Rollout Of Electric Vehicles Research Articles

In‐use dissipation of technology‐critical elements from vehicles and renewable energy technologies in Vienna, Austria: A public health matter?

AbstractThe rollout of electric vehicles and photovoltaic panels is essential to mitigate climate change. However, they depend on technology‐critical elements (TCEs), which can be harmful to human health and whose use is rapidly expanding, while recycling is lacking. While mining has received substantial attention, in‐use dissipation in urban areas has so far not been assessed, for example, corrosion and abrasion of vehicle components and weather‐related effects affecting thin‐film photovoltaic panels. Therefore, the question arises to which extent TCEs dissipate during use and which potential non‐occupational human health impacts could occur. We assessed the available information on urban in‐use dissipation and human health concerns and conducted exploratory modeling of in‐use technology stocks, in‐ and outflows, and in‐use dissipation of neodymium, dysprosium, lanthanum, praseodymium, cerium, gallium, germanium, and tellurium contained in 21 vehicle and renewable energy technologies, for Vienna, Austria. In prospective scenarios, TCE dynamics in a trend‐continuation vis à vis official city policy plans and a more ambitious transition scenario were then assessed. We find that electrifying the vehicle fleet without demand‐reduction is the main driver of TCE consumption, effectively doubling cumulative end‐of‐life outflows to 3,073 [2,452–3,966] t and cumulative in‐use dissipation to 9.3 [5.2–15.7] t by the year 2060. Sufficiency‐based measures could reduce demand and in‐use dissipation well below levels with continued trends, thus highlighting the need to combine decarbonization with demand‐reducing measures. These results help assess potential future in‐use dissipation dynamics and inform discussions about potential public health hazards associated with exposure to TCEs accumulating in the urban environment.

Exploring electric vehicle adoption in Indonesia using zero-shot aspect-based sentiment analysis

Transportation emissions significantly contribute to greenhouse gases (GHG) and climate change. Electric vehicles (EVs) offer a promising solution to this problem. Despite the noteworthy adoption of EVs in emerging economies like China and Europe, the pace of EV rollout in Indonesia remains sluggish. Currently, the country's rate of EV adoption is below 0.3 %, leading to stagnation in the Indonesian electric vehicle market. Several factors have impeded the adoption of EVs in Indonesia. Previous studies have investigated consumer acceptance of EV adoption in specific countries using sentiment analysis. Various data analytics and machine learning techniques have been implemented in those studies. However, those studies predominantly rely on the traditional sentiment analysis method, which assigns a single sentiment classification to each document or sentence. On the other hand, this study aims to investigate consumer acceptance of electric vehicle adoption in Indonesia through Twitter conversations. It utilises the Zero-Shot Aspect-Based Sentiment Analysis (ABSA) approach, which can provide a more fine-grained analysis of the aspects discussed and their corresponding sentiments. The findings demonstrate the effectiveness of this approach in identifying discussed aspects within tweets, though its sentiment classification performance is limited. The research also uncovers crucial aspects of electric vehicle adoption and public sentiments on Twitter. These insights could provide valuable guidance to the government and other stakeholders regarding the concerns associated with EV adoption in Indonesia.

Optimization of charging stations integrated with dynamic transportation systems in metropolises

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China’s dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas.

Multiobjective optimal operation strategy for electric vehicle battery swapping station considering battery degradation

The study aims to analyze a futuristic view of the automobile industry conducive to the much-needed penetration of Electric Vehicles (EVs) as per the current environmental and economic scenario. The study suggests the roll-out of EVs in tandem with the supporting Charging Infrastructure, which is a prerequisite for adopting the former. Although transport electrification is a much-accentuated and researched solution to the deteriorating environment and plummeting conventional resources, the design, production, manufacturing, use, degradation, and disposal of an exponential number of lithium-ion batteries for the same have environmental, economic, and social impacts. Thus, emphasis has been made on the sustainable use of charging infrastructure that curbs unnecessary and early battery aging from fast charging technology. Battery swap requests at a Battery Swapping Station (BSS) can be served via batteries from either available battery stock or by charging previously incoming discharged batteries. The study suggests an optimal strategy for the same via a mathematical model representing the operation cost of a BSS consisting of three components, namely, cost of battery utilization, damage cost associated with different charging methods, and dynamic electricity cost. The solution to the multiobjective optimization problem gave the optimum number of batteries that should be used from the battery stock and the charging decision for incoming discharged batteries, given the possible charging options and the constraints on demand satisfaction. Finally, the results from two different optimization tools, Solver in MS Excel and Lingo software, were compared.

