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Reducing human-made greenhouse gas emissions is crucially important for life on earth, but it requires restructuring industries in ways that could disrupt millions of workers' lives globally. Whether this transition is "just" from the perspective of workers depends on the magnitude of job losses, the quality of new jobs, and the transitions workers experience from their current jobs to new ones. Using the example of the German automotive industry, where the shift to electric vehicle production has recently accelerated, the authors identify recommendations for unions and policymakers in North America and beyond. This article provides an overview of the tools for workers and trade unions in Germany to steer the transition and shows how analogous tools could be strengthened or created elsewhere. [ABSTRACT FROM AUTHOR]

Decarbonising the transportation sector through electrification is critical to reducing greenhouse gas emissions. To explore the decarbonisation of conventional gasoline vehicle (GV) automakers in GV and/or electric vehicle (EV) production, we develop analytical models under pure subsidy, pure regulation, and hybrid subsidy-regulation policies. We first examine the monopoly GV automaker's EV market entry intention in each policy and find that it always increases with consumer range sensitivity and subsidy/regulation parameters, but decreases with product substitutability between the two vehicles. Then, we analyse the implication of policy changes on equilibrium outcomes. Our results show that although introducing regulations and removing subsidies have opposite effects on optimal production and driving range decisions, both might stimulate or restrict the GV automaker to decarbonise, depending on the unit carbon emissions of GVs and EVs. Finally, we extend the monopoly model to the duopoly case, showing that while entering the EV market hurts the EV automaker, it allows the GV automaker's carbon emissions to always decrease with GV credit coefficients. Together with sensitivity analyses on key parameters, our findings provide insights not only for GV automakers to respond to policy changes but also for governments to design incentives for EV diffusion. [ABSTRACT FROM AUTHOR]

The new energy vehicle (NEV) is emerging as an important alternative in the automobile industry in its potential to alleviate environmental pollution and contribute to carbon neutrality. The rapid growth of NEVs has been reflected in the scaling up of electric vehicle battery production. The dramatic increase of retired batteries, however, exposes the technological limitations in current recycling operations, which will ultimately impede the sustainable development of the NEV supply chain. Blockchain technology (BT) adoption provides a solution by contributing to the construction of an efficient recycling network. Our research investigates the influence of carbon reduction instruments on the uptake of BT. The key findings are as follows. Under a carbon tax system, (1) carbon emission reduction encourages the battery supplier to adopt BT; (2) BT adoption increases the profits of NEV supply chain stakeholders. Under carbon cap-and-trade regulations, (1) the unit outsourcing fee and the performance of the BT impact the investment decision of the manufacturer; (2) the profit of the third-party enterprise is increased by introducing the BT. Under both policies, improving the efficiency of BT helps to upgrade the traceability level and contribute to carbon neutrality in the NEV supply chain. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

In recent years, worldwide electric vehicle (EV) sales have experienced rapid growth, and recycling an enormous quantity of spent power batteries has become a challenge. This study aims to explore the optimal joint recycling strategy from the EV manufacturer's perspective by using game theory-based models. The model investigates four kinds of joint recycling strategies: 1) nonalliance (NA) mode, 2) alliance with the power battery manufacturer (APBM) mode, 3) alliance with the third-party recycler (ATPR) mode, and 4) integrated alliance (IA) mode. The results show that: 1) A deposit-refund system can effectively increase the recycling rate. However, an excessive upfront disposal fee will reduce overall profits. 2) For the closed-loop supply chain, the optimal alliance mode depends on the intensity of recycling competition. When recycling competition is fierce, the IA mode is the best alliance mode. When recycling competition is weak, the NA mode is the optimal choice from the recycling rate perspective, and the APBM mode is the optimal choice from the total profit perspective. 3) For the EV manufacturer, the alliance in which its proportion of the extra joint profit is highest tends to be the optimal choice. [ABSTRACT FROM AUTHOR]

Naturally occurring cell-derived extracellular vesicles (EVs) have emerged as attractive nanocarriers for drug delivery. However, production of large quantities of EVs for clinical applications in a scalable manner remains a significant challenge. This study investigated at the single cell level how sonoporation, or membrane poration produced by ultrasound-induced microbubble cavitation, impacts EV production using mouse macrophage RAW 264.7 cells stably expressing CD63-GFP as a model system. Real-time fluorescence videomicroscopy detected rapid changes in CD63-GFP, a tetraspanin family member highly enriched in intraluminal vesicles tagged with GFP, to track changes in multivesicular bodies (MVBs), which are the cellular compartments where exosomes originate within the cells. Our results revealed distinct dynamic changes in CD63-GFP intensity and distribution in RAW 264.7 cells in terms of response time and duration depending on whether the cells were directly or indirectly impacted by sonoporation, suggesting reorganization of MVBs in response to direct and indirect mechanisms resulted from the mechanical impact of ultrasound pulse on the cells. Analysis of the supernatant from sonoporation-treated RAW 264.7 cells expressing CD63-GFP demonstrated a delayed and sustained increase in the production of CD63-GFP-positive EVs. These results show the robust and detailed effect of sonoporation and reveal insights into sonoporation-induced EV release useful for guiding the application of sonoporation to enhance large-scale EV production. [ABSTRACT FROM AUTHOR]

