Deployment Of Electric Vehicles Research Articles

A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks

AbstractMeeting climate targets requires widespread deployment of low-carbon technologies such as distributed photovoltaics, heat pumps and electric vehicles. Without mitigating actions, changing power flows associated with these technologies would adversely impact some local networks. The extent of these impacts, and the optimal means of avoiding them, remains unclear. Here we use local-level data and network simulation to estimate variation in future network upgrade costs in over 40,000 geographical regions comprising all of Great Britain. We find that costs vary substantially between localities, and are typically highest in urban areas, and areas with highest deployment of heat pumps and electric vehicles. We estimate reductions in required upgrades associated with local flexibility, which vary substantially between localities. We show that using geographically disaggregated data to inform flexibility deployment across the country could reduce network upgrade costs by hundreds of millions of pounds relative to an approach that treats localities as homogeneous.

Design of a Charging Station for Electric Vehicles Based on a Photovoltaic-Biodiesel Hybrid Renewable Energy System Combined with Battery Storage

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions.

Are vehicle lifespan caps an effective and efficient method for reducing US light-duty vehicle fleet GHG emissions?

With light duty vehicles (LDVs) responsible for 17% of annual US greenhouse gas (GHG) emissions, integrating emerging GHG-reducing technologies into the fleet is essential. However, the slow rate of vehicle turnover presents a significant barrier to the market penetration of new technologies, with adoption delayed by the low number of vehicles needing replacement each year. A strategy of accelerated vehicle turnover through a vehicle lifespan cap could potentially mitigate this limit. While older studies reach differing conclusions on their effectiveness, two newer studies that incorporate life cycle assessment find that accelerated turnover strategies can be effective if coupled with high levels of electric vehicle deployment. We seek to determine whether a vehicle lifespan cap strategy can be an effective and efficient (cost-effective) method for reducing US LDV fleet GHG emissions. We augment the capabilities of the Fleet Life Cycle Assessment and Material Flow Estimation (FLAME) fleet life cycle assessment model, integrating vehicle lifespan caps and comprehensive calculations of cost along with sensitivity analysis for electric vehicle survival curves and battery degradation. The augmented FLAME model is used to analyse the impact of vehicle lifespan caps of varying lengths on a suite of scenarios, including a business as usual (BAU) scenario and eight scenarios modelling different technology improvement assumptions. This work confirms that vehicle lifespan caps have limited effectiveness in reducing GHG emissions under a BAU scenario but show potential to meaningfully reduce GHG emissions in a scenario with accelerated deployment of electric vehicles. However, abatement costs are high, exceeding 2020 USD 1000/tCO2eq under baseline assumptions, but falling within the range of current estimates of the social cost of carbon under more optimistic assumptions. Overall, vehicle lifespan caps must be carefully considered as they accelerate both the benefits and costs of new vehicle technologies, and are best positioned as part of a larger integrated strategy for tackling transportation GHG emissions.

Electrifying the road: Navigating the transition to electric vehicles in Connecticut through hybrid insights and fleet evolution

The adoption of light duty electric vehicles (EVs) in the past 5 years has accelerated dramatically. As EVs continue penetrating the U.S. market, it is critical to understand what a sustainable transition within the transportation sector will look like in relation to mitigating impacts of climate change. This work aims to fill two existing gaps in the current literature: 1) investigate the positive and negative potential influences of the emerging EV transition within the U.S. market and 2) investigate the types of vehicles EVs are replacing. We fulfill these objectives by utilizing a unique attribute dataset of all vehicles in Connecticut (USA) on the road between 2013 and 2020. Our findings suggest that the penetration rate of traditional hybrid vehicles (e.g., Toyota Prius) is an important predictor of EV adoption. Furthermore, our results show that in Connecticut most of that transition is occurring among hatchbacks (e.g., Chevrolet Bolt), a body style where internal combustion engine (ICE) versions have relatively high fuel efficiencies compared with other major vehicle types (e.g., pickup trucks or SUVs). Our results offer important implications for policymakers in terms of maximizing the deployment of EVs and EV infrastructure and the path in which the automotive sector is progressing towards decarbonization.

