Deployment Of Electric Vehicles Research Articles

Autonomous and Electric Vehicles in Urban Living Labs: Smart Mobility Strategies for the Future

The deployment of electric, connected, and autonomous vehicles on public roads presents a significant challenge that can be addressed through previously established frameworks developed globally for implementing these technologies as part of an urban living lab (ULL). This systematic review, based on records from four distinct databases, focuses on projects that have conducted deployments of self-driving technologies in streets within urban environments. The review describes relevant information about various initiatives, including a classification of the stages of development reached according to the urban area covered, safety considerations, and lessons learned for optimal deployment. On-board sensing technology, digital infrastructure, and energy and communication systems emerge as the essential components of a ULL with autonomous vehicles (AVs). A crucial goal for smart cities is ensuring the scalability of large-scale deployments of such ULLs for safe, clean, and future mobility experimentation. This can only be achieved through effective coordination among academia, government, industry, and society to guarantee the successful integration of multiple projects in a unique environment.

Critical Success Factors for Building Resilience in Circular Supply Chains of Electric Vehicle Batteries: Evidence from an Emerging Country

Given the global expansion of electric vehicles (EVs), decision makers in developing and emerging countries must address important challenges across EV battery supply chains (EVBSCs) toward circularity. For example, batteries usually make up about 40% of an EVs’ value, and the race to achieve net zero emissions will further underscore the critical need for vital minerals and metals, such as lithium, cobalt, and graphite, necessary to make batteries. Stakeholders routinely question the resilience of circular (C) EVBSCs worldwide, from mining valuable materials to manufacturing the batteries necessary to support the widespread deployment of EVs. Identifying and investigating critical success factors (CSFs) of any system is a necessary step in achieving its targets. Little research, however, has been performed to investigate the CSFs for building resilience in EVBSCs, particularly those focused on building a circular supply chain. The goal of this research is, therefore, to systematically scrutinize the CSFs of resilient C-EVBSCs in Türkiye. To this end, a decision framework applying inter-valued neutrosophic ISM-MICMAC is proposed. Based on expert opinions, an application of the decision framework finds that effective government policies, directives, and incentives and well-established dynamic capabilities, are key driving CSFs to building resilience in a C-EVBSC.

Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome.

Electrical vehicles impact on unbalanced distribution networks considering different loads and energy prices

The expansion of distributed generation and erratic loads create many challenges for electricity distribution networks (DN), like grid congestion and load unbalance. Technological advances in recent years have made the use of electric vehicles (EVs) more economical and justified for network connection. The use of smart EVs in the network scale, in addition to the peak shedding, if it is used in a single phase, it can also lead to the improvement of load unbalance. This paper proposes an energy and cost management model for the appropriate and distributed deployment of single phase EVs in distribution networks to provide network support for a DN including renewable resources. By connecting or disconnecting single-phase smart EVs to the DN, unbalanced loads in this network will be balanced as much as possible. To evaluate the proposed model, a network of 13 buses has been used, and the calculations of these energy costs have been measured during 24 h by two types of household and industrial loads. These loads are unbalanced in the DN and single-phase EVs have the ability to improve the unbalanced load of the DN. Also, the energy price is variable during the day and depends on the peak load. From the sensitivity analysis and with the relative increase or decrease of load, load change effects on the cost of each DN per-unit power have been measured and compared with the presence or absence of single-phase smart EVs to obtain more practical results. According to this paper, the use of EVs, in addition to improving the load of the DN, greatly reduces the costs of DN.

ADDRESSING INFRASTRUCTURE GAPS IN MUMBAI’S 2-WHEELER EV CHARGING NETWORK: A FOCUS ON ACCESSIBILITY, USABILITY, AND USER FEEDBACK

Electric vehicles (EVs) are transforming transportation in Mumbai, with 2-wheeler EVs gaining significant traction among commuters. However, the insufficient availability and accessibility of charging infrastructure remain critical challenges. This study investigates the experiences of 2- wheeler EV users in Mumbai, focusing on the barriers they encounter when charging their vehicles. Given Mumbai’s dense traffic and urban complexities, limited charging points, long waiting times, and infrastructural constraints can hinder the user experience and discourage further adoption of EVs. The findings provide key insights into optimal charging station locations based on a comprehensive analysis of population density, traffic flow, and urban planning frameworks. Additionally, the study addresses the need for standardized charging protocols, advanced technologies, and user-friendly interfaces to enhance the usability of charging stations. The study emphasizes that addressing these challenges is crucial not only for the user experience but also for policymakers, urban planners, and stakeholders aiming to expand Mumbai’s EV infrastructure. Moreover, the environmental benefits of 2-wheeler EV adoption are examined, including reductions in energy consumption, carbon emissions, and overall environmental impact. By analyzing these factors, the research highlights the positive environmental outcomes of investing in robust charging infrastructure. Key challenges, such as high costs, feasibility concerns, and policy limitations, are also explored, offering a holistic understanding of the practical barriers to improving the EV charging network. The study concludes with actionable recommendations aimed at overcoming these challenges, fostering sustainable growth, and ensuring the efficient deployment of 2-wheeler EV charging infrastructure across Mumbai.

