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This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions. [ABSTRACT FROM AUTHOR]

This study addresses the challenging real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem in electric carsharing services. The complexity arises from ad-hoc demand, charging requirements of electric vehicles (EVs), and staff scheduling constraints. The problem aims to maximize the profit of carsharing operators by determining strategies for vehicle relocation, vehicle charging, and staff rebalancing in real-time. It considers the uncertainty of demand and the practical nonlinear charging profile of EVs. We formulate this problem as a Markov Decision Process (MDP), and propose an efficient concurrent-scheduler-based policy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed policy and methodology. The results show that the proposed policy significantly improves service level and profitability compared to a benchmark policy. It is also found that ignoring staff rebalancing in decision making can lead to overestimation of service level and profitability. In conclusion, this study presents a real-time solution for vehicle relocation and staff rebalancing in one-way electric carsharing services. The proposed policy and methodology improve performance and highlight the importance of considering staff rebalancing in decision making. [ABSTRACT FROM AUTHOR]

The integration of a composite of porous medium with phase change material (PCM) offers significant advantages in thermal management systems, enhancing heat transfer efficiency and addressing various thermal regulation challenges. This approach utilizes the PCM's latent heat absorption and the enhanced thermal conductivity provided by the porous medium, resulting in optimized system performance. Its applicability spans across electronics cooling and building insulation systems. However, predicting the thermal behavior of this composite material is challenging, necessitating computational tools to anticipate its response under different conditions and evaluate its influence on cooling strategies. The objective of this study is to create a computational tool specifically tailored to evaluate constitutive parameters of this composite material, thereby providing a comprehensive description of its thermal behavior. To achieve this goal, the multiscale homogenization principle is employed to assess the composite's effective thermophysical material properties using the representative volume element approach. The repeating unit cell of the aluminum lattice is incorporated into the PCM to define a representative volume element. The finite element method (FEM) is utilized to solve the three-dimensional homogenization problem, yielding an orthotropic effective thermal conductivity due to the inherent symmetry of the repeating material cell. Moreover, the study leverages the apparent heat capacity method to effectively manage the phase transitions within the PCM domain, utilizing smooth and temperature-dependent functions to accurately describe the thermophysical properties of the PCM. Integrating the composite into battery pack thermal management, this study thoroughly examines thermal dynamics by comparing outcomes with and without PCM integration. The transient thermal problem is accurately tackled using the FEM, employing the evaluated effective constitutive parameters of the homogenized composite to minimize computational effort. The results indicate a notable decline in the highest temperatures of the battery pack, leading to a reduction of about 14 °C at the specific moment when the phase change material fully transitions into its liquid form. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time. [ABSTRACT FROM AUTHOR]

The modern quest for sustainable cities increasingly relies on using distributed energy resources (DERs), which requires new planning practices. This paper proposes an optimisation strategy to solve the fast charging station (FCS) allocation of electric vehicles (EVs). A mixed-integer programming (MIP) model minimises investment and operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The embedded set covering problem has special aspects that allow the development of a novel approach to evaluate candidate sites to accommodate FCSs. A preprocessing strategy is developed to fine-tune the entire solution space. A multiobjective approach is used to obtain an optimal compromise solution for the MIP model when it is required to serve the maximum number of EV owners at the lowest possible cost. The combined strategies reduce the computational burden, allowing full-scale studies of EV charging system planning. The results of studies using a real-world Brazilian case certify the benefits of the proposed strategy in the FCS allocation problem and in optimising the operation when considering renewable alternatives. [ABSTRACT FROM AUTHOR]

This study delves into the energy and emissions impacts of Shared Autonomous and Electric Vehicles (SAEVs) on disadvantaged communities in California. It explores the intersection of evolving transportation technologies—electric, autonomous, and shared mobility—and their implications for equity, energy consumption, and emissions. Through high-resolution spatial and temporalanalyses, this research evaluates the distribution of benefits and costs of SAEVs across diverse populations, incorporatingenvironmental justice principles. Our quantitative findings reveal that electrification of the vehicle fleet leads to a 63% to 71% decrease in CO2 emissions even with the current grid mix, and up to 84%-87% under a decarbonized grid with regular charging. The introduction of smart charging further enhances these benefits, resulting in a 93.5% - 95% reduction in CO2 emissions. However, the distribution of these air quality benefits is uneven, with disadvantaged communities experiencing approximately 15% less benefits compared to more advantaged areas. The study emphasizes the critical role of vehicle electrification and grid decarbonization in emissions reduction, and highlights the need for policies ensuring equitable distribution of SAEV benefits to promote sustainable and inclusive mobility.View the NCST Project Webpage