Motivation Purpose Methods and approach Findings Policy implications Strategies to develop manufacturing industry are the subject of lively debate over whether to follow or defy national comparative advantage. Electric vehicles (EVs), a major change for automobile manufacturers, are no exception.Despite the US government's promotion of global integration to activate the semi‐invisible hand of market forces, neither traditional Detroit automakers nor new firms, even with government grants or loans, were able to launch EVs on the world market promptly, with the notable exception of Tesla. In China, the government helped dozens of domestic electric vehicle manufacturers enter the market through formidable government subsidies. However, none of China's manufacturers, other than Wuling, were able to surpass Tesla in sales in China.The rapid growth of Tesla and Wuling without relying heavily on government protection and subsidies raises the question of whether their success can be attributed to other forms of state intervention.What policies and strategies enable manufacturers to develop and market a new technology, such as EVs? What can we learn by comparing the experiences of US and Chinese vehicle manufacturers?We consider these questions and what the implications are for debates about industrial development strategy.We compare four carmaking firms—GM, Tesla, BYD, and Wuling—their business strategies and their interactions with the US and Chinese states. We draw on company reports, government documents, and press reports.Both China and the US employed semi‐developmentalist, semi‐neoliberal policies to promote the manufacture of EVs. The US subsidized research and development in its automobile industry and provided incentives to consumers. It has subsequently moved to lending to the most promising firms for EV production. China, in contrast, has selectively subsidized EV firms all the way from development to marketing.Subsidies from the US government have been steady: they are expected to stay this way in the near term. However, the fastest‐growing carmakers have used outward foreign direct investment to gain competitive advantage. China's subsidies, on the other hand, have been unstable: linked to the performance of the target firms. The higher the firm's profit, the lower the subsidies and vice versa. China has been careful with subsidies to prevent rent seeking and moral hazard.Neither following nor defying comparative advantage explains the strategies followed by the four firms: comparative advantage considerations have been tempered by the possibilities of foreign investment.The results imply that traditional developmental strategies have resorted to inward foreign direct investment in high‐income countries as a means to learn technologies and outward foreign direct investment in low‐ and medium‐income countries as a way to reduce production costs, but have missed the comparative advantage of outward and inward foreign direct investment, and strategic mixes of these, when developing and diffusing radical innovation like EVs. [ABSTRACT FROM AUTHOR]

Current mobility trend indicates that the number of private cars will decline in the near future. One of the reasons for this trend is the development of Mobility as a Service (MaaS), which in conjunction with information and communication technologies (ICT) drive the application of transport services in smart city, respond to environmental issues, and provide users with reliable mobility. Electric vehicle sharing (EVS) travel has been regarded as a feasible mainstream model of sustainable mobility services in the future, which can effectively improve the utilization rate of motor vehicles, solve the problems of traffic congestion, environmental pollution and urban land, and promote low-carbon and sustainable development. To help electric vehicle operators improve service quality, the establishment of EVS program service performance evaluation is an urgent problem to be solved. Based on this, this paper firstly constructs the evaluation index system from 5 aspects: electric vehicle, charge station, user experience, payment and intelligent services through literature review and Delphi method. Secondly, the criteria importance though intercriteria correlation (CRITIC) and the improved G1 method are introduced to overcome the shortcomings of the single method, and the combined weights are calculated by the multiplication normalization method. Finally, a decision model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIse Kriterijumski Optimizacioni Racun (VIKOR) method is constructed to select the best service performance of EVS program, and its feasibility and effectiveness are verified by sensitivity analysis and comparative analysis. The result shows that EVCARD is the best performing EVS program, and shared electric vehicle and charge station are the key factors to be considered in the selection. This study provides scientific and feasible guidance for the optimal service performance selection of EVS programs, which is of great significance for users to choose EVS programs. [ABSTRACT FROM AUTHOR]

