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Abstract: The polarization phenomenon of papers containing different amounts and different types of inorganic fillers during corona charging was studied using a dose-charging method. This method enables to investigate the phenomena in the conditions close to the real ones. It revealed that the paper composition affects the polarization tendency during the corona charging of paper. The filler influences the paper''s dielectric constant and hence its electrical capacitance, charging potential and polarizing electric field. It was shown that paper polarization depends mainly on the electric field and this is caused by the orientation polarization and the role of the filler origin and its concentration in the polarization was surprisingly low. Polarization temperature dependences enable to propose the paper polarization mechanism. Paper polarizability is dominated by cellulose and the moisture content in it, and the liability of cellulose and hydrogen bonds hydroxyl groups. [Copyright &y& Elsevier]

Decarbonizing road transportation is vital for addressing climate change, given that the sector currently contributes to 16% of global GHG emissions. This paper presents a comparative analysis of electric and hydrogen mobility infrastructures in a remote context, i.e., an off-grid island. The assessment includes resource assessment and sizing of renewable energy power plants to facilitate on-site self-production. We introduce a comprehensive methodology for sizing the overall infrastructure and carry out a set of techno-economic simulations to optimize both energy performance and cost-effectiveness. The levelized cost of driving at the hydrogen refueling station is 0.40 €/km, i.e., 20% lower than the electric charging station. However, when considering the total annualized cost, the battery-electric scenario (110 k€/year) is more favorable compared to the hydrogen scenario (170 k€/year). To facilitate informed decision-making, we employ a multi-criteria decision-making analysis to navigate through the techno-economic findings. When considering a combination of economic and environmental criteria, the hydrogen mobility infrastructure emerges as the preferred solution. However, when energy efficiency is taken into account, electric mobility proves to be more advantageous. [Display omitted] • Decarbonization of public mobility in a remote island context. • Comparative analysis of hydrogen and electric mobility infrastructure. • Techno-economic optimization modeled in PyPSA framework. • Multicriteria Decision Making Analysis to steer policymakers' decisions. [ABSTRACT FROM AUTHOR]

The use of porous 3D current collectors, especially carbon-based ones, has already been demonstrated as a concept of choice as alternative to standard 2D aluminium foil in sulfur positive electrodes. In this study, we investigated non-woven carbon (NwC) as a 3D current collector for Li 2 S positive electrodes. Improved charge and discharge capacities, and capacity retention were obtained, as compared with electrodes containing Al-foil current collector. However, irrespective of the current collector used, the initial charge presents a particular and weakly reproducible voltage shape. By applying GITT and XRD measurements an electrochemical mechanism was proposed to explain the initial charge profile, in terms of equilibrium potential and polarization phenomenon. During the 1st charge, competitive oxidation reactions can be evoked involving both Li 2 S and long polysulfide intermediates (Li 2 S n ), in the main part of the charge process. While the oxidation of Li 2 S is thermodynamically favorable over the oxidation of Li 2 S n , the kinetics of the latter is much more rapid. [ABSTRACT FROM AUTHOR]

Abstract: In this work, a review on the recent progress in understanding defects and instabilities in Hf-dielectric/SiON stacks will be given for both nMOSFETs and pMOSFETs. The key issues addressed for nMOSFETs include the capture cross section of electron traps, their location and justification of the model used. For pMOSFETs, a framework will be proposed for the defects and then used to interpret the anomalous NBTI kinetics. The dominant dielectric layer for positive charging will be identified. Wherever possible, the classical SiO2 will be used as a benchmark and the similarity and difference between the stack and SiO2 will be highlighted. It will be shown that the stack behaves like a degraded SiO2. [Copyright &y& Elsevier]

Abstract: Hole trapping is often considered a parasitic component clouding the real degradation mechanism that is responsible for the negative bias temperature instability (NBTI). As such, it is often dealt with in a rather sketchy way that lacks physical rigor. We review hole trapping mechanisms that go beyond the conventional elastic tunneling mechanism by including structural relaxation and field effects. Contrary to some previous studies, it is shown that the rich spectrum of experimentally observed features of the most commonly observed defect in amorphous oxides, the E′ center, is consistent with experimental data available for NBTI. In particular, we show that a full model that includes the creation of E′ centers from their neutral oxygen vacancy precursors and their ability to be repeatedly charged and discharged prior to total annealing is consistent with a first stage of degradation. In a second stage, positively charged E′ centers can trigger the depassivation of P b centers at the Si/ SiO2 interface or K N centers in oxynitrides to create an unpassivated silicon dangling bond. We formulate a complete model and evaluate it against experimental data. [Copyright &y& Elsevier]

