the restricted range of EVs. Therefore, this paper proposes a fuzzy logic controller model for the estimation of the EV range based on the battery's state of charge and the load's power usage. In this work, the load power consumption of the vehicle and the status of the battery charge are selected as inputs and the EV range is selected as a Fuzzy Logic Controller output. This model is implemented in the Matlab/Simulink environment.

Gap Analysis in Eco Categories, Electric Vehicle Comparison and Solutions to Global Transport Challenges

Nowadays, the world is facing many challenges that require a fast environmental and energy transition, but some obstacles have been found. A gap analysis has been made and the conclusion is that there are flaws in the eco categories distribution of different types of electric vehicles. A comparison is done and the conclusions are that hybrid electric vehicles (HEVs) and plugin hybrid electric vehicles (PHEVs) with smaller batteries are the most versatile, while the battery electric vehicles (BEVs) are good primarily for urban driving. The BEVs reduce the urban pollution, but the global ecological effects are rather controversial and their usage still has many limitations. A discussion of adequate options and solutions for the transport pollution and energy problems is presented. The ultimate goal is to provoke adjustments in government transportation policies towards appropriate solutions that solve today’s pollution problems in better and more adequate way.

Full Implementation of Electric Mobility in a Countryside Region of Spain

The ongoing spread of electric sustainable mobility is transforming the local ways of transport in metropolitan areas. This is meant to be extended outside of big cities in the near future thanks to new technological developments. Little towns should adapt to these changes, as they are located geographically far from the big cities and are generally characterized by low economic and demographic indicators. Hence, little towns must keep pace with these changes in mobility to avoid being isolated from the main cities in a country. People living in the countryside usually move toward big cities for various reasons, either related to work or living necessities. Therefore, it must be possible to conduct usual displacements through the use of electric vehicles (EVs), i.e., reaching the destinations and supplying the batteries through charging infrastructures. This paper studies the full implementation of electric mobility applied in the case of Cuenca, a city located in middle Spain. A brief geographical context is provided, together with the routes and destinations of interest considered. Then, different EVs are considered and an analytical vehicle model is provided. The model was exploited to simulate the electrical energy demand to reach the destinations chosen; the results allow comparing the performances offered by different types of EVs. This aspect is then considered as the basis to propose further upgrades in the charging infrastructures where needed, to comply with the widespread use of electric mobility.

A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles

The increasing popularity of electric vehicles (EVs) has been attributed to their low-carbon and environmentally friendly attributes. Extensive research has been undertaken in view of the depletion of fossil fuels, changes in climatic conditions due to air pollution, and the goal of developing EVs capable of matching or exceeding the performance of today’s internal combustion engines (ICEs). The transition from ICE vehicles to EVs can reduce greenhouse gases significantly over a vehicle’s lifetime. Across the different types of EVs, the widespread usage of batteries is due to their high power density and steady output voltage, making them an excellent energy storage device (ESD). The current downsides of battery-powered electric vehicles include long recharge times, the impact of additional strain on the grid, poor societal acceptance due to high initial costs, and a lack of adequate charging infrastructure. Even more problematic is their short driving range when compared to standard ICE and fuel cell EVs. Battery degradation occurs when the capacity of a battery degrades, resulting in a reduction in travel range. This review article includes a description of battery degradation, degradation mechanisms, and types of degradation. A detailed investigation of the methods used to address and reduce battery degeneration is presented. Finally, some future orientation in terms of EV research is offered as vital guidance for academic and industrial partners.

Charging Electric Vehicles Today and in the Future

It is expected that more vehicles will be electrified in the coming years. This will require reliable access to charging infrastructure in society, and the charging will include data exchange between different actors. The aim of this review article is to provide an overview of recent scientific literature on different charging strategies, including for example battery swapping, conductive- and inductive charging, and what data that may be needed for charging of different types of electric vehicles. The methodology of the paper includes investigating recent scientific literature and reports in the field, with articles from 2019 to 2022. The contribution of this paper is to provide a broad overview of different charging strategies for different types of electric vehicles, that could be useful today or in the coming years. The literature review shows that data utilized for charging or discharging includes for example information on the battery, temperature, electricity cost, and location. It is concluded that the preferred charging strategy for an electric vehicle may depend on the type of electric vehicle and when, where, and how the vehicle is used.

Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data

This research proposes an approach to estimate the number of different types of electric vehicles for a vast area or an entire country, which can be divided into a large number of small areas such as a subdistrict scale. The estimation methodology extensively utilizes the vehicle registration data in conjunction with Thailand’s so-called EV30@30 campaign and GIS-based road infrastructure data. To facilitate the analysis, square grids are built to form cells representing the number of electric vehicles in any specific area of interest. This estimated number of electric vehicles is further analyzed to determine the energy consumption, calculate the recommended number of public chargers, and visualize an increase in the substation loads from those charging stations. The effectiveness of the proposed methods is demonstrated using the whole area of Thailand, consisting of five regions with a total area of 513,120 km2. The results show that the trucks contribute the most energy consumption while taxis rely on a lot of public chargers. The total energy consumption is about 79.4 GWh per day. A total of 12,565 public fast chargers are needed across the country to properly support daily travel, around half of them being located in the metropolitan area.

Health-Conscious vehicle battery state estimation based on deep transfer learning

Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle.

An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles

In this paper, we present a discussion about green energy sources that have been widely utilized in electric vehicles (EVs). To achieve different requirements of various EVs, the correct sizing of energy sources is crucial so that the cost and output performance will be optimized. In this research, three energy sources, supercapacitors (SCs), lithium titanate oxide (LTO) batteries, and Nickel Manganese Cobalt (NCM) (or Li3) batteries, were considered for hybridization. An effective global search algorithm (GSA) was designed for optimal sizing of hybrid electric energy systems (HEESs). The GSA procedures were: (1) vehicle specification and performance requirements of energy sources, (2) determination of cost function and constraints, (3) GSA optimization with for-loops, (4) optimal results. Five examples of EVs, the electric sedan, long-distance electric bus, short-distance electric bus, electric forklift, and electric sports car, were analyzed for optimal hybrid energy combination under different criteria and specifications. The GSA effectively optimized the designs of energy sizing. The performance indices and vehicle requirements studied were the specific price, specific energy at a constant volume, specific energy at a constant mass, and specific power at a constant mass for three energy sources, SCs, LTO batteries, and Li batteries. The vehicle requirements including the maximum output power, vehicle acceleration, climbability, and maximum speed have been formulated as the design constraints. A numerical analysis of five types of EVs was analyzed for optimal sizing of the HEES and the optimal position of the DC/DC converter with the lowest cost function. The integrated system and control designs of the HESS using the GSA, more applications for green energy sources, and different types of EVs will be studied in the future.

Global research on the air quality status in response to the electrification of vehicles

Electric vehicles (EVs) can substantially decrease atmospheric pollutant emissions, thereby improving air quality, decreasing global warming, and improving human health. In this study, we performed a comprehensive bibliometric analysis using Web of Science to understand the research developments and future perspectives in EVs between 1974 and 2021. The analysis of indicators such as research trends, publication growth, and keywords revealed that most research in the selected timeframe was focused on applying and optimizing the existing technologies of different types of EVs to decrease air pollution and mortality. The changes in air quality owing to such electrification received special attention, with approximately 441 publications preferably in the English language. Among all the retrieved documents, research articles were most common (n = 295; 66.89% of the global output), dominated by the research domains of environmental sciences, followed by energy fuels and transportation science technology. Journal analysis revealed that Sustainability (n = 19, 4.30%) was the leading journal, followed by Journal of Cleaner Production and Science of the Total Environment. The most frequently used keywords were “electric vehicles,” “air quality,” and “air pollution.” The most highly impactful article was published by Jacobson et al. (2005) in Science, with 620 total citations and 38.82 average annual citations. Furthermore, the United States (n = 118; 26.75% of the global output) had the highest publication rate, followed by China and the United Kingdom. The leading institutions were Tsinghua University (n = 16; 3.62% of the global research output) in China, followed by the University of Michigan and Cornell University in the United States. The current analysis warrants more focus on comprehensive analysis employing transport and chemistry modeling and using the latest technology for long life and sustainable batteries. This study provides a basis for future studies on improving air quality through innovative work in the electrification of vehicles.