Types Of Electric Vehicles Research Articles

Multi-type electric vehicle load prediction based on Monte Carlo simulation

In the paper, an electric vehicle (EV) charging load prediction approach considering vehicle types is developed. The GM (1,1) model is initially introduced to estimate the ownership of electric vehicles (EVs), and the model is validated by examining the difference between the forecast results and the actual situation. The Monte Carlo algorithm is also used to build the probability distribution model of travel laws and charging characteristics to predict the load demand when the scale of EVs is connected to the grid. For various types of EVs, the charging behavior is analyzed by considering charging start moment, charging duration, the initial state of charge (SOC), daily mileage, and charging mode. The charging load curves for each type of EV are superimposed to obtain the total load demand curve. Then it is compared with the original load curve in a certain region to analyze the impact of EV charging load on the original load curve of the grid, and to provide theoretical guidance for grid planning and optimal dispatching.

Which type of electric vehicle is worth promoting mostly in the context of carbon peaking and carbon neutrality? A case study for a metropolis in China

Electric vehicles (EVs) have been promoted acceleratively to reduce greenhouse gas (GHG) emissions, however, the GHG emission reduction potential of different powertrain EVs has not been investigated thoroughly. In this study, we firstly quantified and compared the GHG emissions of different powertrain vehicles in a life cycle perspective with particular focus on energy and climate consequences, for current and future integrated scenarios, to facilitate carbon reduction assessment for Shanghai. Four major types of EVs were considered. The results show that life cycle total energy consumption and GHG emissions of all EVs are lower than that of gasoline internal combustion engine vehicles (GICEVs), among which battery-powered electric vehicles (BEVs) is the lowest. Compared with GICEVs, the total energy use and GHG emissions of BEVs decrease by 34.2% and 41.7% respectively. As the electrification of vehicle powertrain system innovates, the life cycle emissions of GHG are gradually concentrated to the upstream stage. The sensitivity analysis demonstrates that life cycle GHG emissions of vehicles are most sensitive to the proportion of thermal power than other three parameters (utilization rate of recycled steel, vehicle lifetime and curb weight). The scenario analysis indicates that BEVs present the more favorable carbon emission decline performance over other EVs from a long-term perspective. It is estimated that up to 12.5 million tons of GHG emissions could be reduced under the optimistic scenario in 2050 in Shanghai. In the process of energy conversion from oil to electricity in transport in Shanghai, BEVs should be constantly promoted.

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.

Electric Vehicles in Malaysia and Indonesia: Opportunities and Challenges

In the roadmaps of the automotive industry, the electric vehicle (EV) is regarded as a crucial technology for the future of automotive power systems. The EV has become a top priority of major global car manufacturers and is expected to disrupt the road transportation sector. In Malaysia and Indonesia, EVs just started as an important force. However, in Malaysia, the lack of EV infrastructure, along with its strong dependency on fossil fuels, poses an enormous challenge. The situation is very similar in Indonesia. Indonesia has huge potential as Southeast Asia’s largest vehicle market and a major nickel producer, an important EV battery ingredient. Therefore, this article addresses several critical issues in implementing EVs in Malaysia and Indonesia. In preparing this review, we have thoroughly selected very important EV keywords that are frequently asked. We have also interviewed some prominent figures in the field of EV to address the most critical aspects worth including in the paper. In doing so, we plan to provide content that will be beneficial not only to the academic world but also to the automotive industry in general. Firstly, a summary of the EV adoption scenario in each country was presented. Afterwards, the types of EVs and battery capacities available in both countries were explained. The next section focused on the adoption rate of EVs, followed by the discussion of EVs charging infrastructure. In addition to that, issues pertaining to vehicle tax credit were also addressed. The opportunities and challenges of EV were then addressed in depth before concluding remarks were given.

Coming Electric Vehicle Era

Coming Electric Vehicle Era

Multi-year planning for the integration combining distributed energy system and electric vehicle in neighborhood based on data-driven model