A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources

A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources

TAVA: Traceable anonymity-self-controllable V2X Authentication over dynamic multiple charging-service providers

The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users’ identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation.To this end, this paper proposes a Traceable Anonymity-self-controllable V2X Authentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users’ credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %.

Does the electric vehicle promotion policy drive Thailand's passenger transport towards environmental sustainability?

Electric vehicles (EVs) are considered an alternative to internal combustion engine vehicles (ICEVs) for promoting sustainable transport, with environmental benefits depending on many local factors. This study performed a comprehensive life cycle assessment of passenger transport in major urban areas of Thailand, encompassing on-road, metro trains, and inland water transport, to determine the benefits of EV deployment. The findings showed that the vehicle life cycle (manufacture, maintenance, and disposal) was a significant contributor to human health and ecosystem quality impacts when comparing across transport life cycle phases in both ICEVs and EVs. Among private vehicles, with the current electricity mix, EVs had impacts on human health and ecosystem quality comparable to small-size ICEVs. At the projected 2037 electricity mix, the EVs will have lower impacts on human health, ecosystem quality and resource scarcity in comparison with the ICEVs. In scenario analysis, when considering only on-site emissions, it was found that private EV promotion had an environmental cost by about 16–43 % lower than the business-as-usual (BAU) scenario, depending on the rate of EV adoption, while shifting to public electric buses or metro trains had a lower environmental cost by 21 % and 23 % compared to BAU, respectively. If assessing the full transport life cycle, shifting to public electric buses or metro trains had a lower environmental cost (22–23 %) than the promotion of EVs with current electricity mix (14 %). The effectiveness of environmental impact mitigation through deployment of private EVs might increase to a comparable level with public electric bus or metro train shifts when Thailand achieves the electricity mix target with 34 % of renewable energy in 2037.

Integration and Optimization of Multisource Electric Vehicles: A Critical Review of Hybrid Energy Systems, Topologies, and Control Algorithms

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICEs), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESSs) present a viable current solution to these issues. This review thoroughly explores the state of the art in the emerging field of multisource EVs that utilize HESSs, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

An Investigation of factors Influencing electric vehicles charging Needs: Machine learning approach

Decarbonization of the world is greatly contributed to by the recent technological advancements that have fostered the development of electric vehicles (EVs). The EVs relieve transportation dependence on natural fossil fuels as an energy source. More than 50 % of the petroleum products produced worldwide are estimated to be used in the transportation sector, accounting for more than 90 % of all transportation energy sources. Consequently, studies estimate thatthe transportation sector produces about 22 % of global carbon dioxide emissions, posingsignificant environmental issues. Thus,using EVs, particularly on road transport, is expected to reduce environmental pollution. To accelerate EV development and deployment, governments worldwide invest in EV development through various initiatives to make them more affordable. This research aims to investigate the changing needs of EV users to establish factors to be considered in the selection of charging demands using machine learning, usingan extreme gradient boosting model. The model reached high accuracy, with an R2-Score of 0.964 to 1.000 across all predicted needs. The model performance is greatly affected by age, median income, education, and car ownership. High values of people with high income, high education, and age between 35–54 years show a positive contribution to the model’s performance, contrary to those with 65+, low income, and low education attainment. The outcomes of this research document factors that influence EV charging needs; therefore, it provides a basis for decision-makers and all stakeholders to decide where to locate EV charging stations for usability, efficiency, sustainability, and social welfare.

Development, characterization and validation of a novel physics-informed equivalent circuit model for silicon–graphite battery cells

The widespread deployment of electric vehicles (EVs), as well as the growing requirements both from manufacturers and consumers, necessitate an increase in energy density with regard to current lithium-ion batteries (LIBs). In this regard, one of the most promising alternatives is the addition of silicon to conventional graphite anodes, thus giving rise to energy-dense LIBs. However, this entails a significant increment in modeling, parameterization and computational complexities due to the interactions between both negative electrode materials. In this article, we present a novel physics-informed equivalent circuit model for cells with blended electrodes, which is able to provide accurate results with respect to a state-of-the-art electrochemical model, not only for the output voltage (≤25 mV RMS), but also internal states, namely active material particle surface concentrations (≤1.2 %) and current distribution (≤2.6 %) at a much lower computational cost. Furthermore, this formulation also allows for a notable simplification of model parameterization. Hence, we describe thorough static and dynamic parameterization processes from half-cell experimental data and impedance measurements. Finally, we validate the proposed model in constant-current as well as dynamic operation, highlighting the influence that the choice of hysteresis model has on the latter. We understand that the presented model is an interesting alternative for the modeling of cells with blended electrodes, and is also suitable for its implementation in Battery Management Systems (BMSs) in EVs.