Carsharing Worldwide: Case Studies on Carsharing Development in China, Europe, Japan, and the United States

Carsharing has received considerable attention as a sustainable mobility paradigm. Various service designs and dynamic business environments have increased the decision complexity for the carsharing business. Therefore, carsharing operators require a tool for assessing business development from holistic perspectives. This research provides a framework for outlining the requirements of the carsharing system with holistic perspectives of stakeholders being considered, as well as to derive assessment metrics for examining carsharing development. To create the framework, the system modeling tool, context diagram, was adopted to map out the interactions of externalities with the system and the requirements of the system. Eight assessment metrics: the market condition, business advantage, parking condition, electric vehicle deployment, self-serving configuration, vehicle reservation, vehicle maintenance, and pricing scheme, were eventually identified from the system modeling. From these dimensions, we review 24 carsharing cases from China, Europe, Japan, and the United States, and we summarize discrepancies among different marketplaces and some managerial insights on carsharing development, such as carsharing motivators and inhibitors, innovations in respect of different business backgrounds, approaches of increasing parking privileges, approaches of increasing electrifications, essential digital features, reservation regimes, methods of vehicle maintenances, and service pricing regimes.

Optimal design of an LCC-S WPT3 Z1 SAE J2954 compliant system, using NSGA-II with nested genetic algorithms for simultaneous local optimization

Wireless Power Transfer (WPT) for electric vehicles is one of the most promising methods that, given its advantages, will drive the deployment of electric vehicles. This paper presents a mathematical optimization method applied to the complete design of an LCC-S WPT3 Z1 11 kW system that complies with the SAE J2954 standard (Wireless Power Transfer for Light-Duty Plug-in/Electric Vehicles and Alignment Methodology, 2020). A design method based on three phases is proposed, allowing the complete inductor system, including ferrites shielding and compensation circuit components, to function in any relative primary and secondary position. In Phase 1, a multi-objective NSGA-II algorithm is designed, utilizing three nested genetic algorithms. The goal is simultaneously searching for the local optimum between the primary and secondary systems in three positions. This is achieved by modeling the circuit’s electrical and electromagnetic parameters with equations, enabling an iterative process with reduced computational time. The NSGA-II algorithm yields three scenarios: primary copper volume minimization, secondary copper volume minimization, and a compromise solution that optimizes the total volume. The result is then modeled in Phase 2 using a 3D finite element program that includes ferrite and optimal shielding, obtaining the values of inductances and mutual inductance in the three positions, as well as design data for manufacturing. This result is introduced in Phase 3 to optimize compensation circuit components using a second NSGA-II algorithm with three nested genetic algorithms. Again, three scenarios are obtained based on the desired system behavior and the optimal cost of the components. The result is validated through simulation with Matlab-Simulink and experimentally using a prototype constructed for this purpose.

Copper Oxide Nanoparticles Anchored on Porous Carbon Nitride Nanosheets for Supercapacitor Applications

Electrochemical energy storage devices are vital for renewable energy integration and the deployment of electric vehicles. Ongoing research seeks to create new materials with innovative morphologies capable of delivering high specific capacitance for the next generation of customizable energy devices. Carbon nitride is an excellent candidate for electrochemical energy storage devices; however, it has limitations such as layer stacking, poor electric conductivity, a restricted number of electroactive sites, and static electrochemical reaction rates. This research objective is to make porous structures in carbon nitride nanosheets and integrate them with CuO particles to increase surface area and improve electrochemical performance. The use of thermal heating, acidic treatment, and hydrothermal processes accomplishes this. Along with X-ray diffraction peaks of the CuO phase, a prominent peak (002) at 27.67° indicates the presence of graphitic-structured carbon nitride. TEM images show that CuO particles are evenly attached to the surface of g-C3N4 nanosheets with lattice intervals of 0.336 and 0.232 nm, which are the (002) and (111) orientations of the g-C3N4 and CuO phases, respectively. Adding CuO nanoparticles to porous g-C3N4 nanosheets avoids layer stacking and provides micro- and mesopore channels, increasing the specific surface area (42.60 m2 g-1). The CuO@ porous g-C3N4 electrode delivered 817 F g-1 of specific capacitance at 1 A g-1 and admirable capacitance retention (92.3% after 6000 cycles) due to the synergistic impact of its unique composition and structural characteristics. Because of its outstanding electrochemical performance and fascinating discoveries, CuO@ porous g-C3N4 may be employed as a cathode material for high-performance supercapacitors.