The global demand for high energy density batteries, mostly for application in electric vehicles, offering increased durability, safety, and sustainability is growing rapidly. In the past, this demand has been met primarily by the development and/or improvement of new/established battery materials and technologies. The high-entropy design concept—aiming at increasing chemical complexity/occupational disorder—has recently been introduced into the field of electrochemical energy storage. Various high-entropy battery materials that are seemingly capable of outperforming low-entropy counterparts by offering desirable properties have been reported. However, future studies are required to explore if the concept is broadly applicable and can be extended to all types of battery materials, especially those that are of industrial relevance. Herein, we provide a brief overview of the existing high-entropy anodes, cathodes, and solid/liquid electrolytes for use in rechargeable Li- or Na-ion batteries and discuss potential research directions and opportunities. [ABSTRACT FROM AUTHOR]

The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, the researchers consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. The project examines two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a “Small Vehicles” scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a “Large Vehicles” scenario with 4 million of each category. The authors find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access. View the NCST Project Webpage

Strong policies are needed to accelerate the zero-emission vehicle (ZEV) transition so that it occurs at a pace in line with international climate goals. The purchase price of new vehicles tends to be the variable that most affects consumer decisions. With urgency for a ZEV transition, fiscal pressure for governments can be high as rebates for consumers and incentives supporting manufacturers in the switch to ZEV technologies will be needed for a mass-market transition. Fees on high-polluting vehicles—and rebates on clean ones—have become an effective and increasingly common strategy in European countries. The feebate mechanism can raise the necessary capital for financing a ZEV transition in combination with other regulatory mechanisms. This paper reviews and assesses feebate design types, issues, and implementation strategies in France, Germany, Italy, Sweden, and the United Kingdom. These examples show that feebates can be designed in a variety of ways to meet unique policy objectives and that periodic adjustments are helpful in achieving goals. Among twelve design considerations for an effective feebate, the authors find that: (1) focusing on a single fee parameter, such as CO2 emissions, can be a simple yet effective mechanism; (2) a continuous functional form for the fee and a stepwise rebate are likely to be most effective in driving EV adoption; and (3) pure feebates, where fee revenue funds EV incentives by program design, provide certainty for manufacturers, regulators, and consumers. View the NCST Project Webpage

The United States (US), under the Biden Administration, has set a goal of reaching a 50% sales share for zero-emission vehicles by 2030. The administration is pursuing a combination of aggressive fuel economy and greenhouse gas performance standards along with tax credits for consumers who purchase electric vehicles (EVs). Given the anticipated high costs of the EV transition and limited public funds, policy mechanisms that generate extra-budgetary funding are enticing. Feebates—where a fee charged on some purchases is used to offer a rebate for others—can serve as a self-sustaining tool. Feebates have been attempted at the state and federal level in the US but did not pass legislatures due to a lack of political support for levying a fee on internal combustion engine (ICE) vehicles. However, as governments face increasing fiscal constraints, there is greater support for self- funding EV incentive programs. Feebate policies can provide certainty for both producers and consumers to facilitate a steady transition to sustainable transportation. This paper assesses the potential utility of feebates for shaping the US light-duty vehicle market. The analysis demonstrates that: (1) revenue-neutral incentive systems are possible and (2) revenue-neutrality can be achieved with relatively low fees on ICE vehicles to support economic equity among buyers. From an industry perspective, market certainty can be created by incorporating fuel economy targets into a fee schedule as pivot points and allocating fees to finance rebates. This would likely influence industry investment decisions in ways that increase EV production and model availability. View the NCST Project Webpage

The widespread adoption of electric vehicles (EV) is a centerpiece of California’s strategy to reach net-zero carbon emissions, but it is not fully known how and where EVs are being used, and how and where they are being charged. This report provides the first at-scale estimate of EV home charging. Previous estimates were based on conflicting surveys or extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combined billions of hourly electricity meter measurements with address-level EV registration records from California households, including roughly 40,000 EV owners. The average EV increases overall household load by 2.9 kilowatt-hours per day, well under half the amount assumed by state regulators. Results imply that EVs travel less than expected on electric power, raising questions about transportation electrification for climate policy.

