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Recycling lithium-ion batteries (LIBs) has gained prominence in the last decade due to increasing supply chain constraints for critical materials (such as lithium and cobalt) and policy shift toward increased circularity of materials to mitigate environmental concerns. Conventional recycling methods (e.g., pyrometallurgical techniques) are suboptimal because of high-temperature (>1400 °C) processing with recovery yields ranging from 50% to 85%. On the other hand, optimal preprocessing/pretreatment of end-of-life (EoL) LIBs results in a) high (>90%) recovery yield, b) lower temperature processing (lower environmental footprint), c) high potential for commercial returns of materials, and d) lower safety risks. This paper reviews major preprocessing methods, including sorting, stabilization, dismantling and comminution, and separation for spent LIBs. The capabilities of major recycling firms and preferences for preprocessing in recycling methods are also reviewed, highlighting research and development (R&D) initiatives to allow more efficient and cleaner solutions for recycling spent LIBs. The industry-wide state-of-the-art recycling process is also detailed based on global practices, focusing on the highest yields and lowest environmental footprint. Finally, this paper provides policy recommendations to enable sustainable recycling of LIBs on a global scale, consequently reducing the environmental Impact of waste material and addressing the growing need for LIBs as a result of the increased demand for electric vehicles and stationary storage. [Display omitted] • This paper addresses waste management of Li-ion batteries (LIBs). • Outlines the pros and cons of the preprocessing approach in LIB recycling. • Reviews the commercial preprocessing techniques used by LIB recyclers. • Proposes an optimized recycling method to increase element recovery. • Highlights (and proposes) new regulations to make preprocessing viable. [ABSTRACT FROM AUTHOR]

This research proposes an Optimized Current chopping Control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements, comprising minimized torque ripple to reduce vibrations and acoustic noises, maximized output torque to enhance vehicle acceleration, and improved efficiency, which contributes to extending the EV's battery life. Therefore, an optimization problem is formulated and solved offline, incorporating a CCC-based SRM drive model. The control variables for this optimization problem are the switching angles of the SRM. A multi-objective function is chosen to combine three performance indices: torque ripple, average torque, and efficiency. The Harris Hawks Optimization (HHO) method is utilized in this paper to solve the optimization problem and find the optimal switching angles based on the selected objective function. HHO demonstrates a strong search capability that can effectively handle the nonlinear magnetization characteristics of SRMs. Constraints on the switching angles are also included in the optimization problem to control the phase current's RMS value and power consumption. The optimized switching angles are applied to a current chopping control (CCC) strategy and an asymmetric half-bridge converter to implement the proposed HHO-based CCC drive. Moreover, to demonstrate the effectiveness of the proposed HHO-based CCC drive, a comparative analysis based on simulations and experimental measurements is presented against other CCC approaches for SRM drives, including modified particle swarm algorithm (MPSO)-based CCC drives and analytical-based CCC drives. • A finite element analysis (FEA) software is utilized to determine the magnetic characteristics of SRMs accurately. • The obtained magnetic characteristics are then verified using a series of indirect experimental measurements to build an accurate four-phase 8/6 SRM model. • The Harris Hawks optimization (HHO) method is employed in this paper to find the optimal switching angles based on the selected objective function. • This research proposes an optimized current chopping control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements. • The proposed HHO-based CCC drive is validated and compared based on simulations and experimental measurements against other CCC approaches for SRM drives, including modified particle swarm algorithm(MPSO)-based CCC drives and analytical-based CCC drives. [ABSTRACT FROM AUTHOR]

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale. • Complexity in charging infrastructure placement for freight transport. • Empirical data from a fleet of freight vehicles in Gothenburg metropolitan area. • Investigation of travel and parking patterns of freight vehicles. • Node-based optimization approach to allocate charging infrastructure. [ABSTRACT FROM AUTHOR]

The convergence of modern technology and supply chain operational dynamics has shaped the fabric of today's freight transportation practice and made the role of electric vehicles (EVs) profoundly significant in regular use in business operations. In addition to the expanding prevalence of EVs and charging infrastructures for medium and heavy-duty supply chain transport systems, it challenges the business community for deployment purposes. Moreover, these intelligent transport systems for modern supply chain organizations delve into the heart of a transformative realm, offering opportunities for academics and practitioners to explore the tools, strategies, methodologies, and training operation managers that propel enterprises toward EV-driven operational excellence. This paper describes a reference map that relates to different interface types and consists of various categories of electric vehicle supply equipment (EVSE). The paper also reviews security and privacy threats associated with the reference map interfaces and relevant components and presents highlighted security protection solutions in academic literature. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

With the advantages of simple structure, high power density, and better fuel adaptability, the hydrogen FPE is one of the ideal onboard power devices for new energy vehicles in the future. The combustion system is essential for forming the in-cylinder mixture and the stable operation of the engine. This paper compares combustion, injection, and performance at different ignition energy using high-energy ignition methods and applying computational fluid dynamics simulations. In order to further optimize the combustion system, the influence of ignition timing was discussed. The results show that with the increase of ignition energy, the flame kernel size increases continuously, the speed of flame propagation increases, and the mixture participating in combustion increases simultaneously. When the ignition energy reaches 300 mJ, the fuel combustion efficiency rises to 90.22%, and the indicated thermal efficiency rises to 32.76%. Finally, a comparison was made with a gasoline FPE; the hydrogen FPE in this paper reduces the NO emission value to less than 20 ppm without any after-treatment device, while no other pollutants are generated. The performance stability of pure hydrogen combustion under FPE is preliminarily revealed. [Display omitted] • A prototype of hydrogen FPE with a high-energy ignition system was designed. • Establish a CFD model for hydrogen FPE. • Set up the bench test and calibrate the experimental data. • Early ignition timing has the advantages of a fast heat release rate and low emission. • The performance of hydrogen FPE with different ignition energy was compared. [ABSTRACT FROM AUTHOR]

China's New Energy Vehicle (NEV) industry has entered an accelerated development stage, with the technology innovation efficiency becoming an essential factor affecting the speed of its development. Therefore, to explore ways to improve the efficiency of technology innovation, this paper divides the technology innovation process into two stages: the research and development (R&D) stage and the commercialization stage. This paper introduces a framework for evaluating the technology innovation efficiency of NEV enterprises based on data envelopment analysis (DEA), meta-frontier, and tobit regression. Then, the technology innovation efficiencies of 45 vehicle, battery, and motor & electronic control NEV enterprises are analyzed. The results show that: (i) During the survey period, the overall technology innovation capability of Chinese new energy vehicle enterprises is not strong, especially regarding economic transformation. However, the general development trend is on the rise. (ii) Different types of enterprises have different technology gap ratios in the two stages. The technology innovation efficiency level of motor and electronic control enterprises is relatively weak in the new energy vehicle industry. (iii) Many measures can be implemented to help improve technology innovation efficiency: encouraging the large-scale development of enterprises, reasonably increasing the shareholding ratio of significant shareholders, improving the ratio of capital investment, stimulating the vitality of innovation within the enterprise, and optimizing the number of employees. [ABSTRACT FROM AUTHOR]