Intelligent Integration: Harnessing Artificial Intelligence for Enhanced Performance and Efficiency in Electric Vehicles

The transition towards electric vehicles (EVs) necessitates the development of efficient and reliable charging infrastructure. This paper presents an AI-driven approach to optimize EV infrastructure, focusing on five key aspects: profiling, augmentation, forecasting, explainability, and charging efficiency. Profiling involves understanding EV drivers' behaviors and preferences, facilitating targeted infrastructure development. Augmentation utilizes AI algorithms to identify optimal locations for new charging stations or upgrades based on usage patterns and demand forecasts. Forecasting models leverage machine learning techniques to predict future EV adoption rates and charging demands, aiding in infrastructure planning. These datasets can be used to generate insights and decisions through the use of artificial intelligence (AI) algorithms. A thorough analysis of the usefulness of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability, and charge optimization of the EVI has not yet been conducted, despite a number of recent studies in this area. This study's goal was to create, develop, and assess a thorough AI framework that fills in this EVI gap. The findings of an empirical assessment of this AI framework on an actual EVI case study validate its usefulness in tackling the new issues surrounding dispersed energy resources in the deployment of EVs.

Unified Framework for the Analysis of the Effect of Control Strategies on On-Load Tap-Changer’s Automatic Voltage Controller

With the advent of new loads and generation on the low voltage grid, voltage fluctuation has increased, especially in active distribution grids with a high penetration of distributed resources and a large deployment of electric vehicles. The coordination of different technologies has emerged as the best way for voltage regulation, among others, smart inverters, open soft points or transformers with on-load regulation capability. This paper proposes a novel way to model the control strategies for the automatic voltage controller of On-Load Tap-Changer transformers. The purpose is to standardize and simplify the way these strategies are represented, in order to facilitate (i) their selection by Distribution System Operators, (ii) their future integration with other systems, and (iii) to increase the ability to anticipate On-Load Tap-Changer behavior. The proposal has been validated using real data, obtaining an accuracy of 99.15% in the tap changer positions. A unified framework is also introduced, which allows the proposed functional representation to be combined with the On-Load Tap-Changer controller behavior estimation. A experimental validation has been carried out where more than 150 000 strategies have been simulated, finally determining the one that best fits the objectives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Historically, on-load tap-changing transformers have been used in high-voltage substations. However, with the integration of electric vehicles and the penetration of distributed energy resources, the need to implement this type of solution in low-voltage substations has grown. However, the characteristics and requirements are not the same, for example, given their nature, low voltage grids are more unbalanced and are often regulated to ensure the quality of supply. Therefore, inheriting the control strategies of traditional on-load tap changers may pose a serious risk. This paper proposes a novel unified framework that facilitates the modeling and simulation of almost any control strategy for distribution transformers with on-load tap changers. This will allow the choice of control parameters that minimize voltage deviation from the voltage setpoint and maximize device lifetime. Our proposal can be considered as a solution to the uncertainty of which control parameters to use. It can be performed before commissioning, based on historical data, or a posteriori, based on the collected data.

Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks

The electric vehicle deployment, due to the plans defined according to the energy transition objectives, produces new challenges for the electrical system. These challenges are associated with the charging infrastructure of these vehicles since they require a high current during specific periods, which can increase losses in the network, overload the lines, or cause voltage drops that affect the system’s stability. To solve these challenges, one of the possible solutions is the investment in new network infrastructure to face the increase in demand, such as the construction of new transformation centers or new medium and low-voltage lines. However, in the case of rural networks with a small number of users, these investments may not be viable. This article analyzes the possible impacts of connecting electric vehicles in a rural low-voltage network located in a Spanish municipality, as well as possible implementable solutions that do not require investment in new infrastructure. The number of connected vehicles has been calculated based on the national plan for 2030, and the network model used is based on actual data provided by the distribution company that operates in the area.

