Deployment Of Electric Vehicles Research Articles

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

Charging forward: deploying EV infrastructure for Uber and Lyft in California

With recent policies such as the Clean Miles Standard in California and Lyft’s announcement to reach 100% electric vehicles (EVs) by 2030, the electrification of vehicles on ride-hailing platforms is inevitable. The impacts of this transition are not well-studied. This work attempts to examine the infrastructure deployment necessary to meet demand from electric vehicles being driven on Uber and Lyft platforms using empirical trip data from the two services. We develop the Widespread Infrastructure for Ride-hail EV Deployment model to examine a set of case studies for charger installation in San Diego, Los Angeles, and the San Francisco Bay Area. We also conduct a set of sensitivity scenarios to measure the tradeoff between explicit costs of infrastructure versus weighting factors for valuing the time for drivers to travel to a charger (from where they are providing rides) and valuing the rate of charging (to minimize the amount of time that drivers have to wait to charge their vehicle). There are several notable findings from our study: (1) DC fast charging infrastructure is the dominant charger type necessary to meet ride-hailing demand, (2) shifting to overnight charging behavior that places less emphasis on daytime public charging can significantly reduce costs, and (3) the necessary ratio of chargers is approximately 10 times higher for EVs in Uber and Lyft compared to chargers for the general EV owning public.

Can planned control measures for private passenger vehicles achieve China's carbon peak goal and mitigate the environmental impact?

There is a lack of consensus as to whether planned control measures for vehicular emissions can achieve China's carbon peak goal and mitigate the environmental impact of private passenger vehicles. This paper studies the environmental benefits of China's planned control measures by using a dynamic fleet-based life cycle assessment (LCA) method and “what-if” scenario analysis at a full national scale in a life-cycle systematic perspective. The results showed that burden-shifting may occur for electric vehicle (EV) deployment and demand-side interventions. China cannot achieve carbon peak goal before 2030 with the current planned control measures, unless the internal combustion engine vehicle (ICEV) fleet or the power grid is improved significantly. Large-scale deployment of EVs in the short term, with much improved ICEVs and a less clean power grid, is not conducive to peak fleet carbon emissions. Therefore, improving the ICEV fleet should be a priority for policy-making in the short term.

Current Trends in Electric Vehicle Charging Infrastructure; Opportunities and Challenges in Wireless Charging Integration

Nowadays, the imperative need for the reduction of Greenhouse Gas (GHG) emissions leads to the wider adoption of environmentally friendly transportation means. As a result, various policies underpinning the Electric Vehicle (EV) deployment are legislated globally, and several technical advances contributing to the electrification of the transportation sector are pursued. In this paper, a comprehensive overview of the current status of the infrastructure utilized for the realization of both conductive and contactless (wireless) charging of an EV battery is conducted. Furthermore, the issue of EV integration in conventional distribution networks, as well as in future power system architectures, is discussed in detail. Particular focus is given to wireless (i.e., inductive) charging. A detailed presentation of the respective standards and charging levels, as well as the magnetic couplers and the compensation network configurations, is carried out. Moreover, innovative concepts such as dynamic and quasi-dynamic wireless charging, as well as future challenges and opportunities, are presented and discussed. Finally, smart control and communication techniques applicable to EV charging are presented in the context of the future Internet of Energy (IoE) concept.

Stacking regression technology with event profile for electric vehicle fast charging behavior prediction

Large-scale deployment of electric vehicles (EVs) poses a huge challenge to the operation of the distribution network. As a possible mobile energy carrier, the interaction between EVs and distribution networks can provide some opportunities for power operation. Where to charge and how to charge have become an important research topic in EV charging scheduling. Previous studies mainly focused on slow-charging behavior analysis rather than fast-charging behavior. Here, we provide an in-depth understanding of EV user fast-charging behavior in public stations based on more than 220,000 real-world charging records with the Variational-Bayesian Gaussian-mixture model. Characteristics related to charging energy and charging duration are mainly considered in the cluster model, especially dwelling duration after charging is taken into account to better support the decision of charging recommendation strategy and charging power allocation. Inspired by the future application scenario of the charging behavior cluster of previous studies, we propose a charging behavior prediction framework considering behavior catalogues with stacking regression technology. The results show that the proposed framework improves the prediction accuracy of charging behavior and can effectively evaluate the priority of charging behavior.

Organosulfur Materials for Rechargeable Batteries: Structure, Mechanism, and Application.