As more residents choose electric vehicles (EVs), the strain on the neighborhood’s energy supply will increase due to EVs’ dynamic charging requirements. Furthermore, the distributed energy system (DES) has demonstrated its benefit on local energy supply, considerably increasing its efficacy. This study concentrates on multi-year planning for the integration framework combining DES and EV charging supply in the neighborhood business center(NBC). Two types of EVs are considered, and their models are developed separately based on different stochastic behaviors. A novel data-driven method using real weather data to generate charging scenarios is proposed, which can provide quantitative guidance for planning. Latin hypercube sampling(LHS) is applied as the technical method for scenario generating. Machine learning(ML) clustering method with elbow criterion is used to conduct scenarios reduction. The multi-year planning optimization of the integration is accomplished based on two-stage stochastic programming. The multi-year planning project is employed in a NBC in Beijing. The planning result of integration cases with different external power limitations is assessed comprehensively. Other planning methods are introduced and compared. It shows that the integration case with moderate limitation can achieve a reduction of 67.8% and 31.6% respectively in economic cost and carbon emission. Furthermore, the EV satisfaction rate in varied scenarios is evaluated. What is more, the integration cases are furtherly assessed based on varied EV growth expectations. In general, the results have verified the effectiveness of the multi-year planning in neighborhood.

RETRACTED: A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.

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.

High Gain Converter with Improved Radial Basis Function Network for Fuel Cell Integrated Electric Vehicles

In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that uses a fuel cell combined with a small battery or super-capacitor to power its on-board electric motor. However, the power obtained from the FC system is much less and is not enough to drive the EV. So, another energy source is required to deliver the demanded power, which should contain high voltage gain with high conversion efficiency. The traditional converter produces a high output voltage at a high duty cycle, which generates various problems, such as reverse recovery issues, voltage spikes, and less lifespan. High switching frequency and voltage gain are essential for the propulsion of FC-based EV. Therefore, this paper presents an improved radial basis function (RBF)-based high-gain converter (HGC) to enhance the voltage gain and conversion efficiency of the entire system. The RBF neural model was constructed using the fast recursive algorithm (FRA) strategy to prune redundant hidden-layer neurons. The improved RBF technique reduces the input current ripple and voltage stress on the power semiconductor devices to increase the conversion ratio of the HGC without changing the duty cycle value. In the end, the improved RBF with HGC achieved an efficiency of 98.272%, vehicle speed of 91 km/h, and total harmonic distortion (THD) of 3.12%, which was simulated using MATLAB, and its waveforms for steady-state operation were analyzed and compared with existing methods.

Optimal Siting and Sizing of Wireless EV Charging Infrastructures Considering Traffic Network and Power Distribution System

The main challenges in the widespread deployment of electric vehicles (EVs) are limited driving range, long charging downtime, lack of charging stations in some areas, and high battery cost. Dynamic wireless charging (DWC) technology can overcome some of these obstacles by recharging EV batteries remotely while vehicles move over the charging infrastructures. However, most studies have limited this technology to public EVs working in specific routes like electric buses and taxis routes. This paper presents a long-term stochastic scenario-based mathematical model for allocating and sizing DWC infrastructures considering Ev’s location-routing, power distribution system (PDS) losses, and transportation network traffic. The proposed long-term model allows all types of EVs to take advantage of the installed DWC infrastructures and facilitate EVs’ widespread use by overcoming conventional charging technologies problems. The optimization problem is structured in the form of a Mixed Integer Non-Linear Programming (MINLP) model. Simulation results with detailed analyzes are furnished to illustrate the characteristics and performance of the model in a coupled network combining transportation and power networks. The numerical analysis provides meaningful insight into the transportation system design based on DWC technology and the effect of EV routing on the charging infrastructure allocation. Simulation results show that using Location-Routing Problem (LRP) can save up to 45% of the total cost of the DWC system. In addition, the proposed model, along with the simulations performed, demonstrates the advantage of DWC technology in reducing the size of vehicle batteries.

GaN Semiconductors Driving More Efficient Automotive Traction Inverters [Expert View

Electric Vehicles (EVs) are continuing to grow their share of total vehicle sales as consumers, companies, and governments prioritize automotive electrification. Types of EVs include hybrid electric, plug-in hybrid electric, battery electric, and fuel cell vehicles. Today, EVs commonly use an ac permanent magnet or ac induction motor. A traction inverter is a critical drivetrain element required to convert the dc current available from the battery pack to a variable frequency ac current. In addition, it acts as a rectifier to regenerate the energy from motor to the battery pack during braking.