In the current scenario, the impact of the automobile sector on greenhouse gas emissions becomes inevitable. Considering the case, automobile manufacturers pay attention to commercializing Electric vehicles (EVs) in a wide range. The effective functioning of the electrified vehicle system is highly determined by its charging infrastructure. This paper presents a comparative analysis of wired and wireless charging methods, followed by an overview of the evolution of the Wireless Power Transfer (WPT) system. WPT system's performance is flexible, safe, reliable, and cost-effective operation retains its essentiality in the research area for further development in contactless charging methods. In recent years, EV battery charging is dominated by the attractive concept of wireless power transfer with the capability of automatic power transmission without human assistance. Planar pad structures such as polarized and non-polarized pads and compensation networks like SS, SP, PS, and PP are used in the inductive power transfer system to achieve high power transmission. This literature review briefly compares different charging pads and various compensation techniques used in WPT. The prominent role and essentiality of different stages of power converters in the WPT system are highlighted in detail. The effect of EV charging on the grid, the bidirectional capability of EVs, and the strategy of charge management are mentioned. The paper particularly brings out the essentiality of integrating Renewable Energy Sources (RES) with EVs in concern with environmental health. At last, the short notes on the status of the WPT system in the Indian scenario, and the future challenges like safety, health management, Foreign Object Detection (FOD), and economic criteria in the progress of wireless EV charging systems are also addressed. [ABSTRACT FROM AUTHOR]

The hydrogen pressure typically increases to approximately 700-bar for storage in high-pressure tanks. Some industry applications use such a high-pressure hydrogen but some others need lower-pressure hydrogen where the pressure is reduced through pressure reduction valves. Some energy is lost during pressure reduction which would be significant in large-scale systems. A multi-level hydrogen storage can address this issue, where the hydrogen is stored at various pressures. This paper addresses an electricity-hydrogen nexus system integrated with multi-level hydrogen storage and solar PV sites. The designed system supplies electric and fuelcell vehicles at 3 different charging points including an electric vehicle charging station, fuelcell car refueling station, and fuelcell truck refueling station. The hydrogen is stored at 30, 350, and 700-bar pressure levels and each level feeds various users. The water electrolysis is employed to generate hydrogen at 30-bar pressure and 2 compressors increase the pressure to 350 and 700-bar. The cost function, defined as the daily energy cost, is optimized at $1045.65 per day. The water electrolyzer produces the most hydrogen during nighttime when electricity is inexpensive. Under normal operating conditions, the fuel cell is not utilized due to its low efficiency of about 60%. However, it serves as a backup resource in the event of a system outage, such as when the external grid fails, in hours 22 to 24. During this time, the fuel cell is activated and produces 154 kWh. • Multi-Level Hydrogen Storage. • Electricity-Hydrogen Nexus System Integration. • Optimized Hydrogen Production and Pressure Management. • Cost Optimization and Efficiency. • Backup Resource Utilization. [ABSTRACT FROM AUTHOR]

Energy and transport are the two largest contributors to carbon emissions. In recent years, the transport sector has made strides in reducing its carbon footprint by producing electric vehicles that emit zero carbon. This paper investigates the use of Fuel Cell Electric Vehicles (FCEVs) with a newly developed regenerative suspension module (RSM). The RSM uses an electromechanical mechanism to obtain regenerative electrical energy from the engine and is mounted to the passive suspension system. To determine the potential energy savings of the RSM module, a hydrogen fuel cell electric vehicle simulated under specific road geometries, speeds, and acceleration conditions. The simulation results indicate that the Regenerative Suspension Modules (RSM) mounted on each wheel of the FCEV generate 2.5 times more energy than the regenerative braking system. This result is evaluated as an increase in efficiency and range in electric vehicles. • This study aimed to investigate efficiency gains from adding an RSM to hydrogen fuel cell electric vehicles. • This is the first study in literature to integrate RSM into a Fuel Cell Electric Vehicle. • The charging efficiency of the FCEV is improved by the Regenerative Suspension Modules mounted on each wheel. • The FCEV's Regenerative Suspension Module mounted on each wheel generates 2.5 times more energy than regenerative braking. [ABSTRACT FROM AUTHOR]

In recent years, Electric Vehicles (EVs) powered by a Proton Exchange Membrane Fuel Cell (PEMFC) which has high energy density and efficiency have become an attractive solution for the automotive industry. However, since the power density of the PEMFC is not high enough for different road conditions such as accelerating and going uphill, a supercapacitor (SC) is preferred as the auxiliary energy source for high power density in EVs. Thus, in this paper, a hybrid power system consisting of the Fuel Cell (FC) and SC for a Four-Wheel Drive Electric Vehicle (4WDEV) is proposed. Since energy requirement of the EV depends on the travel route, an effective Energy Management System can be designed by using a Geographic Information System (GIS). According to the road trajectory, the power requirement of the EV is estimated and thus, energy consumption is reduced. The proposed system is validated by using the Matlab Simscape Toolbox. • A hybrid power system consisting of a PEM Fuel Cell and SC for a 4WDEV is proposed. • An effective energy management system is designed using a GIS based on road route. • The high power demand of the EV is met from the SC in the sloping roads. • The energy consumption and the stresses of the power converters are reduced. [ABSTRACT FROM AUTHOR]

Aluminum nitride (AlN) has attracted huge attention in new energy vehicle, communication equipment fields, due to the high-thermal conductivity and reliable insulating ability. One of the most crucial factors is the preparation of high-performance AlN powders, and more significantly, the two-dimensional nanostructure would change the phonon vibration mode and transport process, bringing in the novel heat conduction and electricity properties. In this paper, kilogram of two-dimensional aluminum nitride nanosheets (AlNNS) with thicknesses around 30 nm and particle sizes around 400 nm were prepared by a new strategy for the nano sanding process, in which they still keep high purity (impurity content ppm grade) without further purification. Furthermore, the technological parameters of the sanding time and line speed are found as the key factors, that would affect the transformation process fro m hexagonal AlN into super-thin AlNNS. The morphology of the nanosheets is more complete at the line speed of 8 m/s. In addition, AlNNS have an increase of 7% in thermal conductivity (at 50 °C) compared to that of raw powders. Moreover, AlNNS have stronger sintering activity due to the larger specific surface area, in which show an increase of 16.8% in shrinkage. Therefore, AlNNS would represent excellent physical properties more than thermal conductivity, providing a new dimension research in high-thermal conductivity ceramics. Last but not least, nano sanding strategy as a purely physical process, avoids the subsequent chemical purification process, and has the advantages of simple and efficient, low energy consumption and environmental protection. [ABSTRACT FROM AUTHOR]

This paper introduces a smart cyber physical multi-source energy system for electric vehicle applications. This system is realized in order to increase the autonomy of the vehicle as well as a good self-dispatch energy system. In this paper, three energy sources have modeled and integrated in the energy conversion chain of the vehicle. Moreover, the design of cyber energy dispatching system is proposed based on the genetic algorithm NSGA-II. Finally, the simulation and experimental results are demonstrated. [ABSTRACT FROM AUTHOR]