AbstractThe burgeoning proliferation of infections attributed to multidrug-resistant (MDR) bacterial pathogens is profoundly undermining conventional chemotherapeutic modalities, portending a grave menace to global public health. The propagation of drug resistance among bacteria is fundamentally facilitated by bacterial interactions, with extracellular vesicles (EVs) assuming a critical role in interbacterial communication. Here, we briefly delineate the methodologies for isolation, extraction, and characterization of EVs from both Gram-negative and Gram-positive bacterial origins. We further investigate assorted methodologies to augment EV production, embracing physical stimulation, chemical elicitation, and genetic engineering. Moreover, we expound on the pivotal involvement of EVs in the facilitation of bacterial drug resistance proliferation and anticipate future trajectories of research and application potential. This overview of EV-mediated novel mechanisms of horizontal gene transfer implicated in antibiotic resistance among bacteria aims to obstruct the transmission conduits of bacterial drug resistance and thus fortify public health integrity. [ABSTRACT FROM AUTHOR]

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This study investigates the evolutionary game dynamics among electric vehicle distribution entities in the context of shared charging facilities, addressing the critical issue of inadequate charging resources. To understand the behavior of different stakeholders under government incentive policies, we develop an evolutionary game model involving a government department and two logistics enterprises (A and B). Through stability analysis, we explore equilibrium conditions of evolutionarily stable strategies (ESSs) for the tripartite evolutionary game. To ensure the robustness of our findings, we conduct a MATLAB simulation analysis to validate the analytical results. Our findings highlight that government subsidies, the costs incurred by logistics enterprises to share charging facilities, and the additional distribution income derived from this sharing are critical in determining whether the evolutionary game can achieve a stable equilibrium state. This research enables logistics companies to optimize the use of charging resources, lower operating costs, and enhance delivery efficiency. Additionally, government subsidy policies play a crucial role in encouraging logistics enterprises to engage in charging facility sharing, thereby fostering the sustainable development of the entire logistics industry. Based on these insights, the paper offers practical recommendations to further promote the sharing of charging facilities in electric vehicle distribution. [ABSTRACT FROM AUTHOR]

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the "curse of dimensionality" and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services. [ABSTRACT FROM AUTHOR]

Amidst the swift expansion of the electric vehicle industry, the imperative for alternative battery technologies that balance economic feasibility with sustainability has reached unprecedented importance. Herein, we utilized Perovskite-based oxide compounds barium titanate (BaTiO3) and strontium titanate (SrTiO3) nanoparticles as anode materials for lithium-ion batteries from straightforward and standard carbonate-based electrolyte with 10% fluoroethylene carbonate (FEC) additive [1M LiPF6 (1:1 EC: DEC) + 10% FEC]. SrTiO3 and BaTiO3 electrodes can deliver a high specific capacity of 80 mA h g−1 at a safe and low average working potential of ≈0.6 V vs. Li/Li+ with excellent high-rate performance with specific capacity of ~90 mA h g−1 at low current density of 20 mA g−1 and specific capacity of ~80 mA h g−1 for over 500 cycles at high current density of 100 mA g−1. Our findings pave the way for the direct utilization of perovskite-type materials as anode materials in Li-ion batteries due to their promising potential for Li+ ion storage. This investigation addresses the escalating market demands in a sustainable manner and opens avenues for the investigation of diverse perovskite oxides as advanced anodes for next-generation metal-ion batteries. [ABSTRACT FROM AUTHOR]

Apartments account for 64.6% of all housing units in the Republic of Korea, and most of them receive electricity under a contract, which includes a progressive rate plan. Recently, due to the electrification of energy used in homes and the growing adoption of electric vehicles, electricity consumption in apartment complexes has been gradually increasing. Given the characteristics of the progressive rate system, an increase in electricity usage results in a significant higher rise in electricity bills. Thus, an effective alternative is required to reduce electricity bills for each household. In this paper, the savings in electricity bills achieved by reducing household electricity usage are analyzed from both apartment complex and individual household perspectives, using metering data from 13,332 households. When households are sorted by the amount of savings in descending order, the resulting values are found to follow a negative exponential curve. This indicates that the benefits from reducing electricity usage in households with higher saving are significantly larger compared to other ones. We analyzed bill savings when electricity usage reductions were selectively applied to the top 10%, 20%, and 30% of households with the largest savings. From the results, it is found that the largest savings in electricity bills for households are achieved when usage reductions are applied to the top 10% of households. It is expected that this amount of savings would encourage these households to reduce their electricity consumption. Additionally, it is found that the savings for apartment complexes and the total savings for selected households are not the same, resulting in changes in the bills for households that do not reduce their usage. From the results, it was observed that when the usage reduction of selected households is small or the proportion of households reducing usage is low, the common area charges for non-reducing households tend to increase, leading to higher electricity bills. On the contrary, when the usage reduction of selected households is large or the proportion of households reducing usage is high, the common area charges for non-reducing households tend to decrease, resulting in lower electricity bills. [ABSTRACT FROM AUTHOR]