Grid Integration of New and Old Electric Vehicles Considering the Battery Degradation Model

In the last decade, the upward trend of electric vehicles (EVs) in the automobile industry facilitates end-users with long-term benefits in fuel efficiency and a decrease in global warming. Uncoordinated and random EV charging would create an extraordinary and unnecessary burden on the distribution grid. EVs integration into a charging station has distinct parameters, such as human adaptation, state of charge (SOC), battery life, location of charging stations, charging time, charging cost, etc. Thus, a structured model is required to fulfill the human demand and maintain the overall efficiency of the charging stations to deliver the most desirable and optimal outcome. With the Netlogo platform, this paper will achieve the complex simulation phenomena and real-time urban architecture simulation for the multi-charging stations. The battery degradation algorithm connects and distinguishes EVs type that further identifies the SOC, and depth of discharge (DOD) of the battery. The model has four discrete stations, including dedicated EV charging stations, commercial, offices, and residential. This approach is promising; analyzing several human factors in large-scale grid integration for both new and old EVs is tested in this paper to cover existing models. This scalable architecture could be adapted and extended for more extensive or different urban infrastructure to design a practical EV charging station.

Electric vehicle route planning using real-world charging infrastructure in Germany

Electric vehicles (EVs) are a way to reduce emissions from mobility. To replace conventional vehicles, EVs must be able to deliver a similar level of comfort on long-distance travel. A key enabler here is an adequate charging infrastructure (CI). In this paper, we evaluate the current CI in Germany, a leading adopter of EVs in Europe. The goal of this evaluation is to understand where innovations and investments need to take place such that overall costs are minimized while user comfort is maximized. We perform this evaluation by analysing travel duration for five typical EVs on 60 routes through Germany. We include non-linear, state-of-charge-dependent charging power as well the non-linear velocity-dependent energy consumption. EVs are modelled after five typically used vehicles in Germany and charging stations are based on real-world data taken from the federal registry. This level of detail in modelling allows for representative results. In our sample, travel time is ∼8% longer when charging compared to non-stop driving. The delay strongly depends on route distance where many routes with <300 km length can be driven non-stop while some extreme cases through areas with unsuitable infrastructure result in 30% more driving time. The necessary breaks were in line with recommendations for truck drivers in Germany. To understand the impact of key technical data we perform a sensitivity analysis. The most relevant factors are start state of charge (SOC) since a low SOC means that typically an additional stop is necessary, often at unfavourable locations. A start SOC of 30% results in 30% additional travel time compared to a non-stop drive. A low energy efficiency and battery capacity of the car (70% of base case) increases travel time by 20%. Increasing charging station power does not have a strong effect since current vehicles cannot charge at 350 kW. • Calculation of time required for recharging in Germany. • 60 routes calculated with 5 vehicle models. • Measured charging curves and energy consumption model for vehicles. • Parameter sweep for battery capacity, charging power, etc.

A Blockchain-Supported Framework for Charging Management of Electric Vehicles

Profound changes driven by decarbonization, decentralization, and digitalization are disrupting the energy industry, bringing new challenges to its key stakeholders. In the attempt to address the climate change issue, increasing penetration of renewables and mobility electrification augment the complexity of the electric grid, thus calling for new management approaches to govern energy exchanges while ensuring reliable and secure operations. The emerging blockchain technology is regarded as one of the most promising solutions to respond to the matter in a decentralized, efficient, fast, and secure way. In this work, we propose an Ethereum-based charging management framework for electric vehicles (EVs), tightly interlinked with physical and software infrastructure and implemented in a real-world demonstration site. With a specifically designed solidity-based smart contract governing the charging process, the proposed framework enables secure and reliable accounting of energy exchanges in a network of trustless peers, thus facilitating the EVs’ deployment and encouraging the adoption of blockchain technology for everyday tasks such as EV charging through private and semi-private charging infrastructure. The results of a multi-actor implementation case study in Switzerland demonstrate the feasibility of the proposed blockchain framework and highlight its potential to reduce costs in a typical EV charging business model. Moreover, the study shows that the suggested framework can speed up the charging and billing processes for EV users, simplify the access to energy markets for charging station owners, and facilitate the interaction between the two through specifically designed mobile and web applications. The implementation presented in this paper can be used as a guideline for future blockchain applications for EV charging and other smart grid projects.