To achieve California’s ambitious climate goals, a shift to hydrogen fuel for some transportation sectors may be essential.In this report, we explore the build-out of a hydrogen fuel distribution system including uptake of light-, medium-, and heavy-duty fuel cell electric vehicles. Our analysis of Base and High Case scenarios includes costs of building and operating a hydrogen vehicle and fuel system and estimates workforce impacts. We consider scenarios with about 125,000 vehicles by 2030 in the Base Case and 250,000 in the high case. This increases by an order of magnitude to 2045. Vehicle and station investment costs associated with the Base Case reach anywhere from $4 to 12 billion USD by 2030 and increase by a factor of eight by 2045. Costs per kg of hydrogen, including fuel transmission to stations and station costs delivered to vehicles, could be in the range of $4 to 8 per kg. This becomes $6 to 10/kg as a final delivered cost, if production of hydrogen were to cost $2/kg. Workforce impacts in the Base Case include 600 to 2,200 jobs created by 2030, rising rapidly thereafter. This report was prepared by the ITS-UC Davis Energy Futures Hydrogen Program in partnership with the UCLA Luskin Center for Innovation.

Under the Advanced Clean Cars II (ACC II) rule, California must move to 100% zero emission vehicle (ZEV) sales by 2035. Tomake this transition equitable, it is important to understand how we can support ZEV adoption in all communities–including rural communities. The aim of this study is to explore the experiences and perceptions of current rural plug-in electric vehicle (PEV) owners, identify barriers to charging and ownership, and suggest factors to guide the development of infrastructure in rural areas. (PEVs include battery-electric vehicles [BEVs] and plug-in hybrid vehicles.) Semi-structured interviews were conducted with rural PEV owners and included questions related to travel behavior, at-home and public charging experiences, and motivation for household vehicle purchase. Major themes were extracted from the interviews including that PHEV owners tend to have minimal at-home and public charging requirements, while BEV owners require access to Level 2 charging at home and reliable fast charging in public spaces. Additionally, the magnitude of public charging reliability and availability issues appear to be greater in rural than non-rural areas. Grid reliability issues and specific vehicle requirements were also points of discussion among rural PEV owners. The findings of this report could inform policy makers, car manufacturers, and PEV charging companies to better serve rural communities in the transition to 100% PEV sales.

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [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]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [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]

The primary metric for measuring electric vehicle (EV) adoption growth is new car sales. However, to enable mass market penetration, EV adoption in the used car market will play a crucial role. The used vehicle market is relatively under-studied or has been studied mostly for specific regions. This project analyzed US national consumer expenditure survey data that tracks households' expenditure on vehicle acquisition and operation. The study aim is to understand new versus used vehicle choice behavior and the consequent cost of vehicle ownership, with the larger aim of determining how much households who generally buy used vehicles can gain or lose if they transition from a used internal combustion engine vehicle (ICEV) to a used EV. A choice model and cluster analysis showed that ownership of used vehicles is influenced by family size, income, housing tenure, and age. For lower-income renters, current vehicle ownership and purchase costs tend to constitute a high fraction of their household income, raising concerns related to equity and suggesting that these households in particular should be considered in policies to encourage the EV transition. Moreover, while at present the average price paid for a used ICEV is approximately $18,000, the price of a comparable used EV can range between $14,000 (e.g., lower electric range Nissan Leaf) to $50,000 (high-range Tesla), suggesting the need for incentives to encourage the used EV market.