Lithium-ion batteries have received significant attention over the last decades due to the wide application of portable electronics and increasing deployment of electric vehicles. In order to further enhance the performance of the batteries and overcome the capacity limitations of inorganic electrode materials, it is imperative to explore new cathode and functional materials for rechargeable lithium batteries. Organosulfur materials containing sulfur-sulfur bonds as a kind of promising organic electrode materials have the advantages of high capacities, abundant resources, tunable structures, and environmental benignity. In addition, organosulfur materials have been widely used in almost every aspect of rechargeable batteries because of their multiple functionalities. This review aims to provide a comprehensive overview on the development of organosulfur materials including the synthesis and application as cathode materials, electrolyte additives, electrolytes, binders, active materials in lithium redox flow batteries, and other metal battery systems. We also give an in-depth analysis of structure-property-performance relationship of organosulfur materials, and guidance for the future development of organosulfur materials for next generation rechargeable lithium batteries and beyond.

Analysis of Charging Infrastructure for Private, Battery Electric Passenger Cars: Optimizing Spatial Distribution Using a Genetic Algorithm

To enable the deployment of battery electric vehicles (BEVs) as passenger cars in the private transport sector, suitable charging infrastructure is crucial. In this paper, a methodology for the efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a 100% market penetration of BEVs of (around 1.3 million vehicles) in Berlin, Germany. The goal of the evaluated methodology is the development of various charging infrastructure scenarios—including public and private charging—which are suitable to cover the entire charging demand. Therefore, these scenarios are investigated in detail with a focus on the number of public charging points, their spatial distributions, the available charging power, and the necessary capital costs. For the creation of these charging infrastructure scenarios, a placement model is developed. As input, it uses the data of a multi-agent transport simulation (MATSim) scenario of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital costs of the charging points and the mean detour car drivers must undertake are used as the optimization criteria. Using these criteria, we expect to generate cost-efficient infrastructure solutions that provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found, and suitable solutions can be selected by subsequently using other criteria. Besides the generated charging scenarios for Berlin, the main goal of this paper is to provide a valid methodology, which is able to use the output data of an agent-based, microscopic transport simulation of an arbitrary city or area (or even real driving data) and calculate different suitable charging infrastructure scenarios regarding the different optimization criteria. This paper shows a possible application of this method and provides suggestions to improve the significance of the results in future works. The optimized charging infrastructure solutions for the Berlin scenario show capital costs of between EUR 624 and 2950 million. Users must cover an additional mean detour of 254 m to 590 m per charging process to reach an available charging point. According to the results, a suitable ratio between the charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50 kW) of less than ten percent seems to be sufficient if it is situated at the main traffic routes and highly frequented places.

Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030

The energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation. We include both in-use and end-of-vehicle-life use phases and find a technical capacity of 32–62 terawatt-hours by 2050. Low participation rates of 12%–43% are needed to provide short-term grid storage demand globally. Participation rates fall below 10% if half of EV batteries at end-of-vehicle-life are used as stationary storage. Short-term grid storage demand could be met as early as 2030 across most regions. Our estimates are generally conservative and offer a lower bound of future opportunities.

Planning for the majorities: are the charging needs and preferences of electric vehicle early adopters similar to those of mainstream consumers?

Abstract The mass deployment of electric vehicles (EVs) may bring significant challenges to the electricity sector. However, many of these challenges can be converted into opportunities depending on how and when consumers decide to charge their vehicles. While there are currently multiple efforts worldwide investigating EV charging behaviour, these efforts measure the behaviour of EV early adopters and may not represent the actual behaviour of the mainstream consumer. The current study uses data from a survey with near a thousand Australian consumers to shed light on the potential similarities and differences between the charging needs and preferences of EV early adopters and mainstream consumers. We find that consumer groups vary in terms of charging needs, perceived access to residential charging, and acceptance of direct charging control and management by suppliers. Our conclusions point to (i) the need for campaigns that increase the awareness and understanding of residential EV charging by mainstream consumers; (ii) the significant interest across all consumer groups in free workplace charging, which could together with residential demand management strategies leverage the use of solar energy for charging; and (iii) the need for utility plans and management strategies that enhance the mainstream consumer sense of control over charging together with their perceived monetary savings.

