Large-scale Adoption Of Electric Vehicles Research Articles

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Charging Stations for Large-Scale Deployment of Electric Vehicles

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established.

The Impact of the Adoption of Electric Vehicles in Public Transportation Markets on Their Diffusion in Consumer Markets

Green innovative products usually diffuse in both the organizational and consumer markets at the same time. It is important to carefully consider how the diffusion in these different markets may interact with each other. This article focuses on how the large-scale adoption of electric vehicles in public transportation markets affects individuals' perceptions and preferences in consumer markets. Experiment 1 demonstrates that in the separate evaluation mode, consumers are more inclined to purchase vehicles from a company that sells electric buses, taxis, or online booking cars in addition to personal cars, compared to a company that solely focuses on selling electric vehicles to individual consumers. Furthermore, the large-scale adoption of electric vehicles in public transportation markets enhances consumers' perception of the functional and expressive value of these products. However, experiment 2 reveals that in the joint evaluation mode, the product preferences are reversed. When consumers have the opportunity to compare and choose between a company that specializes in selling personal cars and one that offers both personal cars and public vehicles, they express a preference for purchasing a car from the former, believing that it has higher functional and expressive value compared to the latter. This paper highlights that the change in evaluation mode leads to differences in consumers' interpretation of signals sent by companies. In the separate evaluation mode, consumers are more likely to perceive positive signals related to products, while in the joint evaluation mode, they are more likely to perceive negative signals related to products, leading to a reversal of product preference. This article promotes an understanding of the subsequent influence of companies entering the organizational market.

Scheduling and Routing of Mobile Charging Stations With Stochastic Travel Times to Service Heterogeneous Spatiotemporal Electric Vehicle Charging Requests With Time Windows

This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging.

A Review on Electrode Materials of Fast-Charging Lithium-Ion Batteries.

In recent years, the driving range of electric vehicles (EVs) has been dramatically improved. But the large-scale adoption of EVs still is hindered by long charging time. The high-energy LIBs are unable to be safely fast-charged due to their electrode materials with unsatisfactory rate performance. Thus it is necessary to summarize the properties of cathode and anode materials of fast-charging LIBs. In this review, we summarize the background, the fundamentals, electrode materials and future development of fast-charging LIBs. First, we introduce the research background and the physicochemical basics for fast-charging LIBs. Second, typical cathode materials of LIBs and the method to enhancing their fast-charging properties are discussed. Third, the anode materials of LIBs and the strategies for improving their fast-charging performance are analyzed. Finally, the future development of the cathode materials in fast-charging LIBs is prospected.

Electric vehicle charging infrastructure planning for integrated transportation and power distribution networks: A review

Environmental concerns and urban air pollution are major drivers for the transition from combustion engines to electric vehicles. To support a large-scale adoption of electric vehicles an efficient charging infrastructure roll-out is required. However, the optimal planning of charging stations is a non-trivial task, as it requires coordination between planning activities in the transportation and the power distribution network. Historically, the modeling of these two networks has been approached from different research areas and with the use of different methodologies. As a result, few papers embrace the combined problem to its full extent and the literature is highly scattered and represents different model perspectives and the targeting of many different objectives. In the paper, we present a systematic review of the literature that considers the combined investigation of transportation and distribution networks, with a focus on objectives, methodology and scope. It allows us to describe the current state of the art, to highlight research gaps and to propose directions for future work of the research community. The identified research gaps include, but are not limited to: 1) a general lack of integrated modeling approaches, 2) the need for increasingly detailed modeling of charging demand that accounts for randomness and variability in space and time rather than averages to support power grid planning, 3) the need to consider a mix of different types of charging options rather than only fast charging and 4) the need to shift from standard test networks and theoretical based planning approaches to large-scale and real-world case studies. • A systematic literature review on optimal planning of public charging infrastructure •Charging infrastructure planning for joint transportation and distribution networks •Identification of research gaps with regard to modeling approaches and applications •The necessity for more detailed modeling of charging demand in space and time •The need for real-world case studies and consideration of different types of charging

Evaluation of the multi-dimensional growth potential of China's public charging facilities for electric vehicles through 2030

Based on almost 6 million charging records of 1588 charging stations in Jiangsu Province, China, this paper quantitatively analyzes the growth potential of public charging facilities for electric vehicles (EVs) in China towards 2030, from both aspects of saving power cost and providing flexible regulation capability. Based on the single EV charging optimization model, the unsupervised method is adopted to handle the efficiency problem of sampling calculation for massive actual data, and loop calculations were then conducted for sampling optimization. Bus charging stations (BCS), urban public charging stations (UPCS), and highway charging stations (HWCS)) are considered. Calculation results show that charging operators or users can save more than 20% of the power purchase cost (about 88.1 billion yuan) through intelligent charging management. By 2030, large-scale adoption of EVs would enable schedulable capacity that accounts for more than 10% of China's annual electricity generation from renewable energy sources (wind and photovoltaic energy). Moreover, electrical buses have the most potential now, while the figure for electrical passenger cars charged in UPCS would account for the greatest proportion in the future.

Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model

While large-scale adoption of electric vehicles (EVs) globally would reduce carbon dioxide (CO2) and traditional air pollutant emissions from the transportation sector, emissions from the electric sector, refineries, and potentially other sources would change in response. Here, a multi-sector human-Earth systems model is used to evaluate the net long-term emission implications of large-scale EV adoption in the US over widely differing pathways of the evolution of the electric sector. Our results indicate that high EV adoption would decrease net CO2 emissions through 2050, even for a scenario where all electric sector capacity additions through 2050 are fossil fuel technologies. Greater net CO2 reductions would be realized for scenarios that emphasize renewables or decarbonization of electricity production. Net air pollutant emission changes in 2050 are relatively small compared to expected overall decreases from recent levels to 2050. States participating in the Regional Greenhouse Gas Initiative experience greater CO2 and air pollutant reductions on a percentage basis. These results suggest that coordinated, multi-sector planning can greatly enhance the climate and environmental benefits of EVs. Additional factors are identified that influence the net emission impacts of EVs, including the retirement of coal capacity, refinery operations under reduced gasoline demands, and price-induced fuel switching in residential heating and in the industrial sector.

An Autonomous Charge Controller for Electric Vehicles Using Online Sensitivity Estimation

Despite their sustainable benefits, large-scale adoption of electric vehicles (EVs) into the distribution system is challenging. Uncontrolled charging of EVs could increase voltage violations, system power losses, and feeder overloads. In this article, an autonomous EV charge control strategy is proposed to control EV charging in a way that mitigates their negative impacts. To ensure fair contribution from EVs connected to different nodes, both the local nodal voltage and sensitivity of voltage to load changes are used as inputs to the proposed controller. This is so because these two inputs have a complementary relationship. That is, nodes with lower voltages are generally more sensitive to load changes than those with higher voltages. In addition, a novel approach for locally estimating the voltage sensitivities online is proposed. This ensures the robustness of the proposed control strategy to changes in loading conditions and system configurations without the need for communication. An EV-rich test distribution system is modeled in the DIgSILENT PowerFactory environment and used to validate the proposed control strategy. The test results demonstrate the effectiveness and robustness of the proposed strategy considering different loading conditions, system reconfiguration, distributed generators, and voltage control devices.

Expanding EV Charging Networks Considering Transportation Pattern and Power Supply Limit

For large-scale adoption of electric vehicles (EVs), a charging network is needed near the road. The charging station planning and deployment problem should consider the increasing penetration ratio of EVs over a long period of time, and the highly dynamic and location-dependent demands and power grid constraints. This paper focuses on the dynamic charging network design, i.e., how to optimize the charging station locations and the number of chargers in each station at different time stages with an increasing EV penetration ratio. For each candidate location, we first model its coverage area to estimate the dynamic EV charging requirements. Then, we formulate the problem at each time stage as profit maximization, which is a mixed-integer optimization problem. To make it tractable, we investigate the profitability of candidate locations and derive their upper and lower bounds on the expected profit. Then we take two steps to transform and relax the problem to convex optimization. A fastconverging search algorithm, named RMCL-E, is proposed. Using real vehicle traces, simulation results show that the proposed algorithm can make a good trade-off between the service blocking probability and the construction cost to maximize the total profit, which is attractive for charging service providers.

Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context

The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l-1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.

An Automated Charger for Large-Scale Adoption of Electric Vehicles

The penetration of electric vehicles (EVs) is expected to increase in the future. With more EVs on the road, more loads will be added to power systems, which will impact the system voltage and loading. This paper studies the impact of the EVs on the distribution system and provides an automated controller that satisfies the customer requirements and mitigates the negative impacts of the charging of EVs on the system. The controller takes into consideration the system voltage, the customer requirements, and the state of charge of the battery. The controller is tested using a large-scale distribution system in MATLAB Simulink. It is also validated using a small-scale four-bus experimental system. To show the interaction between local distributed generations (DGs) with the EV charging, the controller is tested in the presence of DG units. The results showed the superior performance of the controller in charging the EVs smoothly and mitigating the negative impacts of the grid.