To mitigate climate change and air pollution, multiple US states and other countries have beensetting and adjusting goals and policies aimed at shifting sales from conventional, fossil-fuel–powered vehicles to plug-in electric vehicles (PEVs), defined as plug-in hybrid and battery electric (all-electric) vehicles. For example, US policies have offered federal tax credits for the purchase of PEVs, with limits set on how many PEVs from a single manufacturer, which PEVs, and which consumers qualify. A key to developing or adjusting these policies is understanding how financial incentives affect consumers’ decisions to purchase or lease PEVs. To better understand the impact of financial incentives on PEV leasing and purchasing, researchers at the University of California, Davis, analyzed survey responses from approximately 2,800 California PEV owners. The survey asked: If the federal tax credit were not available would you: purchase or lease the same PEV, switch to a different PEV, switch to a conventional or hybrid (non-plug in) vehicle, or not acquire a vehicle at all? This policy brief discusses findings from those survey responses and provides policy implications. View the NCST Project Webpage

Lithium-ion batteries (LIBs) are the enabling technology for modern electric vehicles (EVs), allowing them to reach driving ranges and costs comparable to internal combustion engine vehicles, an important development with EVs being integral to greenhouse gas mitigation efforts. However, LIB advancements include the use of rapidly evolving and chemically diverse batteries as well as larger battery packs, raising concerns about battery production sustainability as well as battery end-of-life (EoL). This study seeks to respond to these concerns by analyzing potential pathways for EoL EV batteries, quantifies flows of retiring EV battery materials, proposes economically and environmentally preferable LIB EoL strategies, and recommends pertinent policies with an emphasis on environmental justice. The researchers used a loosely coupled dynamic systems model that utilized life cycle assessment and material flow analysis and a mixed methods research approach. They find that the U.S. can make significant gains in securing supply chains for critical materials and decrease life cycle environmental impacts through the adoption of Recycled Content Standard policies similar to those found in the European Union. In addition, they examine the currently understood waste hierarchy in the context of LIB technology. Comparing immediate recycling to repurposing and reusing, they find that repurposing and reusing reduces life cycle environmental impacts relative to recycling. This project also includes an investigation of EoL battery collection and transportation and the vehicle afterlife ecosystem, as well as general stakeholders in the LiB life cycle, informed by expert interviews and a case study of a developing lithium industry in Imperial, California.View the NCST Project Webpage

This paper describes a model for developing highly resolved, time-of-day specific electric vehicle charging demand profiles from travel survey data. Since timing of vehicle charging is dependent on electric vehicle supply equipment (EVSE) availability, four EVSE scenarios are considered: 1) home only, 2) home and workplace only, 3) universal EVSE, and 4) a probabilistic scenario where EVSE availability varies by location. To illustrate the implications of differing demand profiles on power grid operation with high renewable generating capacity, the profiles are in a typical regional economic dispatch model. The results provide a valuable approach for understanding the interactions between vehicle electrification and renewable energy deployment while exploring an updated range of assumptions about EVSE availability and charging behaviors for New York and the six New England states. All scenarios result in increased peak demand and increased generation by non-renewable generating sources. This indicates that incentive mechanisms that influence charging decisions are necessary to attain lower emissions outcomes.View the NCST Project Webpage

High-nickel layered cathode materials for lithium-ion batteries have received widespread attention in new energy vehicles for their excellent specific energy and cost advantages, and are considered to be the most promising cathode materials for high-energy density lithium-ion batteries. Despite its many advantages, poor thermal stability and rapid capacity degradation have greatly limited its large-scale application. Ion doping is considered to be an effective way to improve its drawbacks, and in this paper, a series of W-doped LiNi 0.9 Co 0.1 O 2 cathode materials were prepared using WO 3 as a tungsten source. Among them, 1 mol% W doping was the most effective, and its reversible capacity of 204.44 mA g −1 still had 93.25 % capacity retention after 100 this cycles at 1C. Combined with DFT calculations, it is found that the introduction of W does not change the original layered structure and the material transitions from semi-metallic to metallic at lower doping concentrations, resulting in more electrons occupying the Fermi energy levels to enhance its electrical conductivity and leading to an elevated spin state and a lower oxidation state of Ni. [ABSTRACT FROM AUTHOR]

Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV's owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth. [ABSTRACT FROM AUTHOR]

