Potential Of Electric Vehicles Research Articles

The potential of electric agriculture and mobility for the least-cost rural electrification strategy in Sub-Saharan Africa

Electrification is a crucial driver of rural development in Sub-Saharan Africa (SSA), yet progress is often impeded by low demand density, particularly for grid extension. Despite the promising potential of stand-alone and mini-grid systems, rural areas frequently lag behind due to their low economic viability in electrification. Simultaneously, the mechanization of agriculture and the motorization of transport offer the opportunity to increase economic activity. This publication explores the role of electric vehicles (EVs) and electric tractors (ETs) in rural electrification scenarios.We present a methodology to quantify the theoretical impact of EVs and ETs on SSA’s electrification strategies, applied specifically to Nigeria and Chad. Our simulations in Nigeria reveal that not only supply options with higher capacity are needed, but that EVs have the potential to lower the cost of electricity (LCOE) by an average of 54 %, and ETs have the potential to reduce LCOE by 49 %, depending on local mobility and mechanization demand. Rural areas with significant cropland particularly benefit from using ETs to achieve cheaper electricity access. In Chad, where grid infrastructure is minimal, our assessment shows that EVs and ETs can significantly influence least-cost electrification strategies. By 2030, mini-grids will become the least-cost supply option for 60% of the newly connected population, compared to just 13% when only residential demand is considered.Our methodology advances the widely used ONSSET electrification planning model to estimate the maximum joint potential of electrified agriculture, electric mobility and adapted rural electrification strategies. Our approach involves geospatially estimating energy demands using macroscopic available population, land cover, and infrastructure data. The findings underscore the importance of jointly implementing EVs and ETs and electrification strategies for technology leapfrogging, highlighting the economic viability and potential of EVs and ETs in rural scenarios. The results provide policymakers with quantitative indicators on the impact of the implementation for Nigeria and Chad.

Comprehensive Research Study of Challenges and Opportunities in Electric Vehicle Adoption: Case Study in Jakarta

This article delves into the potential of electric vehicle (EV) adoption in Central Jakarta, a densely populated area grappling with significant urban and transportation challenges. Trams are an environmentally friendly mode of transport that is environmentally friendly, efficient, comfortable, accessibility, and enhances the aesthetic value of the city. Trams do not produce exhaust emissions, so they can help reduce air pollution in big cities. This is important for improving air quality and public health. The research variables include cost implications, environmental impact, and other relevant aspects of transitioning from fuel-based cars and motorcycles to electric vehicles. The research extensively explores various reputable sources such as papers, journals, and research studies relevant to the chosen research variables, enhancing the credibility of the findings. The study reveals that while the adoption of EVs presents several opportunities, such as reducing traffic congestion and environmental pollution, it also poses significant challenges. These include high costs of EVs, lack of charging infrastructure, and consumers' unfamiliarity with the technology. The research suggests that addressing these challenges requires an integrated and comprehensive solution, including policy measures such as increasing public charging infrastructure, transforming tax schemes, and providing incentives for EV users. This research contributes to the ongoing discourse on sustainable urban transportation, providing valuable insights for policymakers, academia, and the local community in Jakarta and beyond.

Ensuring a just transition: The electric vehicle revolution from a human rights perspective

Despite the potential of electric vehicles (EVs) to mitigate climate change and reduce reliance on fossil fuels, their rapid adoption presents nuanced challenges to both social equity and environmental stewardship within the transportation sector. While the EV revolution contributes to energy independence, job creation, and sustainable development, it also raises concerns regarding its potential impact on the human rights of people impacted by the production, use, and end-of-life of EVs. Unfortunately, current studies on transportation electrification often fail to provide a systematic review of such human rights challenges and concerns. This paper aims to fill this gap by analyzing the potential impacts and challenges that EVs present for human rights throughout their life cycle, and by proposing potential measures to address human rights violations. Our analysis draws on international human rights norms, law, and standards because of their universality and suitability for developing baseline indicators, modes of inquiry, and reporting that can be used to assess the potential harm of EVs to vulnerable communities and degraded ecosystems. The aim is to enable stakeholders (i.e., actors within civil society, government, and the private sector) to better understand the potential social and environmental challenges of EVs and ensure that the transition to low-carbon transportation is equitable, sustainable, and supportive of human rights.