Toward a Unified Model Approach for Evaluating Different Electric Vehicles

Considering rising pollution as well as fuel expenses, it has now become critical to transition to a sustainable method of transportation. As a result, automakers have begun to spend on research and development in the electric vehicle (EV) industry. The amount of EVs has expanded rapidly in recent years. This is owing to new improved technology, particularly in electric motor engineering, as well as government initiatives to limit the level of environmental impact produced by combustion engines. Because EVs are powered by electricity, implementing their charging stations presents certain complications. In this paper, we have discussed the different types of EVs, such as BEVs, FCEVs, HEVs, PHEVs, and REHEVs. Even though the capacity of many of these electric car models has been substantially enhanced within the past few years, some challenges remain as a selection barrier for several customers. Considering these challenges, we have also implemented a fuzzy AHP-TOPSIS-based unified model to evaluate the different types of EVs. The study’s technical importance is the identification of various evaluation factors, implementation of a unified model for measuring performance, and computation using the fuzzy MCDM technique. The outcomes of the unified model approach also were validated. We concluded that FCEVs are excellent for long journeys, and have the resources to cause minimal disruption.

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.

Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment

The rising level of carbon emissions has plunged the world into global warming. These emissions originate from different sectors however, one of the major contributions comes from the transportation sector. In order to tackle this problem, greener modes of transportation like Electric Vehicles (EV) are introduced as an alternative option. Currently, different types of EVs are available in the market, with Hybrid Electric Vehicles (HEV) being the most popular type of EV in developing countries. Nevertheless, a lot of customers are still preferring Conventional Vehicles (CV) over EVs, which is inevitably damaging the market share of EVs and creating problems in their wider acceptance. Thus, the aim of this study is to solve two problems. First, to identify the factors which make CVs more appealing to the customers. Secondly, how these shortcomings can be overcome for HEVs. For this purpose, the relationship of different parameters with CVs is evaluated using the multiple regression method, which is then incorporated into the Fuzzy Quality Function Deployment (FQFD) model to find the best solution for adding those parameters to HEVs. The analysis resulted in the identification of affordability, reliability, variety and fuel consumption as the key contributors that made CVs, a more attractive option to the customers. Furthermore, local manufacturing was identified as the best solution for improving the quality of HEVs and make them market competitive in developing countries. The practical applications of this research, along with the contextual analysis of the developing countries are the principal novelties of the study.

Road surface influence on electric vehicle noise emission at urban speed

Abstract Considering the relative quietness of electric motors, tyre/road interaction has become the prominent source of noise emission from Electric Vehicles (EVs). This study deals with the potential influence of the road surface on EV noise emission, especially in urban area. A pass-by noise measurement campaign has been carried out on a reference test track, involving six different road surfaces and five electric passenger car models in different vehicle segments. The immunity of sound recordings to background noise was considered with care. The overall and spectral pass-by noise levels have been analysed as a function of the vehicle speed for each couple of road surface and EV model. It was found that the type of EV has few influence on the noise classification of the road surfaces at 50 km/h. However, the noise level difference between the quietest and the loudest road surface depends on the EV model, with an average close to 6 dBA, showing the potential effect of the road surface on noise reduction in the context of growing EV fleet in urban area. The perspective based on an average passenger EV in a future French or European electric fleet is addressed.

WITHDRAWN: Design, simulation analysis of universal battery management system for EV applications

Abstract Now a days, Electric Vehicles (EVs) are familiar with abundant types of battery chemistries with different specifications. Battery Management System (BMS) is an essential and important device of an EV for smooth operation and long life. In present scenario, a separate BMS has been used for the particular battery. So, there is a need of a common BMS for all type of EVs. This paper proposes a simulation design of common BMS for four different batteries namely Nickel-Metal-Hydride, Nickel-Cadmium, Lithium-Ion and Lead-Acid. A common simulation and mathematical modelling of aforementioned four types of battery is executed here. The proposed system is designed to manipulate and control the instantaneous values of the battery parameters like Battery voltage, Current, Power consumption and State of Charge (SoC). This proposed BMS continuously monitors the battery parameters with high accuracy, especially in SoC measurement. The working of BMS is controlled and monitored by using Extended Kalman Filter (EKF). The entire mathematical modelling and simulation work of BMS is analysed by using MATLAB/Simulink.

800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends

Two of the main challenges for electric vehicle (EV) adoption include limited range and long recharge times. Ultrafast charging can help to mitigate both these concerns. However, for typical 400-V battery EVs (BEVs), the charging rate is limited by the practical cable size required to carry the charging current. To reach ultrahigh charge rates of 350 or 400 kW, 800-V BEVs are a promising alternative. However, the design of an 800-V EV requires careful new considerations for all electrical systems. This article reviews the current state of 800-V vehicle powertrain electrical design and performs an analysis of benefits, challenges, and future trends regarding multiple vehicle powertrain components. Specifically, detailed benefits and challenges related to the battery, propulsion motor, inverter, auxiliary power unit, and on- and off-board chargers are discussed.