• A novel piezoelectric semiconductor cylindrical shell is proposed. • The principle of virtual work is applied for the governing equations. • 2D behaviors in static bending and forced vibration are considered. • Mechanical displacements and electric potentials are sensitive to the doping level. • New structure and analytical solutions are helpful for sensing and energy harvesting. This paper examines the mechanically induced electric potential and charge redistribution in a piezoelectric semiconductor cylindrical shell employing first-order shear deformation theory. The two-dimensional governing equations and corresponding boundary conditions are simultaneously derived through a principle of virtual work method and the fundamental lemma of the calculus of variation. For the application of electronic devices, static bending and forced vibration analyses of an open cylindrical shell under locally distributed normal forces are analytically solved with the derived theoretical model. The effects of doping level on electric potentials and mechanical displacements are presented. An interesting result shows that doping levels can alter the peak position of the zeroth-order electric potential. In addition, the first three order natural frequencies of the shell under time-harmonic load are identified, and the doping level is found to have an inhibiting effect on the first natural frequency. All the numerical results are beneficial for optimizing the design and performance of cylindrical shell-shaped sensors and energy harvesters. [ABSTRACT FROM AUTHOR]

• A system-level distribution power system integrating energy source, EV consumers and smart meters is modeled for the EVs charging and discharging scenario, leading a new research perspective for studying EVs charging and discharging with consideration of the vehicle owners' information exchange and interactions in the power system framework. • A two-stage optimization problem with priority for EVs charging and discharging is solved with a hierarchical optimal method, which decompose the problem as high-priority load shifting problem and low-priority cost minimization problem. • A penalty-based consensus algorithm is proposed to solve the cost minimization problem, in which two equational constraints are split and two similar optimization sub-problems which can be solved by a consensus distributed algorithm, and the convergence analysis of the algorithm is given mathematically. In addition, this paper combines the hierarchical algorithm with receding horizon method to deal with a more practical situation, where the EVs arrive and leave randomly. In preparing for the widespread adoption of electric vehicles (EVs), this paper investigates a two-stage optimization problem with priority for EV charging and discharging scheduling, while taking into account power load stability, economic objectives, and technical constraints of the distribution network. To this end, a distribution power system integrating energy source and EV owners is modeled. Considering the disturbance or even loss of the communication link, a periodical connected topology is adopted for information exchange among EV owners with the deployed smart meters, which is substantially different from the existing works. The main challenges of the problem are that the information flow among EV owners is time-varying and asymmetric, and each EV owner's demand should be satisfied throughout the whole scheduling horizon. A novelty distributed hierarchical optimal algorithm, combined with iterative water-filling-based method and penalty-based consensus method, is proposed for the two-stage optimization problem with priority. The convergence and optimality of the algorithm are presented mathematically. In addition, the proposed method combined with receding horizon optimization is performed for EVs random arrival and departure scenarios in the system. Finally, we demonstrate advantages of the proposed algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

Conventional online evaluation methods for the mechanical/thermal behaviour of lithium-ion batteries fall short in terms of efficiency and accuracy, especially under extreme conditions coupling high-temperature and fast charging/discharging. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. An electrochemical submodel is formulated, covering the lithium intercalation/deintercalation, solid-electrolyte interphase (SEI) decomposition/regeneration, and electrolyte decomposition. A thermal submodel is created to simulate heat transfer between the cell and its environment, considering reaction and Joule heating as heat sources. A mechanical submodel is developed to reveal the effects of nonlinear elastic constitutive properties and the mechanical/thermal/electrical states on cell deformation and stress, incorporating the reaction gas evolution. A coupled multidisciplinary analytical model is formed, using the state variables of temperature, stress, and deformation to link the submodels. The model's performance was validated against numerical and experimental results under high-temperature charge/discharge cycle conditions, demonstrating efficiency at the level of seconds, temperature errors below 0.6 %, and pressure errors below 4.6 %. The advantages in efficiency, accuracy, and applicability highlight its excellent prospects in energy storage and electric vehicle applications. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. The temperature/pressure/SOC-dependent cell deformation is analyzed based on the nonlinear elastic property. [Display omitted] • A mechanical/thermal analytical model for prismatic lithium-ion cells is proposed. • The model integrates submodels of electrochemistry, heat transfer, and mechanics. • The mechanical submodel incorporates a nonlinear elastic constitutive equation. • The approach evaluates the state evolution in temperature, stress, and deformation. • The accuracy is validated by high-temperature charge/discharge cycle experiments. [ABSTRACT FROM AUTHOR]