Smart transportation systems using learning method for urban mobility and management in modern cities

The pressing demand for an escalation in renewable energy production arises from the surge in power consumption, escalating environmental concerns, and the exhaustion of fossil fuels. Microgrids, featuring dispersed power sources, exhibit superior resilience, efficiency, and stability in contrast to conventional power grids. Noteworthy investments in eco-friendly initiatives have been made by various major corporations, particularly in institutional structures. The current research advocates for the integration of photovoltaics, energy storage, and electric vehicles (EVs) as pivotal elements in a smart grid. This study introduces an optimal energy management system (EMS) designed to efficiently allocate power from existing sources. To address the challenges within an uncertain and reliable environment, a stochastic framework leveraging the whale optimization algorithm and unscented transform has been formulated. All simulations have been executed using the digital twin of the smart grid components. Results suggest that the proposed EMS ensures uninterrupted power supply, contributing to a substantial 45% reduction in energy costs. Furthermore, the potential of EVs as storage devices is explored. When employed as energy sources, EVs led to a noteworthy 45.58% reduction in power prices, while their use as loads still yielded a significant 19.33% decrease. The study also delves into the analysis of outage effects on the continuous power supply cost.

Cheetah optimization algorithm for simultaneous optimal network reconfiguration and allocation of DG and DSTATCOM with electric vehicle charging station

The potential of Electric Vehicles (EVs) to decarbonize the transportation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms.

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The analysis of electric vehicle integration based on big data technology

The potential of electric vehicles (EVs) to reduce greenhouse gas emissions in the transportation sector has given the adoption of EVs a considerable boost in recent years. Concurrently, the field of big data analytics has witnessed exponential growth, providing unprecedented opportunities for extracting valuable insights and optimizing various industrial sectors. This paper presents a comprehensive overview of the intersection between electric vehicles and big data analysis. Various EV-related data sources are explored along with the discussion of data computing platforms. Followed by this, this paper analyzes different use cases of big data analysis in EVs, covering key areas such as energy management, charging infrastructure optimization, and vehicle condition monitoring, which demonstrates how big data can be crucial for the successful integration of EVs into green smart cities. Finally, the author provides future research insight and opportunities for the use of big data techniques in EV adoptions. In particular, this paper serves as a roadmap for future research in the area of data analytics in EV integration.

Driving Factors for Women's Switch to Electric Vehicles in Conservative Kuwait

Electric Vehicles (EV) is probably the most disruptive and innovative product since the introduction of a smart phone in 2007. However, attitude of women towards (EV) in oil rich and conservative countries like Kuwait has not been explored. The aim of this research is to bridge that gap with this study that employed a quantitative method, that is a questionnaire of 234 women drivers. Results showed that more than half of participated women would buy an EV within the next 3 years, if several conditions were met; including cheaper purchasing pricing with government assistance and availability of suitable infrastructure for EV relating to charging stations, fast lanes, and free parking spaces. More than forty percent of the participants would also seriously start thinking about buying EV if the gas/fuel prices increase between 50% to 199% and thought that EVs are safer in relation to fire and car crashes. Moreover, they would pay 6%-20% more for an EV that is both environmentally friendly and much quicker than gasoline cars. In addition, about forty percent of women drivers would be able to charge EV in their residential areas in Kuwait. This study discussed a variety of valuable suggestions to overcome the barriers of EVs introductions to Kuwait and marketing strategies altogether with government initiatives and policies. One of the key conclusions is the fact that focused educational efforts should be undertaken to help female owners in Kuwait to understand better the benefits and the overall potential of EV.

Smart EV Charging Strategies Based on Charging Behavior

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

Achieving carbon-neutral transportation is the ultimate goal of the ongoing joint efforts of governments, policy-makers, and the transportation research community. Electrification of smart cities is a very important step towards the above objective; therefore, accelerating the adoption and widening the use of Electric Vehicles (EVs) are required. However, to achieve the full potential of EVs, ground-breaking detour computation and charging station selection schemes are needed. To this end, this paper developed a new scheme that finds the most suitable detour/route for an EV whenever an unexpected event occurs on the road. This scheme is based on A* and uses an original, Simple-Additive-Weighting (SAW)-based, charging station selection method. The performance evaluation carried out using the open-source traffic simulation platform SUMO under a grid map, as well as a real road network map highlighted that our scheme ensured more than 99% of EVs will reach their destination within a reasonable time even if a battery recharge is needed. This is a significant improvement compared to the baseline scheme that uses the A* only.

Design and Analysis of a High Torque Density Hybrid Permanent Magnet Excited Vernier Machine

Permanent magnet (PM) excited vernier machines capable of high torque density have good potential for electric vehicles while requiring high rare earth PM consumption. To achieve a high torque density at a reasonable material cost, hybrid PM excited vernier machines incorporating both expensive rare earth and low-cost ferrite magnets are investigated in this paper. Various combinations of PM arrangements for the hybrid permanent magnet excited vernier machine are investigated to acquire low cost and superior torque density. The best solution obtained is that the PM on the stator adopts rare earth material while the rotor uses ferrite. Furthermore, the PMs on the stator are arranged in an iron-cored Halbach array, which can reduce leakage flux and enhance flux density effectively and the ferrite PMs are used in the rotor, therefore, high-temperature demagnetization is avoided. Then, the reluctance torque and the cogging torque can offset each other effectively, which is beneficial to reducing the torque ripple and smoothing the electromagnetic torque. Finally, a prototype is manufactured and tested to verify the correctness of the theoretical analysis.