Optimal allocation of energy sources in hydrogen production for sustainable deployment of electric vehicles

We analyze the use of hydrogen as a fuel for the automotive industry with the aim of decarbonizing the economy. Hydrogen is a suitable option for avoiding pollutant gas emissions, developing environmentally friendly technologies, replacing fossil fuels with clean, renewable energies, and complying with the Paris Agreement and Glasgow resolutions. In this sense, renewable energies such as wind, solar, photovoltaic, geothermal, biomass, etc. can be used to produce the necessary hydrogen to power vehicles. In this way, the entire process from hydrogen production to its consumption as fuel will be 100% clean. If we are to meet future energy demands, it is necessary to forecast the amount of hydrogen needed, taking into account the facilities currently available and new ones that will be required for its generation, storage, and distribution.This paper presents a process for optimizing hydrogen production for the automotive industry that considers the amount of hydrogen needed, the type of facilities from which it will be produced, how the different sources of production are to be combined to achieve a competitive product, and the potential environmental impacts of each energy source. It can serve as a frame of reference for the various actors in the hydropower and automotive industries so that more efficient designs can be planned for the gradual introduction of hydrogen fuel cell vehicles (HFCVs).The methodology implemented in this paper sets an optimization problem for minimizing energy production costs and reducing environmental impacts according to the source of energy production. The EU framework with respect to the decarbonization of the economy, the percentages of the different types of energy sources used, and the non-polluting vehicle fleet in the automotive sector will be considered.

Assessing policy interventions to stimulate the transition of electric vehicle technology in the European Union

The European Union (EU) is taking steps to gradually reduce its reliance on fossil fuels, as well as to decarbonize the entire energy and automotive systems, with the goal of reaching carbon neutrality by 2050. As such, road transportation plays a fundamental role in this process. Internal combustion engine (ICE) vehicles are to be slowly decommissioned as other powertrain systems arise for multiple reasons, from greenhouse gas (GHG) emissions to urban air quality. To change both the technological and consumer landscapes, governments must act through policymaking intervention. Electric vehicles (EVs) provide a significant opportunity to address this issue, and as such, the current work aims at assessing national-level policy intervention within the EU regarding the EV transition. This study employs the ELECTRE (ELimination Et Choix Traduisant la REalité - ELimination and Choice Expressing the REality) TRI-nC method to classify 27 EU Member States (MSs) regarding their governance in terms of EV technology promotion. Overall, financial incentives still have a big effect on EV deployment, since those countries with greater concern on this topic were generally better classified than the rest. Finally, charging infrastructures also play a critical role, either making or breaking the deployment of EVs, leading to the worst classification of MSs with very few charging points per 100 thousand urban inhabitants.

Exploration of the nexus between solar potential and electric vehicle uptake: A case study of Auckland, New Zealand

Joint deployment of solar photovoltaic (PV) systems and electric vehicles (EVs) offers a sustainable option for decarbonisation. However, the possible influence of solar PV on EV uptake within a spatial-temporal framework is often overlooked in the literature. Based on a unique dataset at a detailed spatial level in Auckland, New Zealand, this study explores the potential complementarity of EVs and solar PV using spatial negative binomial regression models. We find evidence of complementarity between solar PV and EV uptakes. Results show that a one-unit increase in the installation of solar PV is associated with an increase in EV uptake count by a factor of 1.021. Households investing in solar PV also have the potential to accelerate EV uptake. Moreover, we find that a one-unit increase in EV charger installation in neighbouring areas is associated with an increase in EV uptake by a factor of 41. Charging infrastructure in neighbouring areas has a positive impact on subsequent EV adoption. Additionally, EV uptake is increased by 1.047–1.049 in response to a one-unit increase in early EV adoption, indicating that the early-adopter phase also positively impacts subsequent EV uptake. Our findings provide important insights for policy aimed at increasing EV uptake.

A Comprehensive Review of Electric Vehicles in Energy Systems: Integration with Renewable Energy Sources, Charging Levels, Different Types, and Standards

Due to the rapid expansion of electric vehicles (EVs), they are expected to be one of the main contributors to transportation. The increasing use of fossil fuels as one of the most available energy sources has led to the emission of greenhouse gases, which will play a vital role in achieving a sustainable transportation system. Developed and developing countries have long-term plans and policies to use EVs instead of internal combustion vehicles and to use renewable energy to generate electricity, which increases the number of charging stations. Recently, to meet the charging demand for EVs, the main focus of researchers has been on smart charging solutions. In addition, maintaining power quality and peak demand for grids has become very difficult due to the widespread deployment of EVs as personal and commercial vehicles. This paper provides information on EV charging control that can be used to improve the design and implementation of charging station infrastructure. An in-depth analysis of EV types, global charging standards, and the architectures of AC-DC and DC-DC converters are covered in this review article. In addition, investigating the role of EV collectors, as well as EV penetration, in electric energy systems to facilitate the integration of electric energy systems with renewable energy sources is one of the main goals of this paper.