Strategies for Implementing Public Service Electric Bus Lines by Charging Type in Daegu Metropolitan City, South Korea

The large-scale adoption of electric vehicles in the public sector is essential for achieving emission reduction targets for transportation. In particular, the replacement of buses with internal combustion engines, which travel long distances and produce massive greenhouse gas emissions, by their electric counterparts can drastically reduce emissions. A variety of electric buses with different power supply systems are currently available, and their performance, charging type, battery capacity, and operating environment are related parameters that must be addressed for their successful and massive adoption. For instance, the appropriate charging type of electric buses depends on conditions, such as the operating environment. In this study, we determined the optimum capacity of electric bus batteries by considering the electric bus range, battery depth of discharge, and deterioration cost while using ADVISOR, which is a MATLAB-based electric vehicle simulator. In addition, we assessed the energy consumed and charging time according to the operating environments of electric buses. Finally, an economic efficiency analysis allowed for determining the appropriated charging type for electric buses. By integrating these data and analyses, we propose a comprehensive plan for selecting the most appropriate charging type according to the operating environment of these electric vehicles. We expect that the proposed plan will contribute to the adoption of electric buses and achieve the greenhouse gas reduction targets set by South Korea.

Cost-effective supply chain for electric vehicle battery remanufacturing

Large-scale adoption of electric vehicles can reap significant energy and environmental benefits while also reducing reliance on fossil fuels. Nonetheless, accompanying the benefits of electric vehicles, several economic and ecological challenges arise from the production of Lithium-ion batteries, which are currently the most popular type of batteries used in electric vehicles. Remanufacturing is a promising end-of-life strategy and can lead to more sustainable Lithium-ion battery supply chains to support large-scale adoption of electric vehicles. Several factors will dictate the feasibility and effectiveness of remanufacturing, including economic viability, production capability, and battery demand and supply. Unfortunately, while there exists significant research efforts on remanufacturing at the laboratory scale, there lacks research that investigates Lithium-ion battery remanufacturing at the enterprise scale. Motivated by this, in this paper, a state-of-the-art closed loop supply chain network model for Lithium-ion battery remanufacturing considering different quality levels of spent battery returns is proposed. An optimization model is developed to maximize the network profit and a sensitivity analysis is performed to determine the impact of several important model parameters on the profitability of the proposed supply chain network. A numerical case study is implemented which shows that 9.81–30.93% increase in profit can be achieved if remanufacturing is integrated in Lithium-ion battery supply chain networks. Moreover, the sensitivity analysis shows that careful implementation of the proposed algorithm coupled with understanding of battery parameters are the keys to implementing cost-effective electric vehicle Lithium-ion battery supply chains. In all, this research will help stimulate the implementation of remanufacturing, promote economically and environmentally sustainable supply chain management in the electric vehicle battery industry, and support the transportation sector in reducing environmental burdens.

Analysis of the Barriers to Widespread Adoption of Electric Vehicles in Shenzhen China

China promoted the large-scale adoption of Electric Vehicles (EVs) in its 13th five-year plan; however, this target faces many obstacles. This paper analyzes the main barriers to widespread adoption of EVs through a survey in Shenzhen, which has the biggest EVs market share out of China’s major cities. Based on previous research, this paper conducted a new study using 406 approved questionnaires among 500 participants. Our study proposed five hypotheses to examine the main barriers to widespread adoption of EVs. The analysis was conducted using statistical method that included two-way frequency tables, chi-square test, and factor analysis. The results indicated that perception of advantages of EVs and access to recharging EVs remained the main barriers in large-scale penetration. Furthermore, our study revealed that a drop in financial incentives would not cause a significant decline in the future adoption of EVs. The study provides suggestions to car manufacturers and government policy advisors based on our analysis and discussion.