The transition to electric vehicles (EVs) plays a pivotal role in achieving decarbonization within the transportation sector. However, the widespread adoption of EVs faces multifaceted challenges, particularly concerning infrastructure development. This paper investigates the intersection of sustainability, decarbonization, and EV adoption, with a focus on identifying and analyzing the challenges associated with infrastructure deployment. Strictly adhering to the methodological principles and process of systematic literature reviews, this paper analyzes research spanning the fields of engineering, energy, computer science, environmental science, social sciences, and others to elucidate the barriers hindering EV adoption, ranging from technological limitations to regulatory complexities and market dynamics. Furthermore, it examines the critical role of infrastructure, encompassing charging networks, grid integration, and supportive policies, in facilitating EV uptake and maximizing environmental benefits. The findings are finally used to present the implications for theory, practice, and policies and to highlight the avenues for future research. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the relationship between the operating parameters of electric vehicles (EVs) and parameters related to road conditions. The data for analysis came from urban driving trips conducted at different times of day. The average energy consumption was the dependent variable in the model. The following parameters were used as independent variables: the stopping time;; the journey time; the average speed; the quartiles of speed, acceleration, and deceleration, and their maximum values; the number of braking; stops; and the acceleration time and regenerative braking. A multiple linear regression model was developed to predict the average energy demand of an EV based on its kinematic parameters. The proposed model enables the analysis of the influence of various factors related to the route and driving style of the driver on the average energy consumption in the vehicle. [ABSTRACT FROM AUTHOR]

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total. [ABSTRACT FROM AUTHOR]

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method's effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm's efficacy for real-world applications. [ABSTRACT FROM AUTHOR]

In light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry. [ABSTRACT FROM AUTHOR]

The global electric vehicle (EV) market overgrew in the previous decade. This paper investigates the factors affecting EV purchase intention in the West Bank, Palestine. This study adopts the exploratory sequential mixed methods approach by conducting unstructured interviews and questionnaires in a developing country context. We obtained 384 survey responses from EV owners and non-EV owners – this study used the partial least squares structural equation modeling (PLS-SEM) tool for empirical analysis. The study results show that environmental concerns, subjective norms, cognitive status, incentive policies, and product perception significantly affect consumers' intentions to purchase EVs in the West Bank. Environmental concerns indirectly correlate with consumers' intentions to purchase EVs through attitudes as a mediator. However, perceived behavior control has no significant impact on purchasing intent. These results will help policymakers in improving transportation policies. [ABSTRACT FROM AUTHOR]

This study examines the factors that impact the purchase intention of electric vehicles (EVs) in emerging economies like India. By employing the Push, Pull, and Mooring (PPM) theory, this study contributes to the existing literature by shedding light on the factors influencing purchase intention of EVs. In the first stage of the study, twitter text mining was conducted to extract the main topics discussed about EVs. The second stage involved a focus group discussion to condense the identified topics into key factors influencing the purchase intention of EVs. Finally, choice-based conjoint analysis was employed in the third phase to comprehend the utility and prioritize the identified factors. Additionally, topic modelling can be valuable for marketing managers, product managers, and branding specialists in developing a brand image and positioning for EVs. Furthermore, the findings of this study can assist policymakers in creating regulatory frameworks to support the development of EV infrastructure. [ABSTRACT FROM AUTHOR]

In polymer matrix composites (PMCs), reinforced lignocellulosic fibres are one of the excellent endeavours; doing so eliminates the need for the more commonplace synthetic fibres. In this respect, the fibres from Enset ventricosum (EV), one of the underutilized which have not been studied extensively, were focused to carry out an investigation on PMCs applications using the particles of Limonia acidissima fruit shell powder (LASP) as reinforcing agent. The study set out to evaluate the adeptness of altered LASP and EV plant fibres by 4% NaOH treatment. The results from morphological, physicochemical, XRD, FTIR, and thermal aspects of alkali-treated samples of LASP and EV fibres revealed that the alkali treatment significantly improved the compatibility of biomaterial' property to utilize the natural fillers in the epoxy–EV fibre composites. The first-degree polynomial model was fitted using the response surface analysis to optimize the impact energy, water absorption, tensile, and flexural strength of reinforced fibre with respect to composition and fibre length. Using RSM numerical model, aforementioned properties were analysed to develop the ideal epoxy–EV fibre composite for attaining a minimal water absorption, a high tensile modulus, flexural strength, and impact energy. Accordingly, 3 mm of fibre length reinforcement with 38.3 wt % of biomaterials loading reinforcement was found to be optimized for idealistic epoxy–EV fibre composite. [ABSTRACT FROM AUTHOR]