Frequency regulation service of multiple-areas vehicle to grid application in hierarchical control architecture

<span lang="EN-US">Regarding a potential of electric vehicles, it has been widely discussed that the electric vehicle can be participated in electricity ancillary services. Among the ancillary service products, the system frequency regulation is often considered. However, the participation in this service has to be conformed to the hierarchical frequency control architecture. Therefore, the vehicle to grid (V2G) application in this article is proposed in the term of multiple-areas of operation. The multiple-areas in this article are concerned as parking areas, which the parking areas can be implied as a V2G operator. From that, V2G operator can obtain the control signal from hierarchical control architecture for power sharing purpose. A power sharing concept between areas is fulfilled by a proposed adaptive droop factor based on battery state of charge and available capacity of parking area. A nonlinear multiplier factor is used for the droop adaptation. An available capacity is also applied as a limitation for the V2G operation. The available capacity is analyzed through a stochastic character. As the V2G application has to be cooperated with the hierarchical control functions, i.e. primary control and secondary control, then the effect of V2G on hierarchical control functions is investigated and discussed.</span>

Load Transfer Path Search and Its Evaluation between Networks in Consideration of the Mobile Energy Storage of Electric Vehicles

Load transfer is an important way of restoring a power supply after equipment failure or maintenance. However, current methods ignore cooperation between networks and users, and they also fail to take into account the vehicle-to-grid (V2G) potential of electric vehicles (EVs). In this paper, a load transfer scheme between transmission and distribution networks is proposed, considering the mobile energy storage capacities of electric vehicles. First, the mobility characteristic and the available discharge capacity of EVs are analyzed on the basis of the parking generation rate. Then, the breadth-first algorithm is used to search the load transfer paths within and between stations, and an edge matching method is proposed to realize the conversion between networks with different voltage levels. Lastly, the optimal combination weighting method was adopted to combine subjective and objective index weights and to evaluate power supply paths. The effectiveness of the proposed scheme is validated in a case composed of an IEEE 30-node network and an IEEE 57-node network, with four typical scenarios.

Flexibility Reserve of Self-Consumption Optimized Energy Systems in the Household Sector

Energy generation and consumption in the power grid must be balanced at every single moment. Within the synchronous area of continental Europe, flexible generators and loads can provide Frequency Containment Reserve and Frequency Restoration Reserve marketed through the balancing markets. The Transmission System Operators use these flexibilities to maintain or restore the grid frequency when there are deviations. This paper shows the future flexibility potential of Germany’s household sector, in particular for single-family and twin homes in 2025 and 2030 with the assumption that households primarily optimize their self-consumption. The primary focus is directed to the flexibility potential of Electric Vehicles, Heat Pumps, Photovoltaics and Battery Storage Systems. A total of 10 different household system configurations were considered and combined in a weighted average based on the scenario framework of the German Grid Development Plan. The household generation, consumption and storage units were simulated in a mixed-integer linear programming model to create the time series for the self-consumption optimized households. This solved the unit commitment problem for each of the decentralized households in their individual configurations. Finally, the individual household flexibilities were evaluated and then aggregated to a Germany-wide flexibility profile for single-family and twin homes. The results indicate that the household sector can contribute significantly to system stabilization with an average potential of 30 GW negative and 3 GW positive flexibility in 2025. In 2030, the corresponding flexibilities potentially increase to 90 GW and 30 GW, respectively. This underlines that considerable flexibility reserves could be provided by single-family and twin homes in the future.

Role of zero emission vehicles in sustainable transformation of the Indian automobile industry

The Indian automobile industry is growing rapidly; however, it is predominantly served by internal combustion engine vehicles (ICEVs). The ICEVs are the primary source of urban pollution and greenhouse gas (GHG) emissions. The alarming levels of urban pollution are putting enormous pressure on the Indian automobile industry to switch from ICEVs to zero emission vehicles such as electric vehicles (EVs). Even though the potential of EVs as a safe transportation alternative has been acknowledged and encouraged by Indian government, the commercialization of EVs has not received the desired success as of now. There are many hindrances to large-scale adoption of EVs such as high initial price, driving range anxiety, lack of proper charging infrastructure, and underdeveloped battery technology etc. This study aims to evaluate the conducive factors for EV adoption and completes in three phases. In the first phase, supporting forces to EV adoption in India are explored using political, economic, social, technological, environmental, and legal (PESTEL) analysis, and capability needs for EV manufacturing business in India are identified in phase two using the people, process, technology, and finance (PPTF) framework. Finally, segment analysis is performed to find out the most profitable segment to foray into the current situation.