Study on development of EV charging services coupled with power system conditions using IoT technology

Deployment of electric vehicles (EVs) has been accelerated in many countries. However, for further deployment of EVs and their contribution to electrical power systems, various new services need to be implemented related to EVs and their charging, called “Place of Use” (PoU) services. The combined menu of the charging services has been proposed which bundles EV charging fees with home electricity bills. Settlement will be needed for such combined menu. This paper proposes the combined menu of EV charging with settlement of electricity. Technical feasibility of the settlement has been tested by EV charging testbed with the IoT-HUB technology. IoT-HUB is the virtual infrastructure which interconnects various connected devices and application by using “drivers” for each device. This paper also proposes a forecast method of EV charging demand for the settlement after the charging starts. The proposed method has reduced the forecast error of total charged energy compared to the simple method, but some of the forecast error has remained because of the variability of the charging time at the final step of state of charge.

Social Acceptance and Preference of EV Users—A Review

Electric vehicles (EVs) are drawing increasing attention, given the volatile fuel prices and impending reduction in fuel supplies. EVs are also preferred in the automotive industry as they reduce the environmental impact and fuel consumption while achieving a higher efficiency when compared to internal combustion engine vehicles (ICEVs). Although most of energy and environmental policies worldwide have set targets with the goal to shift from classic fossil fuel driven vehicles to electrified transport, the share of EVs is still rather low. The complexity of changing the human perception of transportation goes beyond technical and economic aspects and very few research activities managed to capture the additional factors. This paper presents a comprehensive review of studies on consumer preferences for EV, aiming to better inform policy-makers and give direction to further research. In addition, this paper discusses the main obstacles that limit the social acceptance in adopting EVs such as driving obstacles, charging obstacles and willingness-to-pay from consumers. Furthermore, mitigation strategies are outlined as the recommendation to promote a better EV deployment.

Driving a sustainable road transportation transformation

Driving a sustainable road transportation transformation

Full accessibility and user-friendly smart mobility services across the European charging infrastructure

Although most electric vehicle (EV) charging still takes place at home or work, widespread, open-access public charging infrastructure is essential to support EV drivers beyond early adopters. USER-CHI works to unlock the massive potential of electromobility in Europe by integrating innovative charging technologies with a holistic perspective, putting the user at the centre and empowering it, whilst at the same time makes financially attractive for the relevant stakeholders. To fill the existing gaps and to achieve its strategic challenges and objectives, USER-CHI*⁎https://www.userchi.eu/ combines technical, regulatory and business elements and produces a set of solutions that cover all aspects of a massive deployment of electric vehicles, from the optimal design and planning of innovative charging infrastructure to specific solutions to to allow interoperability and e-roaming in Europe (Martín et al., 2021).

Atomic-level insights into the first cycle irreversible capacity loss of Ni-rich layered cathodes for Li-ion batteries

Our study identifies that the first cycle IRC loss is strongly related to the irreversibility of the Ni charge state, which limits the capacity and energy density.

Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles

Europe strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only if electric vehicles dominate the transportation sector. Paving the way for electric vehicle deployment on roads is subject to the provision of electric-vehicle-charging stations on the roads such that sufficiently good driving experience without any obstacles can be achieved. To address this timely societal challenge, we proposed a novel methodology by using the well-known facility-location-allocation methodology named set-covering location models with statistical machine learning and developed it for the problem settings of identifying electric-vehicle-charging station locations. Statistical machine learning was employed in the proposed model to more precisely identify and determine feasible coverage sets. We demonstrated the efficiency of the proposed model for the Capital Region of Denmark, where the green transition is part of the political agenda and is of severe societal concern, by using the newly collected main road transportation dataset.

Ambitious EV policy expedites the e-waste and socio-environmental impacts in India

There is limited knowledge on the potential impacts of electric vehicle (EV) deployment in developing countries to date. India is a rapidly industrializing country with a national policy to promote EVs, but does not have the waste management infrastructure to process battery waste. Through a model analysis for India, we projected that the sale of two-wheeler EVs will peak in 2030 at around 20 million units while the sale of four-wheeler EVs will steadily increase to 10 million units in 2050. Meanwhile, the volume of lead-acid battery waste is projected to peak in 2027, and lithium-ion battery waste from two-wheelers and four-wheelers will exceed 500 kilotons in 2044 and 1000 kilotons in 2050, respectively. Given these projections, it is imperative to create a safe environment for workers and surrounding communities, an extended producer responsibility (EPR) system and a national mechanism to track waste crimes related to batteries in the future.