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts. • A framework of multi-source information interaction is established. • Two charging modes are designed by means of price incentive. • The models of charging decision prediction based on psychology are presented. • Both the energy units and EVs are optimally dispatched in HEVCS. [ABSTRACT FROM AUTHOR]

With the transportation sector being the largest contributor of greenhouse gas emissions in the United States, states are actively planning the deployment of plug-in electric vehicles (PEVs). Mass adoption of PEVs requires attracting potential buyers living in multi-unit dwellings (MUDs). Given the current low adoption rate of PEVs in MUDs and MUD residents' reliance on public charging, this paper studies whether there is a positive correlation between the number of public Level 2 (L2) chargers and MUD density (measured by total square footage of MUD per capita) across census block groups (CBGs) in LA County. The results show that high MUD-density CBGs and low MUD-density CBGs do not differ much in terms of the number of chargers. The charger-to-PEV-ratio range for MUD residents derived in this paper is below the ideal charger-to-PEV-ratio range in the literature. A direct policy implication is that more charging infrastructure should be made available to MUD residents. This includes public L2 chargers near MUDs, which are discussed in this paper, and onsite MUD charging, which is still at an early stage. • Correlation between EV charger quantity and MUD density in LA County is estimated. • Findings suggest insufficient public Level 2 chargers for the use of MUD EV owners. • MUD charger-to-EV-ratios are computed across census block groups (CBGs). • An MUD charger sufficiency map for LA County is created at CBG level. • This paper concludes that more chargers should be made available to MUD residents. [ABSTRACT FROM AUTHOR]

• Control technology & measures of diesel truck emissions in China are introduced. • Comprehensive application of control technology & measures in BTHs are evaluated. • Remote online monitoring technology has been applied to HDDTs. • Transportation structure adjustment is proposed to reduce road emissions. • Peak-alternation transportation in heavy pollution weather is introduced. Chinese diesel trucks are the main contributors to NO x and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment. [ABSTRACT FROM AUTHOR]

This paper asks how legislative elites—lawmakers serving on statehouse committees with jurisdiction over transportation and energy—set transportation policy and funding agendas and what factors matter for compelling lawmaker policy action or explaining inaction. The role of legislatures in federalist systems has broader implications for the setting of transportation policy agendas, but in this study, we focus on U.S. state legislatures. We report on five focus group discussions conducted with 23 legislative representatives from 19 different states to reveal untapped insights into committee members' perceptions and the factors that drive their attention to certain issues in contemporary U.S. transportation policy. We find that these state-level political elites almost universally see funding as the most important issue and recognize the sector's broad fiscal reliance on motor fuel taxes as structurally unsound, given growing vehicle fuel efficiency and electric vehicle adoption. We find further that, with respect to their own policymaking activities, state lawmakers understand transportation policy and energy transition as distinct issues and appear unable to meet these known challenges by crossing committees to redesign policy. We also see implications of these results more broadly for subnational and provincial governments that, regardless of country setting, must often work across multiple layers of government to set policy agendas in the context of an evolving energy and transportation landscape and that warrant greater attention from policy scholars. [ABSTRACT FROM AUTHOR]

• A life cycle analysis on recycling and secondary use of lithium-ion batteries. • Based on the recycling in China, the LCA of different methods has been established. • Compared to other recovery, the secondary use has the lowest environmental impact. • Secondary use has the greatest impact on assessment results in dynamic situations. With the rapid development of the global new energy vehicle industry, how to minimize the environmental impact of the recovery has become a common concern and urgent concern. China is a major production and consumption market for electric vehicles, there are no specific and extensive resource and environmental assessment system for batteries. In this paper, the retired Electric vehicles lithium-ion batteries (LIBs) was the research object, and a specific analysis of the recycling treatment and gradual use stages of power batteries were based on life cycle assessment. Different battery assessment scenarios were established according to the development of battery recycling in China. The results showed that the secondary use has the optimal performance compared to the full-component physical, pyrometallurgical and hydrometallurgy recycling. The results showed that direct recycling has a GWP of 0.037 kg-CO 2 eq·kg LIB-1, which is lower than others. Secondary use of LIB accounts for the most emission reductions with Global warming (GWP) as 12.134 kg-CO 2 eq·kg LIB-1. The secondary use has the greatest impact on the assessment results, especially in dynamic scenarios. Through a comprehensive comparison of different recycling technologies, the secondary use, increasing the recycling rate, reducing resource, energy consumption and pollution emissions. [ABSTRACT FROM AUTHOR]

Nowadays business has shifted from product-centric to customer-centric due to the industrial revolution. As a result, the customer's problem and experience need to be studied and inspected thoroughly to find solutions. Charging stations implementing different charging technologies are reinforced in various topographical locations. Varied charging methods, including AC slow charging, AC fast charging, DC level 1 charging, DC level 2 charging, wireless charging is implemented so far to reduce the charging time. Various business models are established and reported by researchers to support the EV ecosystem. This paper presents one such business model for an electric vehicle (EV) charging system. Design Thinking is one of the problem-solving approaches discussed here. How to tackle the charging issues by implementing different technologies is also discussed. Different phases of Design Thinking are used to find the possible solution to the problems faced by customers. Different groups of customers' problems and how the proposed solution can fulfil a major group of customers' needs are discussed here. [ABSTRACT FROM AUTHOR]

Precooling gaseous hydrogen fuel to a cold temperature before refueling a heavy-duty (HD) hydrogen fuel cell electric vehicle (HFCEV) is essential to avoid overheating the vehicle tank, as well as achieving a high state of charge (SOC). Because a large volume of hydrogen is dispensed during each fill, the need for a shorter fill time amplifies the need to precool each load for refueling a HFCEV. Thus, the design and operation of a precooling unit (PCU), as well as the associated capital and operating costs, plays a pivotal role in any plans for heavy-duty hydrogen refueling stations. In this paper, we present a thermodynamic and technoeconomic analysis of a PCU in a gaseous hydrogen refueling station (HRS) for HD HFCEVs. By employing Argonne National Laboratory's hydrogen station cost optimization and performance evaluation (H2SCOPE) model, the refueling of 50 kg of hydrogen on-board type IV tank at ambient temperatures of 15–45 °C and varying fill rates is simulated. The required degree of precooling temperature to obtain either 100% or the maximum possible SOC is obtained from the simulation. Additionally, the simulation results demonstrate that the average flow rate of hydrogen is approximately 40% lower than the maximum flow rate during a typical fill; which motivates further evaluation of the instantaneous hydrogen mass flow rate profile and suggests the scope of improving precooling unit design. Accordingly, a hybrid strategy of precooling hydrogen has been proposed to address the cooling load by sizing the refrigeration unit for the average flow rate of hydrogen, while supplementing the above average peak hydrogen flow cooling load through thermal buffering. The combined technique enables the downsizing of the original PCU capacity by 25–40% and demonstrates a potential cost reduction of the PCU by approximately 30%, which translates to an installed cost reduction of ∼$125,000 per dispenser. • Hydrogen fill rate during refueling has a strong influence on the precooling load. • Bell-shaped profile of the fill rate advises a scope of improving precooling. • Hybrid strategy of on-demand cooling aided with partial thermal buffer is proposed. • The hybrid precooling approach downsizes the refrigerator capacity by 25–40%. • A potential reduction in the capital cost of precooling unit is 30%. [ABSTRACT FROM AUTHOR]