Over the past decade, as consumers have gradually adapted to electric vehicles, the production and sales of electric vehicles have grown in tandem, and the deployment of charging facilities has increased dramatically. However, along with the rapid development in scale, charging facilities face problems such as low utilisation during off-peak hours and excessive congestion during peak hours. Therefore, research on the spatial and temporal distribution of charging station loads is of great value. In this context, this study focuses on the spatio-temporal distribution characteristics of charging station loads, especially when analysing the spatial distribution characteristics. This study defines the spatially dependent characteristics of short and long distance. In order to be closer to real charging scenarios, this study considers the influence of road network information and point-of-interest data on the spatial distribution of loads. The spatial and temporal variations of loads and influencing factors are considered more comprehensively and richly, and the spatial characteristics are delineated in detail. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, a new machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation. [ABSTRACT FROM AUTHOR]

In recent years, the surge in the adoption of electric vehicles has played a vital role in reducing fossil fuel consumption and greenhouse gas emissions. However, limited cross-national research has been conducted on the determinants of electric vehicle adoption in developing and developed countries. This study examines the factors influencing the intention to adopt electric vehicles in India (378 participants) and Spain (265 participants). This study develops an integrated model that combines the unified theory of acceptance and use of technology (UTAUT2) and the value-belief-norm (VBN) model while accounting for the impact of national culture. The model is tested using structural equation modeling. The results indicate the integrated UTAUT2-VBN model is a valuable tool for explaining the differences in adoption intention across cultures. Moreover, the national cultural system plays a significant moderating role in most relationships within the model. This study offers valuable insights into the factors influencing electric vehicle adoption in different cultural contexts, which can inform policies and strategies to promote sustainable transportation. [ABSTRACT FROM AUTHOR]

Electric vehicles are subsidized at the federal, state, and local level. The design of these subsidies affects which consumers benefit most and where less-polluting vehicles are driven. We study these effects using a panel of new vehicle purchases at the zip-code level for seven US metro areas. We first describe patterns of neighborhood-level electric vehicle adoption and subsidy receipt. Adoption has been spatially uneven and concentrated in more advantaged and less polluted neighborhoods. We then pair a structural model of vehicle demand with a model of on-road emissions to study the effects of electric vehicle subsidy targeting for the Chicago metro area. A neighborhood income-targeted policy achieves lower overall reductions in carbon emissions but higher reductions in criteria pollution (NOx and PM2.5) in lower-income neighborhoods. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1. [ABSTRACT FROM AUTHOR]

Variations in oxygen level affect the phenotype of cells and extracellular vesicles (EVs). Depending on the metabolic oxygen demand of cells, hypoxic cell culture can produce conditions more like those found in vivo, and with appropriate oxygen levels, mimic hypoxic tumours. However, most previous experiments studying both EVs and the effects of hypoxia on cells use periods of 72 h or less of hypoxia. We hypothesised that this was insufficient time for adaptation to hypoxic conditions both for EVs and cells which may skew the results of such studies. In this study, the effects of acute (72 h) and chronic hypoxia (> 2 weeks) on the phenotype of HepG2 and PC3 cells and their EVs were examined. Cells could be cultured normally under chronic hypoxic conditions and cryopreserved and recovered. The effects of hypoxia on EV phenotype are slow to establish and dependent on cell line. In PC3 cells, the greatest change in phenotype and increase in EV production occurred only with chronic hypoxic culture. In HepG2 cells, the number of EVs produced was insensitive to hypoxic culture and the greatest changes in protein expression were observed after acute hypoxic culture. Nonetheless, biphasic changes in EV phenotype were detected in both cell types in response to either acute or chronic hypoxia. These results indicate that for cells which do not induce consumptive oxygen depletion, prolonged hypoxic culture is required for complete adaptation. [ABSTRACT FROM AUTHOR]

Conventional fast‐charging using a high constant current can ultimately accelerate uncontrolled Li plating on the graphite anode, resulting in degradation and poor cycle life of Li‐ion batteries (LIBs). Therefore, identifying suitable fast‐charging methods for LIBs is beneficial for the widespread use of electric vehicles (EVs). Herein, an innovative fast‐charging protocol, designed by combining an active control pulse (ACP) with a multi‐step fast‐charging method (M‐ACP), is proposed to mitigate issues related to Li plating and byproduct formation on the graphite anode during fast‐charging. Because M‐ACP not only reduces charging time but also actively prevents Li plating on the graphite anode surface, the 60 Ah pouch cell used in EVs that employ the M‐ACP protocol exhibits an extended lifetime with no capacity fading over 2000 cycles. The degradation behavior of the negative electrodes, which is strongly correlated to the improved performance of M‐ACP cells, is demonstrated using non‐destructive diagnostic techniques and post‐mortem analyses. The M‐ACP cell exhibits significantly less active material and Li loss, and decreased solid electrolyte interphase layer growth on graphite, owing to effective mitigation of Li+ accumulation at the electrode/electrolyte interface. Consequently, the distinguished fast‐charging protocol effectively decelerates the degradation of the negative electrode, leading to outstanding electrochemical performance. [ABSTRACT FROM AUTHOR]