Scenario analysis of the socioeconomic impacts of achieving zero-carbon energy by 2030

This study uses scenario analysis to assess the socioeconomic impacts of achieving zero-carbon energy by 2030. Three scenarios are developed: 1) business as usual; 2) accelerated deployment of renewable energy and electric vehicles; and 3) scenario 2 plus comprehensive energy efficiency improvements. Quantitative models are used to evaluate the impacts on employment, productivity, consumer costs, inequality and energy security under each scenario. The results show that scenario 3, with the most ambitious decarbonization and efficiency measures, can generate the most jobs (2.1 million more than business as usual) and the lowest consumer costs (12% reduction). However, it may also lead to a small productivity loss (1.2% lower than business as usual) due to higher costs of new technologies. Income and health inequality are projected to decrease across all scenarios due to improved energy access and reduced fuel poverty. Energy security is expected to improve significantly in scenarios 2 and 3 due to reduced oil dependence. This study provides an analytical framework to assess the integrated socioeconomic impacts of zero-carbon transitions under uncertainty. The scenarios and findings can inform policymaking by highlighting the opportunities and challenges around the low-carbon transition, enabling decision makers to maximize benefits and minimize negative consequences.

Enabling mass integration of electric vehicles through distributed energy resource management systems

Global initiatives are actively progressing to integrate large numbers of electric vehicles (EVs) as part of efforts to electrify and decarbonize the transportation sector. This approach plays a crucial role in addressing global greenhouse emissions and pollution problems. However, mass integration of EVs imposes unprecedented challenges to electrical power systems, especially to their distribution grids. These challenges range from planning issues, where the traditional panning procedures are not adequate for integration of EVs and their charging stations, to operational challenges, where a huge number of dispersed EVs can cause peak load increase, voltage violations, congestions, feeder overloads, etc. Hence, despite the significant importance of maximizing the deployment of EVs, their stochastic integration can give rise to critical challenges in electric power systems, thereby impeding their subsequent expansion. Therefore, in order to properly integrate mass amounts of EVs, without causing technical violations in the electrical power system, intelligent software tools are required, to aid distribution system operators and engineers in the transition to a new era of electrified transportation. In this paper, we argue that novel software solutions called Distributed Energy Resource Management Systems (DERMSs) are a key solution for enabling a safe integration of mass amounts of EVs into emerging distribution grids. Specifically, we introduce a novel Hybrid DERMS framework, as an integration of multiple hierarchical levels of DER management solutions, and propose this solution as a suitable instrument for distribution system operators and grid engineers to safely integrate and manage high volumes of EVs within the grid infrastructure. The hypothesis is validated through a real-life use case for integration and management of high amounts of EVs into an existing distribution grid, using an industrial DERMS solution. Specifically, an in-depth analysis of adding two large charging stations, boasting capacities of 5 MW each, has been performed through all stages of integration, from the planning stage, up to using their flexible output to manage technical constraint violations in real time. The influence of these EV charging stations on both the feeder where they are planned to be integrated, as well as on the entire supply substation, have been analyzed in detail. The resultant findings, especially on successfully managing overloads, peak load increases, as well as voltage violations through intelligent use of aggregated EVs through DERMS, have proven to be notably encouraging, further substantiating the paper’s hypothesis.

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.