At present, two subsidy policies, which are based on electric vehicle (EV) consumers and EV charging station firms, have been widely implemented in different areas in China to promote EV adoption. However, the effectiveness of subsidy policies is significantly influenced by the indirect network and herd effects, depicting the positive impacts of the numbers of energy infrastructure firms and the sales of vehicles on consumers' utilities, respectively. Taking the aforementioned effects into consideration, this study examines the optimal subsidy policies by developing a Stackelberg game model. The game model analyzes the interaction and decisions of multiple stakeholders (i.e., consumers, energy infrastructure firms, EV and gasoline vehicle manufacturers, and the government). Interestingly, we find that a stronger herd effect will promote EV sales when the unit production cost of EVs is low; however, it will hinder EV sales when the unit production cost is high. Besides, we compare the effectiveness of the two subsidy policies for promoting EV adoption, reducing environmental impacts, and increasing the social welfare. Findings show that the effectiveness of subsidy policies highly depends on the extent of the indirect network and herd effects. Specifically, compared with EV consumers-based subsidy policy, EV charging station firms-based subsidy policy will be more effective in only two conditions: a weak indirect network effect and a strong herd effect. Otherwise, the EV consumers-based subsidy policy will be more effective. The findings of the paper are helpful in enhancing the effectiveness of subsidy policy in China. • Government subsidy cannot always promote electric vehicle adoption. • The indirect network and herd effects affect the effectiveness of subsidy policy. • A stronger herd effect will not always promote electric vehicle sales. • Either consumers or charging station investors can be the best subsidy object. [ABSTRACT FROM AUTHOR]

• Structural equation modeling approach to analyze EV users' decision-making process. • Acceptability of dynamic wireless charging facilities influenced by latent factors. • User's acceptance and latent constructs are mediated by the intention to use. • Urban users are more likely to adopt DWC technology compared to rural users. • EV experience strengthens the relationship between perceived usefulness and users' intention. Gaining insights into how users perceive dynamic wireless charging (DWC), also known as dynamic wireless power transfer (DWPT), technology for electric vehicle charging is crucial for developing effective measures and policies for its successful integration into the transportation network. This research paper presents a study conducted in New Zealand using an online stated preference survey and employing a structural equation modelling approach. The study investigates users' intention and acceptance of DWC technology, considering various latent factors, while incorporating attributes related to technology acceptance and the theory of planned behaviour in the modelling framework. The findings reveal that perceived concern negatively influences users' intention to use DWC facilities, while perceived usefulness in terms of ease of use and convenience, as well as environmental awareness and social norms, positively impact users' intention. These results offer valuable insights for devising strategies to enhance the acceptance of DWC technology in the future. [ABSTRACT FROM AUTHOR]

Several factors are influencing the spread of Electric Vehicles (EVs) in the automotive market. However, while battery-electric vehicles emit no tailpipe emissions, the manufacturing phase, particularly the manufacturing of the battery packs, can have significant environmental impacts. In addition, as the EV market expands, there will be a significant increase in demand for critical materials used in lithium-ion batteries, such as lithium, cobalt, and nickel. These materials are essential for producing high-performance batteries, and their global demand is expected to rise rapidly to meet the demands of the expanding market. This paper investigates the main challenges that need to be tackled to reach a sustainable path in the battery industry. A cradle-to-gate boundary is set to focus on raw material extraction, production of precursors, cell and module production, and battery pack assembly. In addition, because 7.8 million tons of EV batteries per year are expected to reach the end-of-life phase by 2040, a brief overview of the recycling issue is provided to investigate the potential usage of recycled material in the early stages of battery production. [ABSTRACT FROM AUTHOR]

The accelerated adoption of Electric Vehicles (EVs) necessitates innovative and effective pricing strategies for charging infrastructure. This study leverages the MATSim (Multi-Agent Transport Simulation) framework to meticulously evaluate the performance of two distinctive EV charging pricing models: time-based charging and a hybrid model integrating both usage-based and time-based components. Driven by the pivotal question of how EV charging should be optimally priced—whether contingent on energy consumption or charging duration—the research endeavors to conduct two simulations. These simulations aim to provide a comprehensive comparative analysis, evaluating metrics such as total power derived from grids, utility revenue, charging station queues and served vehicles. In the time-based charging scenario, EVs incur charges based on their plugged-in duration, reflecting a pricing approach that correlates directly with the time a vehicle remains connected to the charging station. In contrast, in the hybrid model, EVs undergo initial billing based on usage until a predetermined battery charge point is reached, such as achieving a full battery. Subsequently, time-based pricing takes effect until the user disconnects the vehicle. The findings indicate that, in the combined approach, utility owners have the potential to generate more revenue. Conversely, the time-based approach demonstrates a capacity to serve a higher number of electric EVs, with comparable queue lengths observed in both approaches. Importantly, using the results of this paper, policymakers can suggest pricing schemes that maximize benefits for utility owners, reduce queues at charging stations, and ensure the security of power grids. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized as low-carbon transportation that adopts green and clean technology with contributing to mitigating global climate change. However, it appears that the practice in using EVs has not gained widespread popularity despite the promotion of various incentives with the presence of various restricting factors and challenges. In conducting this study in the context of China, we hypothesize that restricting factors have varying impacts on promoting EVs in China, where the adoption of EVs is of great potential. This paper identifies these restricting factors by applying AHP-FCE integrated approach, and these factors are classified into five dimensions, namely, technical, infrastructural, economic, governance and social. The results indicate that the top five restricting factors are security issues, limited driving range, long charging time, improper distribution of charging stations and lack of sharing between charging piles respectively. Our research contributes to a better understanding of EV restricting factors and their significance, which offers the assistance in the formulation of policies and decisions in order to promote effectively EVs in China. • The promotion of EVs is of great potential in China for mitigating global climate change. • Explore the RFPEVs in China using AHP-FCE integrated approach. • The RFPEVs in China can be classified into five dimensions, namely, technical, infrastructural, economic, governance and social. • The technical and infrastructural related are the most important constraints. • The main three important restricting factors are security issues, limited driving range, long charging time. [ABSTRACT FROM AUTHOR]

Wire harnesses are vital for any modern automotive vehicle. They control the basic functions in a vehicle, for example, windshield wipers and critical functions such as sensors, cameras, and autopilot functions. Thus, the quality of wire harness assembly is highly important. Today, wire harnesses are usually assembled manually, which creates unergonomic and tedious working conditions for operators. Traditional and collaborative industrial robots have been identified as possible solutions to overcome challenges faced by operators in this type of assembly. The international research community has proposed many solutions for automating the assembly of wire harnesses in automotive vehicles but despite these solutions, the industry has not been able to adopt a method to automate this assembly process fully or partially. This paper presents a review of findings on robot-assisted wire harness assembly processes based on a systematic literature review. Specifically, the assembly of wire harnesses in Electric Vehicles (EVs). The state-of-the-art review focuses on solutions to improve unergonomic work situations and ensure the quality of assembly operations. Best practices and reasons for the lack of extensive implementation in automotive final assembly systems are described. Further, the paper presents suggestions based on success stories where the automation of the wire harness assembly in automotive vehicles has been realised by leveraging human-centred automation solutions. Based on the findings, this paper identifies the research for future study. The findings also indicate that there is already technology that can support the automation of wire harness assembly processes in EVs but it is crucial to identify the human aspects and the role of humans in the assembly of wire harness assembly process. [ABSTRACT FROM AUTHOR]