It has been widely suggested that the transition to battery electric vehicles will require 30% fewer assembly workers than those needed for internal combustion engine vehicles. Here, we use publicly available datasets on vehicle production and employment to show that labor intensity has increased at U.S. vehicle assembly plants that have fully transitioned to assembling battery electric vehicles. During the production ramp-up period, labor intensity increases by more than ten-fold compared to historic combustion vehicle assembly labor intensity. For one assembly site studied, labor intensity and total employment remained three times higher after a decade of electric vehicle production. Our study suggests that it may take longer than 15 years for electric vehicle assembly sites to achieve labor intensity parity with internal combustion vehicle assembly. Thus, rapid widespread loss of employment at vehicle assembly plants is a smaller risk than many fear. Moreover, our study calls for more regionally focused analyses of the transition's effects on labor using data-driven and macro-level surveying approaches. This study finds that U.S. vehicle assembly plants that have fully transitioned from gas engine to electric vehicle assembly have required higher labor intensity. It suggests that more, not fewer, workers are needed for electric vehicle assembly. [ABSTRACT FROM AUTHOR]

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers' routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood. [ABSTRACT FROM AUTHOR]

Given the increasing environmental concerns and energy consumption, the transformation of the new energy vehicle industry is a key link in the innovation of the energy structure. The shift from traditional fossil fuels to clean energy encompasses various dimensions such as technological innovation, policy support, infrastructure development, and changes in consumer preferences. Predicting the future ownership of electric vehicles (EVs) and then estimating the energy demand for transportation is a pressing issue in the field of new energy. This study starts from dimensions such as cost, technology, environment, and consumer preferences, deeply explores the influencing factors on the ownership of EVs, analyzes the mechanisms of various factors on the development of EVs, establishes a predictive model for the ownership of motor vehicles considering the penetration of electric vehicles based on system dynamics, and then simulates the future annual trends in EV and conventional vehicle (CV) ownership under different scenarios based on the intensity of government funding. Using energy consumption formulas under different power modes, this study quantifies the electrification energy demand for transportation flows as fleet structure changes. The results indicate that under current policy implementation, the domestic ownership of EVs and CVs is projected to grow to 172.437 million and 433.362 million, respectively, by 2035, with the proportion of EV ownership in vehicles and energy consumption per thousand vehicles at 28.46% and 566,781 J·km−1, respectively. By increasing the technical and environmental factors by 40% and extending the preferential policies for purchasing new energy vehicles, domestic EV ownership is expected to increase to 201.276 million by 2035. This study provides data support for the government to formulate promotional policies and can also offer data support for the development of basic charging infrastructure. [ABSTRACT FROM AUTHOR]

ABSTRACT In recent years, in order to alleviate global environmental problems, renewable energy power generation and the electric vehicle industry have been vigorously promoted by many countries. During this background, many scholars have proposed the development of vehicle‐to‐grid (V2G) systems to alleviate the impact of renewable energy generation on grid quality, and electric vehicle charging piles are the core part of realizing V2G systems. This article proposes an ultra‐high voltage AC/DC isolated matrix converter applied to V2G electric vehicle charging piles, which can achieve bidirectional flow of energy, and proposes the control strategy that can reduce the current stress of the device. In addition, the ultra‐high voltage module of the proposed topology can provide wide voltage range and high voltage charging power for different types of electric vehicles. The proposed topology and control strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles. [ABSTRACT FROM AUTHOR]

With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in this context, this paper plans the demand response management framework of electric vehicles participating in the regional power grid under the battery swapping mode from the first time. On this basis, the time distribution of battery-swapping demand was proposed by the time series analysis model of different vehicle types of electric vehicles. Then, in order to reduce the peak-valley load difference in the regional power grid as the optimization management goal, the charging schedule optimization scheduling model of electric vehicles participating in the demand response of the regional power grid under the battery swapping mode was constructed. The case analysis shows that under the battery swapping mode, by participating in the demand response through the optimal management and scheduling of the charging load of the power battery, can help the grid balance the contradiction between supply and demand in the peak and valley and promote the full consumption of new energy. [ABSTRACT FROM AUTHOR]

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies. [ABSTRACT FROM AUTHOR]