Role of energy efficiency in energy transition: A decomposition analysis of energy use

The use of energy-efficient technologies tends to reduce the overall energy use of a country and foster the energy transition pathways. However, a change in energy can occur either due to a change in activity effect, intensity effect, or structural effect. The objective of the study is firstly to examine and measure the magnitude of change in energy use and identify the factor responsible for the change in energy use in the selected sectors. Secondly, the paper aims to analyze the impact of the Norwegian economy on the three effects of energy use. The study contributes significantly to identifying the sector that experiences a reduction in energy use due to energy efficiency and examining the impact of the economy on energy use. The overall energy use between 1990 and 2017 is decomposed into three different effects for selected energy-intensive sectors by deploying the ‘Logarithmic Mean Divisia Index’ (LMDI) method. Further, the impact of the Norwegian economy is examined on the three effects of energy use. It is observed that in each Phase, the key driver for change in energy use in all three Phases is the transport sector. Post-recession, the energy use in the transport sector was due to structural effects. Consumer behavior and limitations of sources of finance are the challenges for the deployment of electric vehicles even after technological breakthroughs in energy efficiency. Finally, the policies to enhance energy efficiency in energy-intensive sectors, such as the transport, and services sector must be undertaken to efficiently visualize energy efficiency-driven energy transition.

Climate impacts of critical mineral supply chain bottlenecks for electric vehicle deployment

New tailpipe emissions standards aim to increase electric vehicle (EV) sales in the United States. Here, we analyze the associated critical mineral supply chain constraints and enumerate the climate consequences of these constraints. Our work yields five findings. First, the proposed standard necessitates replacing at least 10.21 million new internal combustion engine vehicles with EVs between 2027 and 2032. Second, based on economically viable and geologically available mineral reserves, manufacturing sufficient EVs is plausible and reduces up to 457.3 million tons of CO2e. Third, mineral production capacities in the United States and amongst allies support the deployment of 5.09 million vehicles between 2027 and 2032, well short of compliance target. Fourth, this shortfall produces at least 59.54 million tons of CO2e in lost lifecycle emissions benefits. Fifth, limited production of battery-grade graphite and cobalt may represent particularly profound constraints. Pathways that afford comparable emission reductions are subsequently explored.

Expert Evaluation of the Significance of Criteria for Electric Vehicle Deployment: A Case Study of Lithuania

This study presents the hierarchical structure of 50 sub-criteria divided into 7 main criteria for the assessment of electric vehicle (EV) deployment. Two options, Average Rank Transformations and Analytic Hierarchy Process methods, were applied in determining the local weights of the sub-criteria. The sufficient compatibility of expert opinions was accomplished using the averages of the ranks of the main criteria and sub-criteria as the result of solving the problem. The averages of the local weights were calculated employing three Multiple Criteria Decision-Making methods that increased the reliability of the research results. Based on this, the global weights and priorities of the sub-criteria were evaluated. The experts suppose that EV deployment at the national level is mainly affected by the higher cost of manufacturing and purchasing EVs, the application of financial incentives for purchasing EVs, the lack of exhausted gasses, the installation of fast charging points, and the absence of infrastructure in the five largest cities nationwide. The obtained results demonstrate that out of 50 sub-criteria, the cumulative global weight of the 10 most important sub-criteria (mainly based in economics) amounts to more than 35%, whereas that of the 22 most important sub-criteria have a weight above the average (0.2), reaching approximately 65%. The findings can be put into practice by state decision makers of EV deployment.

Comparative harmonic elimination techniques for supraharmonic reduction in microgrid

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

The rising demand for electric vehicle (EV) charging is spurring their increased integration into microgrids. With significant advancements, EVs have become widely adopted and integrated into various settings for charging/discharging. EVs integrated with the microgrids possess the capability to serve as variable loads and the various energy suppliers present it as a dual opportunity. However, a primary challenge in EV deployment lies in efficiently managing charging stations (CSs) to minimize waiting times for users and reduce charging costs for EV owners. In addressing these challenges require consideration of dynamic pricing mechanisms and the diverse characteristics of EVs to achieve optimal scheduling. A novel approach that combines dynamic pricing strategies with optimized scheduling for effective EV charging operations using multi-objective Jaya algorithm. To evaluate its performance, we conducted a numerical experiment using real-time data and the Nissan Leaf model EV. The results demonstrate that our multi-objective Jaya-based approach outperforms existing methods by achieving a remarkable cost saving rate of 16.013% and an average profit of ₹ 243.6331 per kilowatt-hour with a network convergence time of 112 s. Also, our proposed algorithm provides a correlation between minimized EV charging costs and maximized EV aggregator profits. These findings validate the effectiveness and practical applicability of our proposed EV scheduling algorithm in real-world scenarios.