Road transport accounts for around 12% of global greenhouse gas (GHG) emissions. As many high-income countries are moving to decarbonise their road vehicle fleets, Kyrgyzstan – with rapid growth in passenger car ownership – is seeing a significant increase in transport-related GHG emissions and air pollution in urban areas. Whilst a transition to electric vehicles (EVs) is a key part of Kyrgyzstan's Nationally Determined Contribution to the Paris Agreement, the potential for successful EV deployment in the region is under-researched. To fill this research gap, this paper presents an assessment of the potential for EV deployment in Kyrgyzstan. Firstly, we present an investigation of the policy and institutional landscape relating to transport and the promotion of EVs in Kyrgyzstan. Secondly, based on research of 50,000 car sales and interviews with 23 key stakeholders in the country, we present analysis of the existing Kyrgyz vehicle fleet. Thirdly, using information ascertained from the interviews and desk-based research, we conduct a Total Cost of Ownership assessment of EVs versus internal combustion engine vehicles (ICEVs) in the Kyrgyz context. We find that under the current conditions, EVs can have a lower total cost of ownership against similar ICEVs in Kyrgyzstan. However, it is evident that this is not typically the case: the analysis in this paper suggests that cost-competitiveness between EVs and ICEVs is more likely for larger and higher-powered vehicles, for which the ICEV version has a higher retail price. Finally, combining analysis of these results and analysis of the current conditions in Kyrgyzstan, trajectories for EV adoption in geographically and economically similar nations, and data from expert stakeholder interviews, we propose a set of policy recommendations to accelerate EV uptake in Kyrgyzstan. • We present a study into electric vehicle (EV) adoption in Kyrgyzstan. • Interviews with 23 expert stakeholders and over 50,000 car sales are analysed. • A total cost of ownership (TCO) model is presented for the Kyrgyz case. • Policy recommendations are made on the basis of this study. • The recommendations presented are applicable to the Central Asian region. [ABSTRACT FROM AUTHOR]

The demand for rounded graphite for lithium-ion batteries will increase rapidly in the next years, mainly driven by the growing number of electric vehicles. Due to the different particle morphology, synthetic and natural graphite materials have different processing requirements to produce battery-grade materials. However, similar rounding processes with batch-wise operating classifier mills or continuously running classifier mill cascades are used today. For natural graphite in particular, these processes are not very efficient due to their low yield and high energy demand. In this paper, we present a newly developed rounding process for natural graphite, with lower specific energy consumption and higher yield compared to the existing processes. These advantages make the proposed process more economical especially in periods of time with high energy prices. The quality of the material produced by the new process was investigated and compared with the material produced by the existing process, by showing similar graphite properties. Furthermore, the scalability of the new spheroidization process is demonstrated in this paper. Finally, the good cycling performance of the rounded natural graphite material produced by the new process is demonstrated in stacked pouch cells assembled with double side coated anodes. The tested pouch cells still had a capacity retention of over 88% after more than 1000 charge and discharge cycles. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper emphasizes the significance of sustainable energy solutions centered around electric vehicles (EVs). This involves Electric Intelligent Parking Lots (IPLs) that are interconnected with Renewable Energy Sources (RES) and Hydrogen Storage Systems (HSS) to achieve both technical and environmental goals. The study introduces a probabilistic approach to managing HSS-based energy in IPLs, employing the Probabilistic Two-Point Estimate Method (TPEM) to consider variables like sun radiation, temperature, wind speed, and IPL load. The Honey Badger Algorithm (HBA) is employed for optimization, striving to minimize operational costs related to energy distribution while maximizing profits by exploring revenue opportunities within the IPL system. This strategy enhances energy management and intelligent EV charging in IPLs, improving efficiency and economic viability. The approach is tested in a standard IPL environment integrated with RES and load demand, showcasing its successful technical and financial outcomes. Results reveal a 7.8 % profit increase from renewable resources and an additional 4.89 % when uncertainties are factored in. Ultimately, the proposed energy management framework utilizing HSS and RES integration proves effective in achieving objectives, with the potential for greater profitability through renewable resource incorporation and uncertainty consideration. • Optimal energy management framework proposed for electric vehicle integration in intelligent parking lots. • Simulation of efficient charging and discharging strategy in an IPL for electric vehicles. • Impact of renewable sources integration (wind and solar) on IPL efficiency and revenue generation. • Uncertainty analysis of renewable sources and load demands in IPL using two-point estimation method. • Smart integration of renewable sources, hydrogen storage, and electric vehicle charging for optimal operation of IPL. [ABSTRACT FROM AUTHOR]

After a natural disaster, electrical grids might go into a self-healing mode to restore service and reconnect any customers that lost power. In the self-heal system, the fast operation and restoring the critical loads (CLs) during a fault by available resources can increase the flexibility and resilience of the network. In addition to renewable energy sources and electric vehicles (EVs) storage devices, the hydrogen storage system and the process of converting hydrogen into electric energy by micro-gas turbine (MGT) have been modeled in order to assess the impact of this storage system in electrical loads restoration in critical conditions. Self-healing functionality is enhanced by using alternative and novel energy sources. In such a situation, how to interact with the stochastic nature of network components will be challenging. This paper formulates the optimal service restoration (SR) problem using chance-constrained Adaptive Distributionally Robust Optimization (ADRO) to address the ambiguities introduced by the uncertain parameters. A tri-level robust optimization model is developed such that the optimal switching in the network until clearing the fault is determined at the upper level, and the maximization of the SR based on the load demand values is determined at the lower level. The proposed method is demonstrated by simulation results on the modified Civanlar test system. In an equal operating condition and considering β = 3, it can be seen that the initial hydrogen State of the Tank (SoT) by 50 % can improve the load restoration by 7.5 % compared to the uncharged hydrogen tank. By increasing the initial SoT to the nominal value (10 Kg), the load restoration can increase to 13.2 %. [Display omitted] • Customized hydrogen into electric energy conversion model is developed in the energy management field. • Each uncertain parameter is considered ambiguous in the manner that follows an unknown but bounded distribution. • EVs, PHEVs, flexible loads, and the coordinated operation of the hydrogen-to-power system are all taken into account simultaneously. • A tri-level MILP model is formulated to determine the service restoration schedule. • CVaR is employed to effectively deal with the uncertainty of the parameters. [ABSTRACT FROM AUTHOR]

