Deployment Of Electric Vehicles Research Articles

Why does electric vehicle deployment vary so much within a nation? Comparing Chinese provinces by policy, economics, and socio-demographics

Despite the global growth of the electric vehicle (EV) market, disparities in EV distribution within countries remain apparent. To shed light on the issue, this study investigates the spatial dimension of China's EV sector. Using five EV-related indicators and the c-means clustering method (FCM), this study conducts a cluster analysis to examine the spatial distribution of the EV sector across 25 provinces in Mainland China. By selecting one representative province from each cluster, a comparative case study was subsequently performed to uncover the factors contributing to variations in EV development among provinces. Our findings reveal the presence of regional imbalances and identify four distinct clusters: producers (five provinces), retailers (two provinces), green transport (seven provinces), and laggards (eleven provinces). The study reveals that provincial economic and socio-demographic factors, along with policy mixes, play significant roles in shaping this heterogeneity. A robust economy drives EV demand and support the establishment of charging infrastructure and new technologies. Additionally, a thriving automotive sector can facilitate the EV development. However, protective tendencies exhibited by provincial governments towards existing production hinder the adoption of new technologies and impede EV sector growth. The proactive approach of governments and the integration of policies into the local context are vital for fostering the growth of the EV sector.

Multi-Criteria Approach for Identification and Ranking of Key Interventions for Seamless Adoption of Electric Vehicle Charging Infrastructure

Development of electric vehicle charging infrastructure needs a multi-sectoral approach with due consideration given to different associated stakeholders in the EV ecosystem. With the global transition towards electrification of the transportation sector, several countries have set ambitious goals of Electric Vehicle (EV) adoption. However, deployment of adequate EV charging infrastructure, which is one of the most critical factors for seamless adoption of EVs, is limited by various challenges, including technical, policy and regulatory issues. In order to mitigate the challenges, this paper presents a combined ranking of the key interventions. Critical analysis of all the identified interventions has been undertaken and the learnings have been utilized to frame a multi-criteria decision making (MCDM) process to identify the priority of roll-out of the interventions. The unified ranking of technical, and policy and regulatory interventions would enable rapid and widespread deployment of EV charging infrastructure.

Cross-sectoral analysis on the implication of ASEAN road transport electrification policies on energy security and climate

Road fleet electrification is one of the key strategies of the ASEAN region in decarbonizing the transportation sector. Electric vehicles (EVs) are seen to be more efficient compared to their conventional counterpart. Moreover, reduce pollution and emission which results in negative externalities like respiratory deceases and global warming. This study aims to perform a cross-sectoral analysis of the implication of ASEAN road transport electrification policies on energy security, climate, and societal welfare. Results show that ASEAN could reach 641 million passenger vehicles by 2050, and the implementation of ASEAN countries’ EV targets increases the penetration of technologies up to 9% penetration in both private cars and buses, 13% in motorcycles, and 4% in taxicabs. Significant petroleum savings can be expected with the deployment of e-vehicles but would increase end-user electricity demand. As the region could remain highly dependent on fossil-based power generation considering the historical trend in electricity dispatch, it was found that EV deployment could rather result in GHG being emitted than mitigated. However, the technology use could still result in significant pollutants reduction improving urban health and welfare. Thus, social cost savings could be expected with the implementation of ASEAN road transport electrification policies.

Are electric vehicles more efficient? A slacks-based data envelopment analysis for European road passenger transportation

The deployment of electric vehicles (EVs) is rapidly progressing worldwide, and energy efficiency is an important indicator for both the economy and the environment. It is necessary to verify whether EVs are more energy efficient than internal combustion engine (ICE) vehicles. However, studies have yet to evaluate the energy efficiency of EVs using the data envelopment analysis (DEA) approach. To fill this research gap, this paper evaluated the energy efficiency of EVs for road passenger transportation using a slacks-based measure (SBM) approach in six European countries from 2012 to 2018. EVs have been separated from the entire transport sector and compared to ICE vehicles. In the case of EVs that use electricity as fuel, the primary energy supply must be used as energy input to reflect conversion loss during power generation. The primary energy supply was calculated by applying the power generation efficiency of each country for use as an energy input to reflect conversion loss. EVs were more energy efficient than ICE vehicles in four out of six countries but less energy efficient in two countries. Based on the measured slack results, the potential energy savings were quantified, and the main factors in reducing energy efficiency were identified.