What happens when the various parties involved in constructing decarbonised futures’ infrastructure diverge in their imaginaries? Much of the published research on the sociotechnical imaginaries relating to electric vehicles (EVs) describes the creation of the future of decarbonised transport as a process mired in conflict, with various interested parties represented as strenuously disagreeing in their assessment of the most efficacious solution. The aim of the article is to offer an alternative account, based upon data gathered through participant observation, interviews, and grey literature. It describes the sort of personal transportation futures currently being imagined in the United Kingdom. The focus is specifically on the installation of electric vehicle charge points. The author contrasts Whitehall’s national vision for this infrastructure with the ‘post-coalonial’ vision of officers of Durham County Council in North East England have articulated an alternative, a ‘post-coalonial’ vision, and finds that the vision of both the British civil service and Government of the United Kingdom focused on private ownership and commuting, while Durham County Council envisioned publicly accessible charge points that enabled various types of different journeys. Despite the striking differences the conclusion is that contrary to the findings of previous studies the existence of these divergent infrastructural imaginaries led not to conflict but to co-existence. [ABSTRACT FROM AUTHOR]

Dielectric capacitors are commonly used in electronic and electric power systems due to their excellent charging and discharging rates and ultra-high power densities. Especially, with the rapid development of new energy vehicles industry, high-temperature films capacitors are urgently needed, while the conductivity loss of polymer capacitive films at high temperatures sharply increases, which should be significantly suppressed for achieving excellent energy storage performance. This paper presents a composite film with polyetherimide (PEI) as the matrix, and core-shell structured particle ceramic as fillers where high polarization of barium strontium titanate (BaSrTiO 3) as the core and excellent insulation strength of alumina (Al 2 O 3) as the shell, respectively, achieving a synergistic enhancement of breakdown strength and dielectric constant of Al 2 O 3 @BaSrTiO 3 /PEI composite film. More importantly, the Al 2 O 3 layer with moderate dielectric constant can effectively relieves the electric field distortion at the interfacial regions due to the large difference between BaSrTiO 3 fillers and PEI matrix. The composite film demonstrates exceptional thermal and cycling stability, as well as energy storage properties within the studied temperature range, a discharge energy density of 4.67 J/cm3 at 150 °C. The enhanced mechanism of energy storage performance is revealed bycomparatively analyzing of the microstructure and dielectric properties of BaSrTiO 3 /PEI, Al 2 O 3 /PEI, and Al 2 O 3 @BaSrTiO 3 /PEI composite films. This paper presents a modification strategy of inorganic/organic composite films by increasing both of dielectric constant and electric breakdown strength at elevated temperature. [ABSTRACT FROM AUTHOR]

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model's efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model's robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model's exceptional generalizability and potential for real-world applications in electric vehicle battery management. [ABSTRACT FROM AUTHOR]

The battery pack is the most valuable component of the electric vehicle and its disassembly is the key process to recover the inner value of the product and apply circular economy strategies, from repair to recycling and remanufacturing. Different models of EV battery packs have been analyzed to assess criticalities in the product structure and disassembly procedure. Regardless the absence of a standardized design, some similarities can be identified and considered for the implementation of disassembly procedures. From the comparison of the disassembly procedures of four in-depth analyzed battery pack models emerged that it is possible to identify six disassembly blocks, grouped in two main disassembly stages. The first stage includes the disassembly of the battery covers, the coolant removal (in case of liquid cooling) and the service plug removal, while the second stage involves the removal of the junction block, the battery management system and the modules. The precedencies and criticalities in the execution of the six disassembly blocks were deeply analyzed and the results reported in the paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a short training curriculum for tertiary level education that can horizontally cover the Electric Vehicle technology in a manner understandable and attractive to students of appropriate background. The training program is divided into four groups of technical topics, the extend of which is balanced with regards to the learning outcomes and the teaching time provided. The blended teaching nature is supported by theoretical online lectures, synchronous and asynchronous, a great number of laboratory courses to enhance knowledge assimilation and a medium size project divided into two parts for the students to delve into the engineering way of thinking through a problem based learning approach. In the first part, the students exploited the Augmented Reality technology in order to describe the electric powertrain of small scale electric vehicles after having troubleshooting them. Their work and findings were utilized for the preparation of a scientific paper in the second part of the project. The training program was completed by a short industrial internship. The transferability of the training program is discussed in the last section of the paper. [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]