The shortage of fossil energy and the aggravation of environmental pollution have led to stricter requirements for energy conservation and environmental protection of automobiles. The development of new energy vehicles still needs time and space. The internal combustion engine is still the power source that automobiles will need to rely on in the future. Lean combustion is one of the key research directions for efficient and clean combustion technology for gasoline engines. In this paper, the effects of high-energy ignition coupling hydrogen blending on the combustion stability of lean-burn gasoline engines were studied by means of multi-point high-energy ignition and gasoline/hydrogen double direct injection. Through the combination of gasoline and hydrogen injection timing, a suitable stratified mixture can be formed in the cylinder. Combined with high-energy ignition, the fire core can be formed smoothly, and the flame spreads rapidly. The introduction of hydrogen increases the concentration of H in the reaction system, enhances the activity of the reaction system, and accelerates the oxidation process of fuel molecules. Compared with a pure gasoline engine, hydrogen blending can expand the lean burn limit by 66.2% and increase ITE (Indicated Thermal Efficiency) by 6.5%. NO x emission increases first and then decreases with the increase in excess air ratio, but when λ > 1.6, NO x emission is much lower than that of the equivalence ratio condition. HC (Hydrocarbons), CO, and CO 2 emissions are also lower than the equivalent ratio conditions. • Multi-point high-energy ignition with three spark plugs. • Gasoline and hydrogen double direct injection to achieve lean combustion. • Effects on combustion and emission of lean burn gasoline engine. [ABSTRACT FROM AUTHOR]

Excessive temperature of battery pack causes the performance degradation, affecting the normal operation and safety of the electric vehicles (EVs). This paper proposes an ant colony optimization-fuzzy sliding mode control (ACO-FSMC) hierarchical strategy for the battery thermal management system (BTMS) of EVs. Ignoring the dynamic process of the pump and compressor leads to the deviation of the cooling efficiency of EVs. Therefore, a control-oriented BTMS model is established to describe the temperature dynamics of battery pack and coolant, considering driving current effect on the speed of pump and compressor. Then, a hierarchical optimization strategy is designed to realize the control of the battery cooling rate as well as the speed of the pump and compressor. The upper layer of controller use an ant colony optimization (ACO) to solve the reference speed of the pump and compressor while satisfying constraints of physical characteristics of actuators, aiming at reducing battery temperature deviation and saving energy of BTMS. Considering the nonlinearity of lower layer of BTMS caused by the disturbance of time-varying load torque, fuzzy sliding mode control (FSMC) is applied to control the speed of pump and compressor, in which an integral sliding surface is designed to reduce the influence of disturbance. In order to weaken the chattering of sliding mode control and reduce energy consumption, fuzzy controller is used to adjust the reaching law parameters of sliding mode surface. The simulation results show that compared with PID, the maximum temperature deviation of ACO-FSMC is reduced by 43%, and the energy consumption is reduced by 23%. • A control-oriented battery thermal management model is established. • The dynamic process of the pump and compressor is considered. • ACO is used to solve the reference speed of the pump and compressor. • Fuzzy sliding mode control is applied to weaken the chattering of system. • The cooling performance and energy saving of the proposed method is verified. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative load frequency control (LFC) strategy based on a multi-agent deep reinforcement learning (MADRL) framework for the multi-area power system in the presence of voltage source converters (VSCs) and electric vehicle (EV) aggregators under cyber-attacks. Different from the existing LFC model, a novel transfer function of VSCs is first improved by the space-vector technique and integrated with EV aggregators to develop a multi-area training environment. By installing the agent in different control areas and interacting state transition information between agents and the new environment, the MADRL-based control strategy is achieved for centralized training and decentralized execution. Thus, the proposed MADRL method can coordinate thermal turbines, VSCs, as well as EV aggregators in the different control areas. Furthermore, a suitable cyber-attack model that can circumvent bad data detection (BDD) is reconstructed according to the perspective of adversaries for the LFC system. Then the double critic networks and parameter updating policy are designed to eliminate and mitigate the fluctuations caused by cyber-attacks. The comparative simulation with other control strategies on a three-area test power system demonstrates the superior performance of the proposed MADRL-based approach. • Novel MADRL-based control approach for load frequency coordination. • Improved integration of voltage source converters and electric vehicle aggregators. • Enhanced robustness against cyber-attacks and frequency fluctuations. • Meliorative stability and control performance in both time and frequency domain. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have emerged as an indispensable component in the development of green transportation such as electric vehicles (EVs) and large-scale applications of renewable energy such as smart grid energy storage systems. The detection, judgment, and prediction of various battery states such as State of Charge (SOC) and State of Health (SOH) in the battery management system (BMS) play a critical role in guaranteeing the LIBs work under a safe and reliable situation. After decades of intensive investigation, accompanied by the fast development of big-data techniques (BDT) and artificial intelligence (AI) algorithms, the framework of BMS is moving from the traditional onboard system towards the functional integrated scheme. This paper starts with a comprehensive overview of the underlying degradation mechanism of the battery and algorithm distinction and judgment of the battery states in BMS. Subsequently, the paper has systematically reviewed and discussed the most commonly used approaches and state-of-the-art algorithms for battery state estimation in BMS from the perspective of three different BMS configurations: onboard-BMS, cloud-BMS, and functional integrated-BMS. This review expects to stimulate more new insights and encourage more efforts to develop advanced BMS for intelligent and innovative battery control. • Various battery degradation phenomena and their model development are explained. • Three distinct BMS structures—onboard-BMS, cloud-BMS, and Fi-BMS—are explained. • The latest advancements in battery state estimation algorithms are reviewed. • Emerging technical innovation prospects are highlighted in four areas. [ABSTRACT FROM AUTHOR]

This paper presents a day-ahead optimal EV demand management framework to reduce feeder peak demand while ensuring the benefits from energy arbitrage. Leveraging the demand flexibility of EV charging, when appropriately coordinated, holds promise for mitigating feeder demand stress. Existing literature lacks a comprehensive framework to facilitate active orchestration of EV demand flexibility for a hybrid scheme of peak load reduction and energy arbitrage due to limitations of existing pricing schemes to effectively harness EV demand adaptability. To this end, this paper proposes a three-stage EV scheduling strategy that optimally coordinates distribution feeder demand while efficiently allocating the candidate EVs. EV demand, estimated in first stage by an EV aggregator, is further optimized by distribution system operator (DSO) in the second stage leveraging charging flexibility of EVs employing a novel pseudo price function termed "critical peak included locational marginal prices" (CP-LMP). Finally, the third stage entails the EV aggregator maximizing EV allocation. Furthermore, a robust optimization-based operational planning framework is proposed to hedge the uncertainties in EV parameters and their distributions. Comprehensive analyses are presented to validate the efficacy of the proposed methodology. Depending on factors such as EV penetration, permissible load shifting, and charging station capacity, our approach demonstrates a notable 4%–10% reduction in peak load, underscoring the potential benefits of the proposed strategy in enhancing distribution system resilience and efficiency for rapid EV proliferation. • Day-ahead EV scheduling strategy considering peak load reduction and Energy Arbitrage. • A new pseudo-price function is proposed for the optimization. • A Robust optimization based operational planning strategy is proposed. • Uncertainties in arrival time and requested SoC are modeled. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