Electric Vehicle Charging Infrastructure in Croatia – First-Hand Experiences and Recommendations for Future Development

One of the most serious obstacles to massive deployment of electric vehicles is insufficient and cumbersome charging infrastructure. Both the number of charging points and their power capacity are often insufficient. On top of that, this new technology often suffers from many issues related to insufficient testing, immaturity and irregular handling.
 This paper summarizes the issues with the electric vehicle charging infrastructure and describes first-hand experiences with long-range electric vehicle trips originating from Zagreb, Croatia, during 2022. Issues with the charging points locations, power and availability are assessed. Based on these experiences, the paper provides some thoughts on the possible directions of the further development of the electric vehicle charging infrastructure in Croatia.

Assessing electric mobility and renewable energy synergy in a small New Caledonia Island community

ABSTRACT In this paper, we evaluate the synergy between variable renewable energy (VRE), electric mobility, and Vehicle to Grid (V2G) deployment for a small community transitioning to a low-carbon economy. We propose an integrated deterministic modeling approach that accurately describes electric mobility activity within the electric grid in the ETEM-SG long-term capacity expansion model. This approach allowed us to keep the entire approach in the linear programming domain. We then perform a scenario analysis on the Isle of Pines case. The results show that V2G is essential for electric vehicles to make a positive contribution to the electric system by providing a supply–demand balance service. The deployment of electric vehicles allows reaching a VRE penetration rate of up to 94%, at no additional cost compared to a solution without electric vehicles, and even with a global-cost reduction of up to 5.3% thanks to a decrease of the capacity of stationary battery packs needed for storage.

Hidden delays of climate mitigation benefits in the race for electric vehicle deployment

Although battery electric vehicles (BEVs) are climate-friendly alternatives to internal combustion engine vehicles (ICEVs), an important but often ignored fact is that the climate mitigation benefits of BEVs are usually delayed. The manufacture of BEVs is more carbon-intensive than that of ICEVs, leaving a greenhouse gas (GHG) debt to be paid back in the future use phase. Here we analyze millions of vehicle data from the Chinese market and show that the GHG break-even time (GBET) of China’s BEVs ranges from zero (i.e., the production year) to over 11 years, with an average of 4.5 years. 8% of China’s BEVs produced and sold between 2016 and 2018 cannot pay back their GHG debt within the eight-year battery warranty. We suggest enhancing the share of BEVs reaching the GBET by promoting the effective substitution of BEVs for ICEVs instead of the single-minded pursuit of speeding up the BEV deployment race.

A genuine V2V market mechanism aiming for maximum revenue of each EV owner based on non-cooperative game model

As the number of electric vehicles (EVs) surges, EVs are expected to supply auxiliary services for power grid through the vehicle-to-grid (V2G) operation. Specifically, vehicle-to-vehicle (V2V), as a beneficial supplement of V2G, has received increasing attention owing to the capability for flexible EVs deployment and profitable individual interaction. In this paper, a genuine V2V market mechanism aiming for maximum revenue of each EV owner is proposed to construct a distributed electricity market. It reveals the dominance of EV owners from the perspective of market and allows the trade among EV owners in local distribution grid with charging points such as charging stations and auto parks. To this end, the implementation of V2V market mechanism is analyzed in detail, including the electricity price, the process and the integration with the internet of smart charging points (ISCP), a cyber-physical architecture. Then, EVs’ energy dispatching is formulated via the non-cooperative game model, in which each EV owner anticipates maximizing individual revenue by choosing feasible strategies. The existence and uniqueness of Nash Equilibrium (NE) in a non-cooperative game is proved by means of variation inequality (VI) theory. Finally, the ISCP-based optimal algorithm is developed to solve the NE efficiently, thereby maximizing reward of each EV owner. Comprehensive case studies show that the proposed V2V market mechanism can implement distributed dealing among EV owners with reducing individual charging cost and regional load variance effectively. Specially, the costs of EV1, EV8 and EV9 decrease by 31%, 36% and 34%, respectively. And V2V charging demonstrates much lower variance (10.5209) than regular charging (14.1124). Additionally, the convergence of the algorithm is also demonstrated, reaching the NE in less than 30 iterations for each EV.

Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Since electric vehicle (ELV) deployment can contribute to overall renewable energy sources, exploration of the heterogeneous influence factors (HIFs) affecting the willingness to accept ELVs can assist in the realization of sustainable development goals, particularly universal access to affordable energy for all. In this research, we explored the HIFs that influence the willingness of individuals to accept ELVs (WAELV) within an integrated decision-making (IDM) framework. We established the IDM conceptual framework through the incorporation of HIFs, notably including the environmental and health benefits of ELVs, knowledge about innovation, and the benefits regarding the built environment and creating a comprehensive structure. We analyzed data gathered through questionnaires from urban and peri-urban areas of the Shandong province (China) by employing the partial least square structural equation modeling technique, which is an appropriate tool for analyzing data measured on a Likert scale. The key findings were as follows. Firstly, the capital cost of ELVs was found to be a significant barrier to the WAELV of individuals. Secondly, among other factors, the societal aspect of ELVs and the environmental awareness aspect were drivers of the WAELV of individuals across all the data samples. However, benefits for the built environment, knowledge about innovation, and the environmental and health benefits of ELVs only positively drove the WAELV of individuals in the urban setting and for the overall sample. Thirdly, these three HIFs were identified as neutral factors in the peri-urban areas. Thus, a clear disparity was detected between the urban and peri-urban areas in terms of factors influencing the WAELV of individuals. Finally, the social aspect of ELVs was revealed as the strongest driver, while benefits for the built environment turned out to be the weakest factor. Based on these findings, some crucial policies are here extracted.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Do electric vehicle charger locations respond to the potential charging demands from multi-unit dwellings? Evidence from Los Angeles County

With the transportation sector being the largest contributor of greenhouse gas emissions in the United States, states are actively planning the deployment of plug-in electric vehicles (PEVs). Mass adoption of PEVs requires attracting potential buyers living in multi-unit dwellings (MUDs). Given the current low adoption rate of PEVs in MUDs and MUD residents’ reliance on public charging, this paper studies whether there is a positive correlation between the number of public Level 2 (L2) chargers and MUD density (measured by total square footage of MUD per capita) across census block groups (CBGs) in LA County. The results show that high MUD-density CBGs and low MUD-density CBGs do not differ much in terms of the number of chargers. The charger-to-PEV-ratio range for MUD residents derived in this paper is below the ideal charger-to-PEV-ratio range in the literature. A direct policy implication is that more charging infrastructure should be made available to MUD residents. This includes public L2 chargers near MUDs, which are discussed in this paper, and onsite MUD charging, which is still at an early stage.

Analysis of Deactivation of 18,650 Lithium-Ion Cells in CaCl2, Tap Water and Demineralized Water for Different Insertion Times.

The deployment of battery-powered electric vehicles in the market has created a naturally increasing need for the safe deactivation and recycling of batteries. Various deactivating methods for lithium-ion cells include electrical discharging or deactivation with liquids. Such methods are also useful for cases where the cell tabs are not accessible. In the literature analyses, different deactivation media are used, but none include the use of calcium chloride (CaCl2) salt. As compared to other media, the major advantage of this salt is that it can capture the highly reactive and hazardous molecules of Hydrofluoric acid. To analyse the actual performance of this salt in terms of practicability and safety, this experimental research aims to compare it against regular Tap Water and Demineralized Water. This will be accomplished by performing nail penetration tests on deactivated cells and comparing their residual energy against each other. Moreover, these three different media and respective cells are analysed after deactivation, i.e., based on conductivity measurements, cell mass, flame photometry, fluoride content, computer tomography and pH value. It was found that the cells deactivated in the CaCl2 solution did not show any signs of Fluoride ions, whereas cells deactivated in TW showed the emergence of Fluoride ions in the 10th week of the insertion. However, with the addition of CaCl2 in TW, the deactivation process > 48 h for TW declines to 0.5-2 h, which could be an optimal solution for real-world situations where deactivating cells at a high pace is essential.

Optimal Deployment of Electric Vehicles’ Fast-Charging Stations

As climate change has become a pressing concern, promoting electric vehicles’ (EVs) usage has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating charging stations in areas with an expanding charging infrastructure is crucial to the accessibility and future success of EVs. Nonetheless, suitable planning and deployment for EV fast-charging stations is one of the most critical determinants for large-scale EV adoption. Installing charging stations in existing fuel/gas stations in the city may be an effective way to persuade people to adopt EVs. In this paper, we aim to optimally locate a fast-charging station in an existing gas station in the real-world scenario of Aichi Prefecture, Japan. The purpose is to locate and size fast-charging stations in such ways that drivers can get access to these charging facilities within a rational driving range while considering real-world constraints. Furthermore, we include the investment cost and the EVs users' convenience cost. This problem is formulated by five integer linear programming using a weighted set covering models. The developed model determines where to locate charging stations as well as how many chargers should be installed in each charging station. The experimental results demonstrate that an appropriate location scheme can be obtained using the model M 5 . A computational experiment identifies the best infrastructure solutions for policymakers to consider in the context of growing environmental policies.