Toward just and equitable mobility: Socioeconomic and perceptual barriers for electric vehicles and charging infrastructure in the United States

Based on a large-scale public survey, we identify and quantify the significance of key factors associated with the deployment of electric vehicles and charging infrastructure. Our results indicate that individual characteristics, such as income, age, region, and single vs. multi-family housing type can significantly affect electric vehicle purchase preferences, especially those concerning overnight charging and perceptions of benefits and barriers. Moreover, our results challenge earlier findings in the literature by showing how certain elements, such as expected electric driving range, certain travel behaviors (e.g., driving distance, destination types), the most common perceived benefits (e.g., cleaner air) or barriers (e.g., reliability concerns), and preferred location for public charging seem to not vary much or at all with the socioeconomic, demographic, and geographical variables examined in this study. We conclude with the implications for policies to advance equitable vehicle electrification. Our findings underscore the importance of lower-cost models of electric vehicles, home and public charging access, charging infrastructure planning, more integrated analysis of interlinked housing and transportation needs and solutions, the availability of alternative transportation modes, and the potential role of gas stations for electric vehicles. We encourage others to build on these results and have shared our complete survey instrument as an added contribution.

Shaping the future: Improvements in the EV charging Infrastructure: A comparative analysis of Germany and India

The shift towards sustainable transportation has accelerated the deployment of electric vehicles (EVs), demanding advancements in EV charging infrastructure. [1] This thesis, "Shaping the Future: Improvements in EV Charging Infrastructure," explores the critical role of enhancing charging networks to boost EV adoption and usage. It delves into the status, challenges, technological innovations, regulatory frameworks, and evaluates the collective impact on future mobility. In-depth investigations reveal the challenges facing the existing EV charging infrastructure, such as grid capacity limitations, interoperability issues, and regulatory complexities. [2] It also compares global strategies from countries like Germany and India to evaluate the impact of government policies, subsidies, and incentives on infrastructure deployment. The research identifies emerging trends, including wireless charging, bidirectional capabilities, and smart, connected charging stations, emphasizing their potential to revolutionize the EV charging experience. It assesses the economic and environmental sustainability of integrating renewable energy sources into charging networks, as well as the scalability and adaptability of the infrastructure to evolving demands. [3] By synthesizing these findings, the thesis provides insights into future directions for the development of EV charging infrastructure. It offers recommendations for overcoming current barriers, fostering technological innovation, and harmonizing regulations to create a seamless, sustainable, and universally accessible EV charging environment.

The OPEVA Manifest: OPtimisation of Electrical Vehicle Autonomy, a Research and Innovation project

Electromobility is a critical component of Europe’s strategy to create a more sustainable society and support the European Green Transition while enhancing quality of life. Electrification also plays an important role in securing Europe’s position in the growing market of electric and autonomous vehicles (EAV). The EU-funded OPEVA project aims to take a big step towards deployment of sustainable electric vehicles by means of optimising their support in an ecosystem. Specifically, the project focuses on analysing and designing optimisation architecture, reducing battery charging time, and developing infrastructure, as well as reporting on the driver-oriented human factors. Overall, OPEVA’s goal is to enhance EAV market penetration and adoption, making them more accessible and convenient. The aim of this paper is to inform the European automotive, transportation, energy and mobility community be presenting the OPEVA manifestation, and the overall solution strategy solidified through the progress throughout the first year of the project.

Prioritizing Target Motorcycle Owners to Promote Electric Vehicles Among Motorcycle Taxi Drivers in Bangkok

Governments in many countries have announced their own emission targets and launched several measures to mitigate transport-sourced air pollution, including policies to promote the use of electric vehicles (EVs). However, electrifying a fleet of vehicles requires high investment and it is difficult to fully implement all at once. Therefore, this research aims to develop a framework to help prioritize locations to promote EVs using motorcycle taxis in Bangkok as a case study. The surveyed data was collected from 406 motorcycle taxi drivers around Bangkok. The proposed framework is based on two aspects. One is the impact on CO2 emission reduction while another is the difficulty of EV deployment. The study's findings yield a potential matrix for EV deployment, which classifies locations into four priority groups. The study concludes that prioritizing locations with high CO2 reduction impact and low deployment difficulty is crucial for efficient EV promotion. Subsequently, recommendations are offered to assist authorities and automobile firms in effectively allocating resources for EV promotion. Policy recommendations highlight the significance of targeted interventions and enhancing public awareness to facilitate the widespread adoption of electric motorcycle. Implications from this study will help the authorities and automobile firms to prioritize areas and allocate budgets for promoting EVs efficiently.