The promotion of electric vehicles brings notable environmental and economic advantages. Precisely estimating the state of health (SOH) of lithium-ion batteries is crucial for maintaining their efficiency and safety. This study introduces an SOH estimation approach for lithium-ion batteries that integrates multi-feature analysis with a convolutional neural network and kolmogorov-arnold network (CNN-KAN). Initially, we measure the charging time, current, and temperature during the constant voltage phase. These include charging duration, the integral of current over time, the chi-square value of current, and the integral of temperature over time, which are combined to create a comprehensive multi-feature set. The CNN's robust feature extraction is employed to identify crucial features from raw data, while KAN adeptly models the complex nonlinear interactions between these features and SOH, enabling accurate SOH estimation for lithium batteries. Experiments were carried out at four different charging current rates. The findings indicate that despite significant nonlinear declines in the SOH of lithium batteries, this method consistently provides accurate SOH estimations. The root mean square error (RMSE) is below 1%, with an average coefficient of determination (R 2 ) exceeding 98%. Compared to traditional methods, the proposed method demonstrates significant advantages in handling the nonlinear degradation trends in battery life prediction, enhancing the model's generalization ability as well as its reliability in practical applications. It holds significant promise for future research in SOH estimation of lithium batteries. [ABSTRACT FROM AUTHOR]

In the current era of rapid development in the new energy vehicle industry, graphite (Gr) has gained significant attention as the primary anode material for commercial lithium‐ion batteries due to its exceptional reversibility in lithium‐ion insertion/extraction and abundant availability. In this study, a multifunctional ferroelectric Bi12TiO20@C (BIT@C) modified interlayer between Gr and polymer electrolyte (PE) is fabricated. This approach established a channel and homogeneous ion‐conducting network that facilitated lithium‐ion intercalation into Gr at the interface while suppressing erratic lithium precipitation, thereby stabilizing the structure of Gr. The uniform electric field generated by BIT intercalation enabled homogeneous intercalation/deintercalation of lithium ions, effectively preventing the structural collapse of the Gr anode. Assembled LIBs exhibited reduced polarization and enhanced electrochemical properties attributed to facile charge transfer at the interface. The results revealed that Gr│carbon‐BIT@C‐lithium bistrifluoromethane sulfonimide (LiTFSI)/silicon dioxide (SiO2)/poly allyl acetoacetate (PAAA)/poly(vinylidene fluoride‐hexafluoropropylene) (P(VDF‐HFP))/polyvinylidene fluoride (PVDF)│LiFePO4 (LFP) batteries achieved an initial discharge specific capacity of 120.4 mAh g−1 with stable performance over 1200 cycles at 2 C and room temperature (RT), maintaining a Coulombic efficiency of 99.5% after 1200 cycles. The ferroelectric‐modified interface addressed challenges associated with ion transport at the electrode‐electrolyte interface. [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 paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices. • Designing a decision framework for a hybrid electricity/hydrogen station. • Participating in day-ahead and intraday electricity auction markets effectively. • Modeling price uncertainties using scenarios and k-means clustering. • Applying Benders decomposition for day-ahead and intraday bidding decisions. • Using stochastic dual dynamic programming for the Benders sub-problem. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEVs) are gaining recognition as a pivotal innovation in the quest for sustainable transportation, seamlessly blending the advantages of fuel cells with electric vehicle technology. As global initiatives to curtail greenhouse gas emissions and mitigate climate change gain momentum, FCHEVs offer a promising alternative to conventional internal combustion engine vehicles and battery electric vehicles (BEVs). This review aims to examine recent technological progress in enhancing FCHEV fuel efficiency and performance, focusing particularly on cost reduction, long-distance operational capabilities, and the incorporation of intelligent autonomous systems. A key innovation highlighted in this study is the exploration of cost-effective hydrogen storage techniques, including metal hydrides and chemical storage solutions, which have demonstrated potential to significantly increase storage capacity and reduce refueling times. Furthermore, advancements in energy management, particularly the integration of ultracapacitors and high-capacity batteries, offer new avenues for extending vehicle range and boosting overall efficiency. The emergence of autonomous FCHEVs represents another major leap forward, setting the stage for more efficient, self-driving vehicles in the near future. However, persistent challenges remain, including reducing hydrogen production costs, improving fuel cell durability, and developing a widespread hydrogen infrastructure. Addressing these issues will require ongoing innovation in fuel cell materials, hydrogen storage systems, and energy management strategies. This review underscores the transformative role FCHEVs can play in reducing emissions and enhancing energy efficiency in the transport sector. Collaboration between industry, researchers, and policymakers will be vital to unlocking the full potential of FCHEVs in creating a more sustainable future. • This review work explores the diverse advancements aimed at enhancing the fuel economy and performance of FCHEVs. • A primary focus of recent developments is the reduction of FCHEV costs. • Merging the benefits of hydrogen fuel cells with advanced electric vehicle technology. • The work also looks Fuel Cell Hybrid Electric Vehicles (FCHEVs), offering a promising path achieving sustainable transportation. [ABSTRACT FROM AUTHOR]