Sector coupling of electricity, transport and cooling with high share integration of renewables in Indonesia

Sector coupling improves energy system efficiency by maximising potential synergies among energy sectors. This paper aims to assess the sector coupling of electricity, transport, and cooling on the Java and Bali islands, Indonesia. Future energy systems in 2040, focussing on decarbonised electricity sector with high electric vehicles deployment and district cooling penetration, were simulated using EnergyPLAN. A bottom-up calculation approach was applied to determine demand in the transport sector. In the cooling sector, geospatial analysis was employed to quantify cooling demand and locate potential district cooling networks in Jakarta. Six scenarios were explored based on their energy demand and supply characteristics. Modelling results show that the sector coupling of three sectors could reduce primary energy supply (PES), CO2 emissions and annual costs. The most suitable scenario has about 8% lower PES, 14% lower CO2 emissions and 2% less annual costs compared to the business-as-usual scenario. Results also show that transport electrification could only effectively and significantly decrease CO2 emissions if its electricity demand is produced from renewables. Transport electrification with large scale integration of renewables could also lower the annual costs by decreasing fossil fuel costs in the transport and electricity sectors.

“I earned the right to build the next American car”: How autoworkers and communities confront electric vehicles

Recent climate and infrastructure legislation in the United States emphasizes the accelerated manufacture and deployment of electric vehicles (EVs) as a major decarbonization strategy. The implications of this transition toward EVs on those who have traditionally worked within the automotive industry and surrounding communities have yet to be evaluated in the literature. It is unclear whether the move to EVs will create non-union positions that are hourly, temporary, and lower-paid. Through 67 focus groups across six deindustrializing cities in the American Midwest, we bring analytical tools from cultural sociology into the Just Transitions literature to examine how autoworkers, former autoworkers, managers, and community members imagine the future of the EV transition. Our findings underscore a marked divergence between the people who assemble cars and the people who manage workers or are part of communities that may benefit from new technologies. Managers and community members expressed confidence in the automobile industry's history of agility and ability to “bounce back” from challenges that confront it. In contrast, autoworkers held deep skepticism about the transition to EVs, on issues ranging from access, usefulness, and infrastructure, with many believing that the technology itself is over-hyped, not appropriate for consumers' needs, and merely a political charade. Workers suspect that their past contributions will be forgotten and express suspicion about the green energy transition. Our findings underscore the need to consider how differently situated groups imagine the future costs and benefits of energy transitions in order to effectively build coalitions across civil society, workers, and government.

Effect of Penetration Levels for Vehicle-to-Grid Integration on a Power Distribution Network

With the exponential growth of electric vehicle sales worldwide over the past years and progress in technology and actions to combat climate change by reducing greenhouse gas emissions, the trend is expected to continue with a significant increase in the deployment of electric vehicles and plug-in hybrids. Given these circumstances, it is essential to identify the constraints that this increase in the number of electric vehicle charging stations poses for the electricity system. Therefore, the analysis developed in this paper discusses the effect of integrating electric vehicle charging stations in a real distribution network with different penetration levels. For this purpose, a typical electric system in Greece, managed by the Greek distribution system operator (HEDNO), is modeled and simulated in DIgSILENT PowerFactory software, one of the most widely used simulation tools in the electricity sector. To study the feasibility of connecting electric vehicle charging stations to the network, different case studies are presented, showing changes in the quantity of electric vehicles feeding power into the network through vehicle-to-grid technology. Quasi-dynamic simulations are used to analyze and discuss the voltage profiles of the system nodes, active power flows with the external source and power losses of the distribution network to determine whether the system is capable of supporting the increase in load produced by the electric vehicle charging stations and to promote awareness of the benefits of implementing vehicle-to-grid connections.

Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

Rise of EV as a Emerging and Convenient Market in Automobile Sector in India

India now ranks fourth among countries that generate greenhouse gases (GHG). Due to a lack of a clear policy or plan, India has been lagging behind other nations in the adoption of electric vehicles (EV). As a measure to reduce air pollution and fossil fuel usage, the USA, China, Norway, and Germany have all provided significant subsidies and incentives to convert to EVs. By examining both the Indian EV market and the overall development of EVs, this paper's primary goal was to analyse the policies, strategies, and technological factors for developing EVs. Also, the research and development status of EVs in India were taken into account in this study. Also, a strength, weakness opportunity and challenges (SWOC) study was conducted on the present EV deployment in India, along with the potential and difficulties that exist within this industry. By coordinating the development and climate change objectives, there are opportunities to reduce GHG emissions and improve the sustainability and climatic compatibility of India's transportation expansion. The National Action Plan for Climate Change (NAPCC) of India acknowledges that GHG emissions from transportation can be decreased by adopting a sustainability approach through a combination of measures, including increased use of public transportation, increased use of biofuels, and improved energy efficiency of transportation vehicles. In 2018, India overtook Germany as the fourth-largest vehicle market in the world. Two-wheeler sales in India have nearly tripled over the past ten years, and both passenger and commercial vehicle manufacturing have increased by half.

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.