Evaluation of Barriers to Electric Vehicle Adoption in Indonesia through Grey Ordinal Priority Approach

Emissions from vehicles are a major contributor to greenhouse gases, and thus climate change. Electric vehicles (EVs) provide a promising solution to deal with this problem. Even though in the emerging economies like China and Europe, the adoption of EVs is praiseworthy, the pace of the EV rollout in Indonesia is slow. The Indonesian electric vehicle market has remained stagnant due to the country's low adoption rate of electric vehicles, which is currently less than 0.3%. This is because electric vehicle adoption has been stymied in Indonesia for a variety of reasons. As such, the purpose of this study is to determine the factors influencing electric vehicle adoption in Indonesia and to rank the barriers to widespread EV rollout in the country using the Grey Ordinal Priority Approach (OPA-G). It is found that high initial purchase price, insufficient amount of charging infrastructure, and a lack of government incentives are key barriers to the EV adoption in Indonesia.

The cost of recharging infrastructure for electric vehicles in the EU in a climate neutrality context: Factors influencing investments in 2030 and 2050

The electrification of passenger cars is a cornerstone of the EU strategy to decarbonising European transport by 2050. A key step for the uptake of electric vehicles (EVs) is to scale up the rollout of rechargers. The paper assesses which factors influence the investments in EU recharging infrastructure in a context of ambitious EV deployment projected under the “Fitfor55” policy package of the European Commission. We use an energy-demand method that includes assumptions on charging requirements of different EV types, the structure of the charging fleet, the survival probability and learning rates of the rechargers and conduct a sensitivity analysis to quantify their impact on investments. The projected electricity share in passenger cars is about 4.5% (65 TWh) and the required infrastructure ranges between 3.2 and 4million chargers in 2030, while investments range between 8.7 and 16.2bn€ in 2021–2030. By mid-century, the electrification share of passenger cars increases to 62% (285 TWh) for which we estimate a total demand of 13.6 to 20.3million chargers and an additional 44.3 to 80.3bn€ of investments in 2031–2050. A regression analysis depicts a strong correlation between fast chargers and investment expenditures, which is particularly relevant for 2021–2030, a period, when the deployment of a fast charging network will be needed to help reduce charging times and enable the roll-out of EVs. The energy output per charging point is the second most influential factor. Other factors, while important, are found not to significantly affect system costs.

Analysis of activity duration-related charging behavioral responses of electric vehicle travelers to charging services

Electric vehicles (EVs) have drawn much attention in recent years due to the advantage of zero on-road carbon emission. The government subsidies and rapid development of electricity charging technologies promote the roll-out of EVs. The boost of EVs brings challenges to the deployment of EV charging services. Given that travelers may need to charge their EVs while conducting out-of-home activities, this study analyzes the activity duration-related charging behavioral responses of private EV travelers to charging services in an equilibrium model. The charging services concern the operating policies related to the spatial allocation of charging opportunities and charging pricing. Considering the trade-offs between travel and activity duration-related charging choices, a boundedly rational dynamic user equilibrium (BR-DUE) model is proposed to evaluate the two operating policies. The case study of the Eindhoven network demonstrates that the operating policies, if appropriately set up, can improve the accessibility to charging services with improved charging service rate and decreased queuing time.