The decarbonization of transport is a key goal facing climate change. The electrification of the powertrain for passenger cars is part of this goal to reduce carbon emissions. This involves a big change in the global supply chain, specifically in countries with a high weight of the traditional automotive sector, such as Spain, where above 10% of the GDP comes from this industry. There is a forecasted shift from the sector of traditional automotive parts to the electric sector, where batteries and electric components will be the major part of the powertrain. This work evaluates socioeconomic and environmental impacts of the changes in the car industry from the ramp‐up of the electric vehicles market in Spain, and also in the European Union and the rest of the world. To do it, we use an environmentally extended multiregional and multi‐sectoral input–output model. Our simulations include the technological change and demand shifts estimated to achieve the penetration of electric vehicles up to 2030 and 2050. The results show significant impacts on employment and economic indicators by 2050, when the share of electric vehicles is expected to increase up to a relevant level. [ABSTRACT FROM AUTHOR]

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users' charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users' charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users' willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users' implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users' feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction. [ABSTRACT FROM AUTHOR]

A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions. [ABSTRACT FROM AUTHOR]

Summary: At present taking into account the situation of the environment and the growing demand of fossil fuels, electric vehicles (EVs) are a highly promising option for the transportation system. Many manufacturers of electric vehicles employ BLDC motors as the vehicle's driving component. In contrast to the many advantages that BLDC motors offer, one of the major drawbacks of employing these motors is the production of relatively high distortion in electromagnetic torque that is created by commutation. This distortion in torque has an effect on the vehicle's performance. In this proposed work a BLDC motor is powered by a cutting‐edge, highly gained q‐ZSI Ultra‐Extended Boost Active Switch Controlled Based Quasi Z‐source Inverter, or UEBASC‐qZSI. In the aforementioned drive, the torque distortion is minimized with the use of a modified direct torque control approach. With the aid of this UEBASC‐qZSI, the efficiency of EVs can be improved, and the overall safety of the driving cycle is also increased. Any vehicle's efficiency can be enhanced with the help of this UEBASC‐qZSI, and the overall safety of the overall system can also be improved. To validate the efficacy of the system, a Simulink model is developed in the MATLAB platform. Also, a hardware prototype model is developed for experimental authentication. The modeling and investigational findings demonstrate that the proposed system has significantly reduced the torque ripple while obtaining the necessary input dc bus voltage. [ABSTRACT FROM AUTHOR]

Demand-side management of smart grid with electric vehicles (EVs) is overviewed in this review paper. The major objective of the work is to reduce the hourly peak load to obtain a steady load schedule, maximize user satisfaction and reduce cost. This review allows for the probability of leveling the everyday energy load arc and unstable demand response to hourly prices from one time period to another. To obtain a balanced everyday load schedule, increase user satisfaction, and cut costs, the main aim is to reduce peak hourly load. A management system for an EV connected to the national grid for a future household with controllable electric loads. The approach that has been presented enables the integration of EVs and renewable resources while also optimizing the demand and generation in hourly distribution. The agents are taken into account for managing load, storage, and generation; specifically, they are EV aggregators. The vehicle-to-grid (V2G) combination of electric vehicles is a key aspect of this study; with this capability, EVs may offer power grid-specific services like load shifting and congestion management. By maximizing the hourly distribution of demand as well as generation, accounting for technical limitations, and enabling the addition of EVs and RES. [ABSTRACT FROM AUTHOR]