Revisiting the Criterion for Mechanical Supression of Dendrites at the Li/Electrolyte Interface

Large-scale adoption of electric vehicles has been challenging due to the high energy density required to achieve a reasonable driving range. One route to increasing the energy density has been to develop “beyond Li-ion” chemistries1 like Li-air and Li-S. Another route is to use Li metal anode coupled with a traditional Li-ion cathode since lithium metal electrode possesses a very high theoretical specific capacity (3860 mAh g-1) and low reduction potential.2 Both routes require a Li metal anode which can reversibly cycle without forming dendrites. Mechanical suppression of dendrite growth through solid or polymer electrolytes has shown potential for alleviating the problem. It has been suggested by treating the electrode and electrolyte as isotropic materials that a solid polymer electrolyte that has roughly a shear modulus twice that of lithium can suppress dendrite growth.3 One potential issue relates to the assumption of isotropic materials as Li metal is highly anisotropic with a drastically different shear and elastic moduli depending on the crystal orientation. This further dictates that the nature of dendrite growth will be affected by the crystal orientation of Li metal in contact with the electrolyte. In this work, we aim to fill the gaps in our current understanding of the mechanical suppression of dendrite growth at electrode-electrolyte interfaces by explicitly accounting for the anisotropic effects. We impose a periodic deformation at the interface and solve two-dimensional linear elasticity equations to get the resulting periodic deformation at the anisotropic electrode-electrolyte interface. We use the full elastic tensor of Li metal anode and solid electrolyte calculated from first-principles density functional theory calculations for the constitutive stress-strain relationship of each material. The elastic tensor was calculated based on the method of homogeneous deformations of the unit cell followed by fitting the energies of the strained cell to energy-strain relationship. Using the calculated deformation, we obtain the stresses at the interface and study their role in the amplification of surface roughness using a kinetic model.4 Our anisotropic model enables us to study the effect of different crystal orientations of Li metal anode and solid electrolyte on the dendrite growth.

Potential for widespread electrification of personal vehicle travel in the United States

Large-scale adoption of electric vehicles will only occur if the needs of individual drivers are met. Here the authors present a model of the energy consumption of personal vehicles in the USA, allowing an evaluation of the adoption potential of electric vehicles.

A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

With the large-scale adoption of electric vehicles (EVs) on expressways, the exploration of a guiding-based charging method to effectively adjust interactions between EVs and the fast charging stations (CSs) is urgently needed. This paper proposes a status-of-use (SOU) price-based charging strategy that can motivate users to charge in advance. A queuing model for a CS cluster was established to verify the effectiveness of the strategy, and then a simulation of traveling and charging conditions of 12,000 pure EVs on the road network from 0:00 to 24:00 was performed according to the related data and using the Monte Carlo method, the Floyd-Warshall algorithm, and the queuing algorithm proposed in this paper. Compared to unordered charging (UC), SOU price-based charging can not only reduce the charging cost and waiting time for users, but also increase the utilization ratio of charging facilities in a CS cluster and thus lower their influence on the power grid and expressway traffic. SOU price-based charging can effectively adjust interactions between EVs and CSs.

Future energy loads for a large-scale adoption of electric vehicles in the city of Los Angeles: Impacts on greenhouse gas (GHG) emissions

Using plug-in electric vehicles (PEVs) has become an important component of greenhouse gas (GHG) emissions reduction strategy in the transportation sector. Assessing the net effect of PEVs on GHG emissions, however, is dependent on factors such as type and scale of electricity generation sources, adoption rate, and charging behavior. This study creates a comprehensive model that estimates the energy load and GHG emissions impacts for the years 2020 and 2030 for the city of Los Angeles. For 2020, model simulations show that the PEV charging loads will be modest with negligible effects on the overall system load profile. Contrary to previous study results, the average marginal carbon intensity is higher if PEV charging occurs during off-peak hours. These results suggest that current economic incentives to encourage off-peak charging result in greater GHG emissions. Model simulations for 2030 show that PEV charging loads increase significantly resulting in potential generation shortages. There are also significant grid operation challenges as the region׳s energy grid is required to ramp up and down rapidly to meet PEV loads. For 2030, the average marginal carbon intensity for off-peak charging becomes lower than peak charging mainly due to the removal of coal from the power generation portfolio.

Impact of Peak and Valley Period Partition on Load Curve of Distribution System with EV

Large-scale adoption of electric vehicles may impose impact on grid. In order to formulate the appropriate scheme of time-of-use (TOU) to lower the adverse effect of charging load on the power grid, it is necessary to analyze the impact of the peak-valley period partition on the load curve of distribution system with electric vehicles. The electric vehicle charging load model considering TOU was built according to the statistics of the driving habits. Several scenarios were set according to different period partitions and the changes of the load at start time of valley period, numerical value and moment of peak load and peak-valley differences were analyzed with the Monte Carlo simulation method under those scenarios. The simulation results show that the farther between the time instant of peak load and start moment of valley period the less impact it has on load curve of distribution system.