In the recent years, the growing pressure by the European Union to phase out the internal combustion engines has raised the quest for alternative solutions for low-environmental-impact mobility. Nevertheless, concerns on the life-cycle emissions of battery electric vehicles and perplexities on the socio-economic sustainability of the ecological transition suggest that maintaining the interest in internal combustion engines can be strategic, provided that carbon-neutral fuels are adopted. On the basis of the technological neutrality principle, relying on already existing and well-established technologies requires less effort and cost to convert the whole road transport. Moreover, the adoption of bio- or e-fuels obtained from renewable sources widely spread across the globe is not of secondary importance. In fact, cost reduction and worldwide diffusion of the resources are both main promoters of socio-economic sustainability. In this scenario, green hydrogen represents one of the main solutions for the survival of reciprocating engines. Since the production is solely based on renewable energy sources, it is not simply characterized by zero CO 2 emissions at the tailpipe, but it can be considered overall carbon neutral. A technical drawback in the use of hydrogen is represented by emissions of nitrogen oxides (NO x), due to the ever-present high temperature combustion process. For this reason, an ad-hoc design is mandatory to minimize NO x production, and CFD can be a valid tool to reduce cost and time to market for the development of hydrogen engines. In this regard, the current work proposes a 3D-CFD numerical methodology, based on the combination of G-Equation and Detailed Chemistry models, for NO x prediction in in-cylinder simulations of reciprocating internal combustion engines fueled with hydrogen. Although the combination of level-set method and chemical kinetics is not a novelty in literature, it is the first time that it is applied to evaluate NO x emissions in H 2 engines. The proposed approach is validated against experimental data on a direct injection, spark ignition, hydrogen engine. The methodology is able to properly predict NO x emissions at different mixture qualities, revving speeds and spark times. The total number of investigated cases is 17, which is a large set of simulations compared to the existing literature. Considering the best chemical mechanism (i.e. the one providing the best results among the tested ones), the error in the NO x prediction is always lower than 25% for all the simulations. Once the methodology is validated, the effect of spark and injection timings on NO x is discussed. Such a deepening is useful to emphasize the potential of the CFD to investigate phenomena leading to emission formation and, thus, to optimize engine parameters for NO x reduction. • Combustion model G equation is reliable in predicting DISI H2 ICEs NOx emissions. • Wide range of operating conditions can be simulated with same numerical setup. • Peak NOx formation in hydrogen combustion occurs around φ ~ 0.75 • Injection timing is crucial in DISI H2 ICEs NOx emissions. • Increase of mixture homogeneity is not always beneficial for NOx emissions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting. [ABSTRACT FROM AUTHOR]

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM. [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]

The fuel cell vehicle is one of the essential directions for developing new energy vehicles. But heat dissipation is a critical technical difficulty that needs to be solved urgently. Nano-coolant is a promising coolant that can potentially replace the existing coolant of a fuel cell. However, its thermal conductivity has a significant impact on heat dissipation performance, which is closely related to nanoparticles' thermal conductivity, nanoparticles' volume fraction, and the nano-coolant temperature. Many scholars have created the thermal conductivity models for nano-coolants to explore the mechanism of nano-coolants' thermal conductivity. At present, there is no unified opinion on the mechanism of the micro thermal conductivity of the nano-coolant. Hence, this paper proposed a novel model to predict the thermal conductivity of ethylene glycol/deionized water-based nano-coolants. A corrected model was designed based on the Hamilton & Crosser model and nanolayer theory. Finally, a new theoretical model of nano-coolant thermal conductivity suitable for fuel cell vehicles was constructed based on the base fluid's experimental data. [ABSTRACT FROM AUTHOR]

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications. [ABSTRACT FROM AUTHOR]