As the battery recycling market in China is still in an embroynic stage, the mechanisms, channels, pricing, and efficiency of battery recycling are shrouded in opacity. Battery recycling in China is currently dominated by electric vehicle manufacturer, supplemented by other market players such as battery producers and electric vehicle retailers. Due to the lack of information, battery recycling lacks cooperation and communication, and the whole market is dealing with "Information Island." Therefore, based on information sharing and channel competition, this paper designs a Stackelberg game model with an electric vehicle manufacturer, an electric vehicle retailer, and a third-party recycler as the primary participants. This study found that effective information sharing can reduce intensified market competition and promote the transformation of market players into "collaboration state". To promote partnership in the battery recycling market, this paper proposes a revenue-sharing contract incentive for the electric vehicle retailer to share demand information about battery recycling. It finds a revenue-sharing contract based on information sharing can help companies and competitors make appropriate behavioral choices in the battery recycling market. This study demonstrates that information sharing benefits the electric vehicle manufacturer and the third-party recycler while offering no advantages to the electric vehicle retailer. The decreasing accuracy of demand signals results in the diminishing value of information sharing for the electric vehicle manufacturer and the third-party recycler. The revenue-sharing contract can incentivize the electric vehicle retailer to share information. [Display omitted] • A battery recycling mode of electric vehicles is constructed. • Effective information sharing would encourage market players to be more collaborative. • A revenue-sharing contract is designed to incentivize the electric vehicle retailer to share information. • Inaccurate information and channel competition reduce the attractiveness of information sharing. • Inter-firm information sharing is beneficial for building an efficient battery recycling system. [ABSTRACT FROM AUTHOR]

Power grids have undergone a massive transformation due to increasing number of renewable/non renewable distributed generators, storage technologies and electrical vehicles. This paper focuses on the flexible energy management of grid connected microgrid (MG) in order to analyze the effects of the flexibility options such as electric vehicle with demand response (EV-DR), battery storage system (BSS), hydrogen storage system (HSS) and dynamic line rating (DLR) in presence of renewable sources. In addition, microturbine (MT), fuel cell (FC), biomass (BIO), geothermal (GEO), and diesel engine (DIE) whose output is controllable are also considered as flexible sources. In this paper, optimum operation of grid-connected MG is modeled as a mixed-integer linear programming problem considering uncertainties of wind turbine (WT) and photovoltaic (PV) sources. A day-ahead forecasting of irradiance and wind speed are performed with Decision Tree Regression (DTR) and Long Short Term Memory (LSTM) algorithms for sensitive calculation of PV and WT output. The cost function merges operating costs including fuel, operation & maintains (O&M), depletion costs with emission cost of CO 2 , SO 2 , and NO x. Obtained numerical results show that total cost of the MG is reduced by 9.40% and emission cost is diminished by 5.59% with inclusion of all considered flexibility options. Further, load factor is improved from 0.7932 to 0.8236 with 100% flexibility condition for MG. • EV-DR, BSS,HSS and DLR are considered as flexibility options of MG. • MT, FC, BIO, GEO, DIE, PV and WT are modeled as local DGs within MG. • Uncertainty of the PV and WT are accurately forecasted using DTR and LSTM. • Detailed costs of each DGs and ESSs are tabulated for realistic calculation. • Five different cases are employed to evaluate flexible operation of MG. [ABSTRACT FROM AUTHOR]

Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration. [Display omitted] [ABSTRACT FROM AUTHOR]

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

• The source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of EVs, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. • A three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. • Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. To improve the flexibility and robustness of energy systems, we propose a three-stage robust optimization (RO) model for a multi-energy microgrid (MEM). First, the source-load uncertainties within the multi-energy microgrid are comprehensively considered, and a data-driven box-based uncertainty set is formulated. In the uncertainty analysis of electric vehicles, an unsupervised clustering method is introduced in the construction of the data-driven uncertainty set containing EVs. Secondly, a three-stage min–max robust optimization model for MEM is developed to handle multiple uncertainties. Meanwhile, this paper adopts Stackelberg game theory to portray the interaction between multi-energy microgrid system operators and EV users. Finally, this paper employs the column and constraint generation (C&CG) algorithm in solving the robust optimization model and constructs a numerical simulation experiment based on a real integrated energy park model. The simulation results show that the uncertainty set constructed based on historical data can eliminate some low probability cases in describing EV uncertainty. Compared with deterministic optimization, the robust optimization approach can effectively resist multiple uncertainties without significantly increasing the operating cost of the MEM. Meanwhile, the C&CG algorithm can ensure fast convergence of the process when the model solves complex multilevel models. [ABSTRACT FROM AUTHOR]

• Multi-source data and econometric models are used to study charging demands of operating EVs. • Charging demands are spatiotemporal uneven, with peak hours and areas. • Irregular spatial division can mirror spatial features of charging demands. • Low SOCs, parking lots, population and road network density have positive effects on demands. • Charging demands of adjacent units are dependent, especially in peak areas. In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations. [ABSTRACT FROM AUTHOR]

This paper conducts a stated preference (SP) survey by integrating vehicle attributes, policy attributes, and psychological characteristics to explore the mechanisms of personal carbon trading (PCT) and tradable driving credit (TDC), as well as consumer preferences for different types of policy instruments. Using a hybrid choice model (HCM), the empirical results obtained from 1546 Chinese consumers reveal that PCT and TDC are more powerful than most existing policy instruments in encouraging consumers to adopt electric vehicles (EVs). Moreover, although psychological characteristics (i.e., environmental awareness, perceived environmental benefits, and subjective norms) play an important role in explaining consumers' EV adoption behaviour, PCT and TDC primarily alter consumers' EV adoption decisions through economic motivations rather than through psychological motivations. Most importantly, the implementation of PCT and TDC enables consumers to adopt EVs while having no substantial impact on their intention to purchase conventional fuel vehicles (CFVs), thereby laying the groundwork for PCT and TDC to function effectively. This paper contributes to the EV adoption literature by systematically investigating the combined effects of different factors, especially market-oriented policy instruments, on consumers' vehicle adoption decisions. The findings also provide new insights for policymakers to design novel policy instruments to promote EV adoption. [ABSTRACT FROM AUTHOR]