The goal is to develop an adaptive energy management method that can predict driving circumstances, reducing stress on the primary storage battery in a hybrid electric vehicle (HEV). To improve battery lifespan and overall efficiency, power allocation among multiple energy storage sources (ESS) must go beyond traditional rule-based schemes. This paper offers a promising solution to address these challenges. Standardized driving cycles, such as the Urban Dynamometer Driving Schedule (UDDS), are used as load torque to evaluate the power demand in hybrid electric vehicles (HEV). First, feature extraction techniques improve the extraction of important features from historical driving cycle data, thereby facilitating the long short-term memory (LSTM) based on multistep velocity predictors using a warm-up algorithm. This predicted future velocity profile serves as input to the HEV system. Second, within the hybrid electric vehicle (HEV) system, the battery is linked to a modified interleaved DC-DC boost converter. The results show that this converter affects factors such as ripple, converter loss of 29%, state of charge (SOC) level, and efficiency improvement to 98%. Finally, the energy management system (EMS) with the modified rule-based scheme is implemented with neural networks for optimization. This EMS is responsible for continuously adjusting the power allocation. Simulation results show a significant average reduction of about 33.33% at peak battery level, thereby extending battery life. Compared with conventional rule-based EMS methods, the proposed strategy significantly improves efficiency by reducing the total energy loss by about 10.36%. These results emphasize the reliability and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells (MSCs) hold immense therapeutic potential due to their regenerative and immunomodulatory properties. However, to utilize this potential, it is crucial to optimize their in vitro cultivation conditions. Three-dimensional (3D) culture methods using cell-laden hydrogels aim to mimic the physiological microenvironment in vitro, thus preserving MSC biological functionalities. Cellulosic hydrogels are particularly promising due to their biocompatibility, sustainability, and tunability in terms of chemical, morphological, and mechanical properties. This study investigated the impact of (1) two physical crosslinking scenarios for hydrogels derived from anionic cellulose nanofibers (to-CNF) used to encapsulate adipose-derived MSCs (adMSCs) and (2) physiological culture conditions on the in vitro proliferation, differentiation, and extracellular vesicle (EV) production of these adMSCs. The results revealed that additional Ca2+-mediated crosslinking, intended to complement the self-assembly and gelation of aqueous to-CNF in the adMSC cultivation medium, adversely affected both the mechanical properties of the hydrogel spheres and the growth of the encapsulated cells. However, cultivation under dynamic and hypoxic conditions significantly improved the proliferation and differentiation of the encapsulated adMSCs. Furthermore, it was demonstrated that the adMSCs in the CNF hydrogel spheres exhibited potential for scalable EV production with potent immunosuppressive capacities in a bioreactor system. These findings underscore the importance of physiological culture conditions and the suitability of cellulosic materials for enhancing the therapeutic potential of MSCs. Overall, this study provides valuable insights for optimizing the in vitro cultivation of MSCs for various applications, including tissue engineering, drug testing, and EV-based therapies. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are lipid nanoparticles and play an important role in cell‐cell communications, making them potential therapeutic agents and allowing to engineer for targeted drug delivery. The expanding applications of EVs in next generation medicine is still limited by existing tools for scaling standardized EV production, single EV tracing and analytics, and thus provide only a snapshot of tissue‐specific EV cargo information. Here, we present the Snorkel‐tag, for which we have genetically fused the EV surface marker protein CD81, to a series of tags with an additional transmembrane domain to be displayed on the EV surface, resembling a snorkel. This system enables the affinity purification of EVs from complex matrices in a non‐destructive form while maintaining EV characteristics in terms of surface protein profiles, associated miRNA patterns and uptake into a model cell line. Therefore, we consider the Snorkel‐tag to be a widely applicable tool in EV research, allowing for efficient preparation of EV standards and reference materials, or dissecting EVs with different surface markers when fusing to other tetraspanins in vitro or in vivo. [ABSTRACT FROM AUTHOR]

With the development of the economy, many regions have experienced a slowdown in economic growth. In order to promote the development of the electric vehicle (EV) industry, the country has also begun to introduce various policies to encourage the development of the EV industry. In this context, many local governments have begun to introduce policies and measures related to the development of the EV industry, such as increasing land use for the development of the EV industry and increasing support for the new energy automobile industry. These policy measures have played a positive role in promoting the development of the EV industry, but there are also some problems. For example, when many local governments introduce policies to support the development of the new energy automobile industry, their support for the EV industry is not significant. This article studied the collaborative optimization of the EV industry chain in response to issues such as insufficient technical strength, imbalanced supply-demand relationship, and insufficient downstream service chain capabilities in the EV industry chain. This article analyzed the composition of the EV industry chain and established an EV industry chain model to address these issues. This article used collaborative optimization algorithms to analyze the production volume of EVs in the EV industry chain, as well as the comprehensive efficiency, pure technical efficiency, and scale efficiency values of upstream, midstream, and downstream. Through experimental analysis, it was found that the comprehensive efficiency value of the upstream of the EV industry chain after using the collaborative optimization algorithm was 0.0792 higher than before. The research results of this article have provided reference significance for the analysis of collaborative optimization algorithms in other fields. [ABSTRACT FROM AUTHOR]

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots. [ABSTRACT FROM AUTHOR]

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel deep learning-based EV charging load prediction framework to assess the impact of EVs on the MES. First, to model the EV traffic flow, a modified weight fusion spatial–temporal graph convolutional network (WSTGCN) is proposed to capture the inherent spatial–temporal characteristics of traffic flow. Specifically, to enhance the WSTGCN performance, the modified residual modules and weight fusion mechanism are integrated into the WSTGCN. Then, based on the predicted traffic flow, an improved queuing theory model is introduced to predict the charging load. In this improved queuing theory model, special consideration is given to subjective EV user behaviors, such as refusing to join queues and leaving impatiently, making the queuing model more realistic. Additionally, it should be noted that the proposed charging load predicting method relies on traffic flow data rather than historical charging data, which successfully addresses the data insufficiency problem of newly established charging stations, thereby offering significant practical value. Experimental results demonstrate that the proposed WSTGCN model exhibits superior accuracy in predicting traffic flow compared to other benchmark models, and the improved queuing theory model further enhances the accuracy of the results. [ABSTRACT FROM AUTHOR]