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

3D Progression of Dead Li on Graphite Electrodes during Extended Fast Charging

Long charging times of commercial lithium-ion batteries (LIBs) constitute one of the critical remaining bottlenecks in the widespread deployment of electric vehicles (EVs). Therefore, there is a global push towards extreme fast charging (XFC)1in 10–15 minutes for 80% of the pack capacity. However, existing LIBs cannot achieve these XFC goals without significant capacity fade during cycling over battery lifetime.2 One of the key XFC degradation mechanisms is “Li plating,” which occurs when Li metal forms on the graphite electrode due the liquid electrolyte and solid-phase (graphite) mass transport limitations and sluggish charge-transfer kinetics of the Li intercalation reaction.3 This is mostly attributed to the formation of dead Li,4 which is the irreversibly plated Li that has become electronically disconnected from the graphite electrode. Since dead Li constitutes most of the XFC-related capacity loss, it is extremely important to unravel the link between its morphological evolution/progression, and battery capacity fade during extended XFC cycling.In this work, we report non-destructive, in-situ three-dimensional (3D) characterization of dead Li on graphite electrodes in full-cell LIBs during extended XFC using high-resolution (pixel size: ~ 7.8 µm; effective spatial resolution: ~25-30 µm) neutron micro-computed tomography (µCT). Specifically, we imaged batteries in the discharged state at the following cycle numbers at 6C charging rate: 10, 25, 50, and 150 cycles. Neutron µCT is promising for in-situ 3D Li detection on graphite electrodes because it provides strong contrast between Li and C (~ 71.87: 5.55 barns5). To leverage high-resolution neutron µCT for this work, we designed a custom neutron-friendly LIB coin-cell that provided exceptional neutron transmission at the graphite-separator-electrolyte interface as well as standard XFC performance at 6C between 3.0 and 4.1 V. We performed these imaging experiments at the MARS (CG-1D) beamline6 at the High Flux Isotope Reactor, Oak Ridge National Laboratory.Our results reveal 3D evolution of dead Li morphology from isolated plated deposits on only one edge of the graphite electrode after 25 cycles at 6C charging, to increasingly mossy-like covering the entire circumference of the graphite electrode after 150 cycles. Based on our imaging data, we did not observe any dead Li after only 10 XFC cycles. However, we noticed that dead Li started to nucleate around a partial edge of the graphite electrode after 25 XFC cycles, and then grew to the other edges after 50 XFC cycles, eventually forming a complete ring after 150 XFC cycles. At 150 XFC cycles, we also observed the dead Li ring outgrowths away from the edges of the graphite electrode onto the separator. In the extended XFC-cycled samples, 50 and 150 XFC cycles, we found significant dried out salt deposits on the separator, spacers, spring, and casing, which may be due to other parasitic reactions occurring at increased XFC cycling. Lastly, we determined quantitative relationships between the amount of dead Li in 3D, XFC cycling number, and XFC-related capacity loss of the battery.In conclusion, this work contributed to the advanced understanding of the 3D progression of dead Li and its relationship with XFC-related capacity loss on graphite electrodes during extended XFC cycling. We believe insights from this work will help develop improved 3D graphite electrode architectures that will minimize Li plating during repeated XFC cycling in fast-charged LIBs.

Stabilized Positive Electrode Material to Enable High Energy/Power Density Lithium-Ion Batteries

Recently, the large-scale deployment of electric vehicles (EVs) and stationary battery systems has putforward urgent requests for rechargeable batteries with higher energy/power density and longerlifespans. As such, next-generation Li-ion batteries (LiBs) are expected to meet these requirements. Thepossibility of using Ni-rich high-capacity cathode materials (NMC) can help to achieve that goal, however,they still suffer from significant capacity fade via several chemical and mechanical degradation modes.One is crack formation within secondary particles which leads to parasitic reactions that evolve oxygenand potentially initiate the formation of inactive crystal structures. LiBs mechanical and chemicaldegradation mechanisms can be extended by applying protective coatings to the cathode’s surface. Manystudies explore atomic layer deposition (ALD) for this purpose. However, the complementary molecularlayer deposition (MLD) technique might offer the benefit of depositing flexible hybrid coatings that canaccommodate potential volume changes of the electrode during the battery cycling. This study reportsthe deposition of novel organic-inorganic films via the ALD-MLD method. Characterization analysis suchas FTIR, XPS, SEM-EDS, and TEM confirmed the film deposition on the surface of the NMC electrode.Besides, the electrochemical results showed the enhancement of the coated electrode, as evidenced byproviding a longer life with a 93% capacity retention after 150 cycles (3.0-4.4 V vs. Li/Li+), while theuncoated one retained only 83% of its initial capacity. Moreover, in situ dilatometry showed irreversiblevolume change for uncoated NMC while it was mostly reversible for coated ones during the cycling. Itrevealed the dilation behavior of the electrode, resulting in crack formation, which is significantlysuppressed for the coated samples. Stable cycling of Ni-rich cathode materials in crucial conditions suchas high voltage windows and elevated temperatures is still extremely challenging. To evaluate this, in situdilatometry was performed at elevated temperatures (40 °C and 55 °C) and higher voltage ranges (3.0-4.6V). The results confirmed that in both conditions, the dilation behavior of the coated sample is still undercontrol while the capacity is comparable to the uncoated sample. This revealed that the coatings presentgood lithium-ion kinetics and reduce electrolyte decomposition. Overall, this work showed the liability ofthe ALD-MLD films deposited on NMC electrodes to mitigate the degradation mechanisms and providesinsight into a method that seems promising as a protective and elastic coating for future high-energylithium-ion battery cathodes. Figure 1