• We propose a procedure to compare micromobility solutions in field trials. • We exemplify the procedure by comparing a bicycle and an e-scooter. • We highlight the limitation of our preliminary results. • We present the potential of the procedure for further applications. Introduction: Recent advances in technology create new opportunities for micro-mobility solutions even as they pose new challenges to transport safety. For instance, in the last few years, e-scooters have become increasingly popular in several cities worldwide; however, in many cases, the municipalities were simply unprepared for the new competition for urban space between traditional road users and e-scooters, so that bans became a necessary, albeit drastic, solution. In many countries, traditional vehicles (such as bicycles) may not be intrinsically safer than e-scooters but are considered less of a safety threat, possibly because—for cyclists—social norms, traffic regulations, and access to infrastructure are established, reducing the number of negative stakeholders. Understanding e-scooter kinematics and e-scooterist behavior may help resolve conflicts among road users, by favoring a data-driven integration of these new e-vehicles into the transport system. In fact, regulations and solutions supported by data are more likely to be acceptable and effective for all stakeholders. As new personal-mobility solutions enter the market, e-scooters may just be the beginning of a micro-mobility revolution. Method: This paper introduces a framework (including planning, execution, analysis, and modeling) for a data-driven evaluation of micro-mobility vehicles. The framework leverages our experience assessing bicycle dynamics in real traffic to make objective and subjective comparisons across different micro-mobility solutions. In this paper, we use the framework to compare bicycles and e-scooters in field tests. Results: The preliminary results show that e-scooters may be more maneuverable and comfortable than bicycles, although the former require longer braking distances. Practical Applications: Data collected from e-scooters may, in the short term, facilitate policy making, geo-fencing solutions, and education; in the long run, the same data will promote the integration of e-scooters into a cooperative transport system in which connected automated vehicles share the urban space with micro-mobility vehicles. Finally, the framework and the models presented in this paper may serve as a reference for the future assessment of new micro-mobility vehicles and their users' behavior (although advances in technology and novel micro-mobility solutions will inevitably require some adjustments). [ABSTRACT FROM AUTHOR]

Renewable Multi-Energy Systems (MESs) are receiving growing attention since they represent a key step towards the decarbonization of electricity, thermal and transport sectors. This paper aims to investigate, from techno-economic perspectives, the performances of an off-grid MES that is designed for satisfying i) the electric and thermal loads of industrial utilities and ii) the energy requirements of novel refueling stations, able to supply electricity for battery electric vehicles fast charging and hydrogen for fuel cell electric vehicles. Wind power is used to meet the electric energy load and the hydrogen demand, while the thermal load is covered by burning biogas and by recovering useful heat inside the system. Two MES configurations, differing in terms of energy storage systems, have been sized by applying an optimization procedure, based on the minimization of the Net Present Cost (NPC), performed by using HOMER Pro software. The annual operations of the MES configurations have been analyzed and the levelized costs of electricity (LCOE), of hydrogen (LCOH) and of thermal energy (LCOT) have been estimated by considering the share of each produced energy stream with respect to the total energy output (or the energy served to the utilities). Results highlighted that the co-production of energy streams allows to significantly reduce their levelized costs with values that are in the range of 0.048–0.054 $/kWh (LCOE), 3.14–3.49 $/kg (LCOH) and 0.037–0.048 $/kWh (LCOT). These costs can be further reduced by connecting the MES to the grid and by delivering the excess of the electricity produced by the renewable source (on-grid configuration). • A techno-economic analysis of two multi-energy system configurations is proposed. • The co-production of green hydrogen, electricity and heat is investigated. • An optimization strategy, aimed to minimize the Net Present Cost, is performed. • The levelized costs of produced hydrogen, electricity and heat are calculated. • The levelized costs of the total output energy are in the range 0.175–0.183 €/kWh. [ABSTRACT FROM AUTHOR]

Policymakers have structured legislation and set targets to increase the adoption of electric vehicles and the deployment of charging stations. As questions arise on how to expand the electric vehicle infrastructure, advanced computational models are attempting to quantify the future placement of charging stations. These models, however, do not necessarily incorporate the variability of user behavior as a subgroup of consumer behavior, as this information is not well-understood. This paper explores a state-wide network of 68 public charging stations' data representing 58,273 charging events to determine users' charging patterns. The methodological approach applied k -means clustering with Minkowski embedded distances to distinguish unique charging subgroups from 608 frequent users. The results indicate four user types: convenient (n = 319, 53%), gradual (n = 147, 24%), anxious (n = 104, 17%), and urgent (n = 38, 6%). Quantifying these users' unique patterns supports the human variability knowledge required for more comprehensive and holistic models of electric vehicle charging station placement. More broadly, these results support the user-centric design of public charging infrastructure, which assists in reducing the electric vehicle adoption threshold in the long run and facilitating the transition to a low-carbon, sustainable transportation system. • Lack of user data has hindered a user-centric EV charging infrastructure design. • Results show four user charging behaviors of a statewide EV charging network. • Anxious charging, affected by range anxiety, is not the primary user group. • Charging stations' siting should include users' variability in charging behavior. • Applying this framework benefits policymakers, vehicle and battery manufacturers. [ABSTRACT FROM AUTHOR]

This paper analytically studies the optimal charging infrastructure investment decisions in parking lots with state-owned firms and private firms, which aims to alleviate range anxiety and facilitate the mass adoption of electric vehicles. The investment decisions are made based on the rational choice in the short term and are assumed to be static. However, the investment in charging infrastructure is a long-term strategy and the decisions might evolve over time. As a result, an evolutionary game theoretic approach is proposed to analytically explore the instability of the investment decisions and derive the optimal investment strategies. We first construct a basic model with a payoff matrix of public and private parking lots and formulate the dynamic replicator equations to find the equilibrium points. We further explore the evolutionary stable strategy (ESS) with its conditions, and analyze the impact of key factors on the strategy evolution. In the basic model, we find that the investment ratio of the state-owned firms and private firms is crucial to the ESS and its original parking revenue has no impact on the investment decisions. To discover the impact of the uncertainty of investments, we next reformulate the model and extend the original payoff matrix by incorporating the perceived value function based on prospect theory. The ESS and its relevant conditions are also derived. We observe that two models have the same ESS in most cases when there is a marked difference in their profitability. When the difference in their profitability is small, the perceived return on investment plays a pivotal role in their investment decisions. Finally, managerial implications are discussed. • Study EV charging infrastructure investment strategies in public and private parking lots. • Examine the decision-making process of state-owned and private firms with bounded rationality. • Quantify the benefits of the charging infrastructure investment in parking lots. • Observe the perceived ROI plays a pivotal role in their investment decisions. [ABSTRACT FROM AUTHOR]

The selection of new energy vehicle power battery (NEVPB) recycling service outlet will promote the rapid development of new energy vehicle industry. In order to cope the green and low-carbon development path of NEVPB recycling under information uncertainty and interaction. This paper intends to construct a multidimensional consensus fusion framework of self-confidence Fermatean fuzzy trust network. Firstly, individuals express their opinions based on Fermatean fuzzy sets with self-confidence (FFS-SC). Subsequently, a novel trust propagation and aggregation is built to obtain complete trust relations, and weights of individuals are determined by trust network, self-confidence, and relative deviation. Meanwhile, the trust network- driven consensus feedback mechanism is developed to collect evaluation information. Furthermore, the correlation coefficient to eliminate the homogeneity influence of indicators. In addition, the decision results are derived by Multi-Attributive Border Approximation area Comparison (MABAC) method. Finally, an applied case with five NEVPB recycling service outlets selection is presented to verify the feasibility of the established framework, and the effectiveness is further demonstrated through sensitivity analysis and comparative analysis. The case results show that the indicator with demonstration of recycling service outlet and market supervision departments strengthen supervision indicators have great impact on NEVPB recycling service outlets selection. Furthermore, this paper has reference significance for standardizing the management policy and technology of power battery recycling and realizing the green and low-carbon environmental protection of the whole life cycle of new energy vehicles. [ABSTRACT FROM AUTHOR]