Planning for low-carbon energy-transportation system at metropolitan scale: A case study of Beijing, China

The urbanization and expansion of megalopolises have led to concerns on traffic, energy crisis and deteriorated green-house gas emissions, and thus the electric vehicles (EVs) are expected to be an essential role in alleviating these problems. In this study, a flexible-possibilist chanced constraints programming (FCCP) model is developed to plan low-carbon energy-transportation systems at the metropolitan scale (METS), which can incorporate multiple uncertainties in both the soft constraints and objective function. By integrating the possibilist programming with fuzzy sets and chanced constraint, the FCCP could tackle multiple complexities such as the combination of vague possibilities, flexibilities and probabilities, hence is superior to conventional approaches. The FCCP model is then applied for the planning METS in Beijing, and solutions are obtained under different satisfactory degrees and confidence levels. The results reveal that: 1) the power demand will be increasingly dependent on the imported power and renewable energy in Beijing; 2) the mass roll-out of EVs will reduce 6.7 million tonnes of CH, 44.7 million tonnes of CO and 1.08 × 105 million tonnes of CO2 respectively, while the need of battery supply facilities will cost approximately 4 × 109 dollars; 3) the carbon emissions will decrease with the growing number of EVs, the upgraded power supply pattern and the stringent policies. These findings could support decision-makers to plan the METS system when faced with multiple uncertainties.

Environmental and Economic Benefits of a Battery Electric Vehicle Powertrain with a Zinc–Air Range Extender in the Transition to Electric Vehicles

Emissions and pollution from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been negatively affecting the global climate and public health. Electric vehicles (EVs) are a cleaner solution to reduce the emission and pollution caused by transportation. Lithium-ion (Li-ion) batteries are the main type of energy storage system used in EVs. The Li-ion battery pack must be considerably large to satisfy the requirement for the vehicle’s range, which also increases the cost of the vehicle. However, considering that most people use their vehicles for short-distance travel during daily commutes, the large pack is expensive, inefficient and unnecessary. In a previous paper, we proposed a novel EV powertrain design that incorporated the use of a zinc–air (Zn–air) battery pack as a range-extender, so that a smaller Li-ion pack could be used to save costs. The design and performance aspects of the powertrain were analyzed. In this study, the environmental and economic benefits of the proposed dual-battery powertrain are investigated. The results from the new powertrain were compared with values from a standard EV powertrain with one large Li-ion pack and a conventional internal combustion engine vehicle (ICEV) powertrain. In addition, an air pollution model is developed to determine the total amount of pollution released by the transportation sector on Highway 401 in Ontario, Canada. The model was then used to determine the effects of mass passenger EV rollout on pollution reduction.

Storage technologies for electric vehicles

This review article describes the basic concepts of electric vehicles (EVs) and explains the developments made from ancient times to till date leading to performance improvement of the electric vehicles. It also presents the thorough review of various components and energy storage system (ESS) used in electric vehicles. The main focus of the paper is on batteries as it is the key component in making electric vehicles more environment-friendly, cost-effective and drives the EVs into use in day to day life. Various ESS topologies including hybrid combination technologies such as hybrid electric vehicle (HEV), plug-in HEV (PHEV) and many more have been discussed. These technologies are based on different combinations of energy storage systems such as batteries, ultracapacitors and fuel cells. The hybrid combination may be the perspective technologies to support the growth of EVs in modern transportation. The advanced charging systems may also play a major role in the roll-out of electric vehicles in the future. The general strategies of advanced charging systems are explained to highlight the importance of fast charging time with high amount of power and its cost-effectiveness for electric vehicles. Furthermore, the battery pack designing calculation is briefly explained along with all mechanical, electrical and environmental battery tests, which helps in the evaluation of batteries. Moreover, this paper also has a brief summarizing with the help of a flow chart, which clearly demonstrates all the parts of electric vehicles in a much simpler way.

Analysing the impacts of a large-scale EV rollout in the UK – How can we better inform environmental and climate policy?