Large-scale public charging demand prediction with a scenario- and activity-based approach

Transportation system electrification is expected to bring millions of electric vehicles (EVs) on road within decades. Accurately predicting the charging demand is necessary to accommodate the surge in EV deployment. This paper presents a novel modeling framework to predict the public charging demand profile derived from people’s travel trajectories, with the consideration of the demand and supply stochasticity of transportation systems and the charging behavior heterogeneity of EV users. The vehicle charging decision-making process is explicitly modeled, and the charging need of each EV user is estimated associated with their travel trajectories. The methodology enables charging demand prediction with a high spatial–temporal resolution for transportation system electrification planning. A case study was conducted in Los Angeles County to predict the demand for public charging facilities in 2035 and perform corresponding spatial–temporal analysis of EV public charging under various scenarios of future electrification levels and network conditions.

Spatial and Cross-Sectoral Transfer of Air Pollutant Emissions from the Fleet Electrification in China by 2030.

Fleet electrification shifts emission sources from the tailpipe to electricity generation and automotive supply chains subsequently, with emission transfer among regions. Such a spatial and cross-sectoral transfer of air pollutant emissions might embody uncertain environmental benefits spatially, which has not been comprehensively quantified, mainly due to the complexity of manufacturing processes of electric vehicle (EV) components (e.g., battery). We developed a hybrid life cycle assessment by combining inventory data of major processes and cross-sectoral input-output information and identified how China's EV deployment would influence the spatial redistribution of air pollutant emissions currently (2017) and in the future (2030). The results indicate that fleet electrification could readily reduce life cycle nitrogen oxides (NOx) and nonmethane volatile organic compound (NMVOC) emissions by 12-93%, and the reductions are estimated to be concentrated in major cities and urban agglomerations. However, increased demand for electricity and power battery production could increase PM2.5 and SO2 emissions in 17-55% of grids under all the scenarios, which emerge in coal-rich (e.g., Inner Mongolia, Shanxi) and industrial (e.g., Shandong, Henan, Jiangsu) provinces. By tracing the upstream, 31-55% of vehicle-cycle emissions are from deep supply chains but exhibit diverse sources. It suggests the necessity to relieve emissions leakage of fleet electrification by synchronizing effective environmental management across multiple sectors through EV supply chains.

Exploring the Potential of Virtual Environment for Electric Vehicle: Study Case and Sound Quality Performance

The consequent growth in the production and deployment of electric vehicles (EV), has urged a reassessment of the interior sound experience for these vehicles. To address the issue, this research employs a virtual environment (VE) with a circular array of eight loudspeakers and visual projection to recreate the soundscape of the EV cabin. The validity of the simulator is evaluated by respondents both in the real cabin and VE. Two stimuli were reproduced in VE: 1. Stimuli with the same sound pressure level (SPL) as the cabin, 2. Stimuli's SPL is lowered by 9.5 dB. Data shows that by reducing the SPL, VE implied a closer perception of the real cabin soundscape. Furthermore, T-test and Pairwise Comparisons results indicate that the VE is able to reconstruct several perception parameters of car cabin soundscape with no significant differences. Parameter classification is also done with Principal Component Analysis (PCA) to reconfirm data behavior. These outcomes confirm that VE is possible to be a tool for modifying the sound design of armored electric SUVs with requiring deliberation on some factors.