Research on carbon emission potential of electric vehicles and fuel vehicles

Electric vehicles are considered as a effective tool for energy conservation and emission reduction, Because of its zero direct carbon emissions. However, thermal power generation accounts for a large proportion in China's power generation structure, so the indirect carbon emissions of electric vehicles must be considered. In order to further study the carbon emission potential of electric vehicles compared with traditional fuel vehicles, the direct carbon emission of fuel vehicles and the indirect carbon emission of battery electric vehicles were calculated. The results show that the carbon reduction effect of electric vehicles is obvious, and the coal power technology and the energy structure of power generation have great influence on the carbon reduction potential of electric vehicles. Based on this, suggestions are put forward to promote electric vehicles and fully release their emission reduction potential from the aspects of enhancing consumers' perception of social value, improving energy structure and improving coal power technology.

Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.

Application of Life Cycle Assessment to Lithium Ion Batteries in the Automotive Sector

This study is a critical review of the application of life cycle assessment (LCA) to lithium ion batteries in the automotive sector. The aim of this study is to identify the crucial points of the analysis and the results achieved until now in this field. In the first part of the study, a selection of papers is reviewed. In the second part of the study, a methodological approach to LCA is adopted to make clear the strengths and weaknesses of this analysis method. The lack of primary data is a crucial concern. Even if the cradle-to-grave approach is the most chosen system boundary, further scientific contribution to the life cycle inventory phase is necessary. It is likely that the more the electric vehicle becomes widespread, the more data will be accessible. Many authors have not specified the chemistry of the used batteries (5% of the studies), the software tool used (30%) or the functional unit used (17%) and, consequently, their obtained results can be questionable. However, even with the aforementioned limitations, the performed review allows us to point out the potential of electric vehicles and lithium ion batteries to reduce the overall contribution of the transportation sector to GHG emissions.

Understanding Ridesourcing Mobility and the Future of Electrification: A Comparative Study in Beijing

ABSTRACT The development of mobile Internet, smartphones, and location-based services has enabled ridesourcing, which pools vehicles and drivers to provide on-demand travel services. As an alternative transportation option, ridesourcing has significant impacts on urban travel. However, the unique mobility pattern of ridesourcing and its impact on vehicle electrification have not been well studied. To address this gap, this paper presents a comparative, big-data-driven framework to characterize the ridesourcing mobility pattern, and evaluate the acceptance potential of electric vehicles for ridesourcing in comparison with other types of vehicle use. Multi-temporal resolution ridesourcing trips are extracted from raw GPS trajectories. The patterns of three urban travel (household, ridesourcing, and taxis) are extracted from GPS trajectories in Beijing, and compared. The electrification potentials of these types of travel under different charging levels are then evaluated. The results demonstrate that mobility patterns of household, ridesourcing, and taxi drivers are similar when a single trip is considered but differ significantly when total vehicle travel is considered. We show that potential acceptance of electric vehicles decreases significantly from household to ridesourcing and taxi vehicle use. These findings provide useful insights into of the role vehicle electrification can play in sustainability of urban personal transportation across a range of drivers.

Electric Vehicle Sales Catastrophe Averted (?)

The literature indicates catastrophic potential for electric vehicles around the year 2019. Amidst the predicted time, this research revisits the prequel analysis with state-of-the-art deterministic artificial intelligence methodologies to posit the potential for catastrophe in the upcoming year. The methodology has proven effective with motion mechanics, electrodynamics, and even financial analysis of sales date in the prequels, since the model commences with simple regression for mathematical model formulation asserting the certainty equivalence principle, followed by derivative modeling and eventually catastrophe analysis of the derivative models. The prequel analysis paradigms are retained in this sequel utilizing both monthly and cumulative sales data in simple least squares algorithms for predictive curve fitting to establish context and help correctly model the mathematical degree of the data. Extrapolation by forward time-propagation established predictions for models of various mathematical degrees (again merely for context). Next, catastrophe analysis (of the derivative form) revealed stable and unstable equilibrium points and then parametric variation was induced to evaluate the resulting behavior of the derivative models, highlighting the importance of the coefficient of the second order term (the acceleration or change of rate of sales as a forcing function). While the forcing function typically embodies both gasoline prices and vehicle charging proliferation, the relative stability of gas prices together with factors such as vehicle-to-grid elevate charging-station proliferation as the primary forcing function of slow-dynamics in catastrophe analysis. This brief manuscript revisits the prequel research to test the validity of those conclusions and with the benefit of the passage of time, reveal how well the mathematical modeling predicted real behavior. The main finding is the predicted potential catastrophe is less likely to occur and recommendations are made to insure catastrophe is averted.