Electrifying transport to meet local pollution and overall net zero carbon ambitions is now a key UK policy focus, but this will have important impacts on the energy system, the economy, and the environment. Understanding the changes that the electrification of transport will bring is crucial for developing sustainable policies for net zero goals and a just transition. A literature is emerging to analyse the impact of a large-scale penetration of electric vehicles (EVs), but generally limiting focus to the implications for the electricity network. In this paper, we aim to provide insight on the wider energy system impacts of the expected EV rollout in the UK, in terms of fuel changes, energy costs, CO2 emission reduction and network investments; and how different EV charging strategies increase or mitigate the impacts of the expected large-scale penetration of EVs. Results show that non-smart and/or decentralised charging will require considerably larger investments on the network to accommodate new EV demand. Network reinforcement costs are passed to the consumer via increased electricity prices and, albeit reduced, emissions shift from the transport to the power sector. These results show the importance of considering the whole energy system and the wider economy, to avoid carbon leakage and to maximise the effectiveness of policies.

Cybersecurity of Smart Electric Vehicle Charging: A Power Grid Perspective

With the roll-out of electric vehicles (EVs), the automobile industry is transitioning away from conventional gasoline-fueled vehicles. As a result, the EV charging demand is continuously growing and to meet this growing demand, various types of electric vehicle charging stations (EVCSs) are being deployed for commercial and residential use. This nexus of EVs, EVCSs, and power grids creates complex cyber-physical interdependencies that can be maliciously exploited to damage each of these components. This paper describes and analyzes cyber vulnerabilities that arise at this nexus and points to the current and emerging gaps in the security of the EV charging ecosystem. These vulnerabilities must be addressed as the number of EVs continue to grow worldwide and their impact on the power grid becomes more viable. The purpose of this paper is to list and characterize all backdoors that can be exploited to seriously harm either EV and EVCS equipments, or power grid, or both. The presented issues and challenges intend to ignite research efforts on cybersecurity of smart EV charging and enhancing power grid resiliency against such demand-side cyberattacks in general.

An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further mass roll-out of electric vehicles. To assess the impact of charging of substantial number of electric vehicles on the grid, a model of 30000 PEVs integrated with unit commitment (UCEV) was investigated in this study. The unit commitment was a large-scale, mixed-integer, nonlinear, NP-Hard (non-deterministic polynomial) optimization problem, while the integration of PEVs further increased the complexity of the model. In this paper, a global best inspired negatively correlated search (GBNCS) method which extends the evolutionary logic of negatively correlated search is proposed to tackle the UCEV problem. In the proposed algorithm, a rounding transfer function in GBNCS, is deployed to convert real-valued variables into binary ones; further, the global best information is combined in the population to improve the efficiency of the algorithm. Numerical results confirmed that the proposed GBNCS can achieve good performance in both a basic IEEE 10 unit commitment problem and the UCEV problem. It was also shown that, among four charging modes, the off-peak charging mode and EPRI (Electric Power Research Institute) charging mode are more economical in PEV charging.

Can spending to upgrade electricity networks to support electric vehicles (EVs) roll-outs unlock value in the wider economy?

We investigate the question of whether spending to enable ambitious EV roll-out programmes can in fact generate net gains across the wider economy. We use a multi-sector computable general equilibrium (CGE) model for the UK economy and focus on the need to upgrade electricity networks to support an initial EV penetration scenario for the period to 2030. We find that large scale spending and cost recovery for network upgrades is likely to result in net negative impacts on key macroeconomic indicators, including real income available for spending across all UK households. This is due to a combination of time-limited network upgrade activity in the presence of capacity constraints combined with the need for costs to be passed on to electricity consumers through higher bills. But the lowest income households – the group of greatest concern to policymakers – suffer the smallest losses. Moreover, the EV uptake delivers sufficient gains that deliver net positive impacts on all household incomes, with sustained expansion in GDP and employment across the economy. The key driver is a greater reliance on UK supply chains with the shift away from more import-intensive petrol and diesel fuelled vehicles towards electric ones.

Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

Electric Vehicle Battery Degradation Under Electric Grid Operations: Vehicle to Grid Strategies

Battery degradation is extremely sensitive to usage and chemistry. Some batteries may be markedly sensitive to temperature, to state of charge, to both, or to some other factors. This raises concerns over battery durability in the rollout of electric vehicles (EVs) in hot climate. Additionally, with the integration of more and more intermittent renewable energy power plants on the grid, there is a push to use EV batteries as energy storage systems which may stress the batteries even more. In most studies on the techno-economical impact of EVs on the grid, the battery is often viewed as a black box and therefore there is no consensus on the actual long term impact of climate and/or vehicle-to-grid (V2G) and grid-to-vehicle (G2V) charging profiles on batteries. This work aims at assessing such impact. Daily vehicle usage can be broken down into driving, charging and idling periods. In most cases, car batteries spend most of their time idling and it is therefore essential to understand the degradation associated with time, temperature and state of charge. Specifically, we studied the impact of driving on the cell degradation with or without V2G and G2V episodes. We also investigated the impact of frequency response at different intensities. All the data was analyzed using HNEI’s unique diagnosis and prognosis tools to forecast the degradation over the battery warranty period and beyond.

A Study of Smart Grid Effects on Electric Vehicle Management Considering the Change of the Power Capacity Mix

Background: To understand the Electric Vehicle (EV) management effects deeply using Smart Grids (SGs) in the electric power sector, it is necessary to examine supply specifics such as the generation mix, generation costs, and CO2 emissions as well as the demand sector including peak load. This study attempts to comprehensively examine the changes in power supply and demand their effects in accordance with the degree of SG utilization, based on a scenario for the projection of EV roll-out in South Korea. Objectives: This study considers the change of the generation capacity mix as well as the change of power generation mix using the WASP model for the analysis of SG effects on EV management. In the scenario of the Korean government's EV deployment, this study has confirmed how electric power demand changes according to the degree of smart grid utilization. In addition, the WASP model has been used to examine not only the power generation mix but also the change in the installed capacity. Result: As a result, if the share of cost-effective and clean power generation sources is below the minimum load, the unit cost and CO2 emission could not be reduced together even though SGs are used to manage EVs. Conclusion: Increasing the share of power generation from clean energy sources to a level higher than that of the minimum load will allow EVs to become an eco-friendly means of transportation.

Electric vehicle charging choices: Modelling and implications for smart charging services

The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers.

Cycle and Calendar Aging Study of Lithium-Ion Batteries Under Various Vehicle-to-Grid Scenarios

Battery degradation is extremely sensitive to usage and chemistry. Some batteries may be markedly sensitive to temperature, to state of charge, to both, or to some other factors. This raises concerns over battery durability in the rollout of electric vehicles (EVs) in hot climate. Additionally, with the integration of more and more intermittent renewable energy power plants on the grid, there is a push to use EV batteries as energy storage systems which may stress the batteries even more. In most studies on the techno-economical impact of EVs on the grid, the battery is often viewed as a black box and therefore there is no real understanding on the actual long term impact of climate and/or vehicle-to-grid (V2G) and grid-to-vehicle (G2V) charging profiles on batteries. This work aims at assessing such impact. Daily vehicle usage can be broken down into driving, charging and idling periods. In most cases, car batteries spend most of their time idling and it is therefore essential to understand the degradation associated with time, temperature and state of charge on top of the impact of the duty cycles. Herein, we studied the impact of driving on the cell degradation with or without V2G and G2V episodes by looking at both the calendar-induced and cycling-induced degradation. All the data was analyzed using HNEI’s unique diagnosis and prognosis tools [1-3] to forecast the degradation over the battery warranty period and beyond. This research supports the goals of the Electric Vehicle Transportation Center. [1] M. Dubarry, C. Truchot and B.Y. Liaw, J. Power Sources 219 (2012) 204-216 [2] M. Dubarry, A. Devie and B.Y. Liaw, J. Energy Power Sources 1(5) (2014) 242-249 [3] https://www.soest.hawaii.edu/HNEI/alawa/

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.