The rise of Battery Electric Buses (BEBs) in urban transportation networks, and the adoption of Opportunity Fast-Charging Systems (OFCSs) to support their operation, demand a close examination of the impact of these technologies on protection systems. Thus, this paper analyzes in detail the effects of OFCSs on conventional overcurrent protection schemes and proposes a novel mitigation strategy to the energization and fault plus load effects introduced by this charging strategy on protection systems. The proposed approach includes additional constraints in the overcurrent coordination problem to minimize operation times and uphold coordination delays between main and remote backup protection. Additionally, the resulting optimization problem is solved with the Water Cycle Algorithm (WCA), and is validated on the IEEE 34 and IEEE 123 test systems. In overload situations, the strategy yields a minimal increase of under 200 ms in main protection response times, ensuring non-activation during BEB connections, whereas in non-overload scenarios, optimal settings maintain comparable response times. The paper concludes by recommending coordinated cut-off mechanisms with grid operators to prevent unmet demands during extended BEB connections, addressing critical challenges in the evolving landscape of BEB charging. • A simulation model for an Opportunity Fast-Charging System (OFCS) is developed. • The impact of OFCS on overcurrent protection systems is presented. • A mathematical model for coordinating overcurrent protections is revised and updated. [ABSTRACT FROM AUTHOR]

The increasing pollution caused by conventional cars and the problems caused by the use of fossil fuels have drawn the attention of researchers and manufacturers to the design of cars that use clean fuels. Electric vehicles connected to the network have a significant impact on reducing environmental pollution and transportation costs, especially in big cities. The cost of supplying loads to subscribers in the distribution network also includes generation and transmission costs. These costs are directly related to the intelligence of the distribution network and the total amount of energy of electric vehicles. The contribution of each generation unit and each transmission line must be calculated to determine the generation and transmission costs. In this research, in order to maximize the profit of the parking lot owner, improve voltage drop and load factor, a comprehensive framework for optimal energy management in a parking lot is presented, which can provide a method to control the charging of electric vehicles, in addition to meeting the needs of their owners, only as a series of controllable loads that they need to receive electrical energy to charge their batteries. In the next step, considering the inherent characteristic of electric cars, i.e. having a battery, and looking at them as a series of storage resources that can return the electric energy in their battery to the grid if necessary, a method to simultaneously control their charging and discharging is provided. In the final step of the paper, it is assumed that hydrogen storage systems will also enter the circuit, and thus, a comprehensive method for energy management is proposed. Finally, the linearized model of demand response and the proposed scheme along with the modeling of hydrogen storage and electric vehicles are considered to be part of contribution to improve the operation and economic situation of the network. • In practice, the high cost that battery depreciation imposes on car owners when using the V2G feature makes the use of this feature not economically justified. • A comprehensive framework for optimal energy management in a parking lot with the aim of maximizing the profit of the parking lot owner, improving the voltage drop and network power factor is provided. • The proposed method for energy management in the presence of hydrogenous storage systems was applied in a sample parking lot and how it works. • Considering the hydrogenous storage systems, while significantly increasing the profit of the parking lot owner, it also significantly reduces the power received from the network during the peak hours of the network. [ABSTRACT FROM AUTHOR]

• Effects of two EEVs openings on the CO 2 vapor-injection heat pump were studied. • The regulation characteristics of EEV1 and EEV2 were experimentally analyzed. • The maximum cooling COP occurs as the refrigerant from EEV1 is in a saturated state. • A control strategy for optimal COP was formulated. The regulation characteristics of electronic expansion valves (EEVs) are critical to improving the comprehensive energy efficiency of the heat pump system for electric vehicles. In this paper, a vapor-injection transcritical CO 2 heat pump system for electric vehicles was first established to experimentally study the impacts of EEV1 and EEV2 openings on the overall system performance. The results show that the cooling capacities of the vapor-injection system are respectively increased by 3.13% / 5.75% with the EEV1's opening of 15% / 25% and injection ratio of 0.0975 / 0.8305, compared against these of the basic system without vapor injection. As the injection ratio rises, the cooling capacity increment tends to increase while the cooling coefficient of performance (COP) declines from 1.7 to 1.51. Then, the regulation characteristics of EEV1 and EEV2 were quantitively analyzed, indicating that the evaporation pressure is mainly determined by EEV2's openings. By adjusting EEV1's opening, the COP of the CO 2 vapor-injection heat pump can be maximized at 1.9, with EEV1's opening at 20% and outdoor/indoor ambient temperature at 0°C / 20°C. When adjusting EEV2's opening, the maximum injection pressure corresponds to the maximum COP of 1.84, with EEV2's opening at 65% under 35°C/ 27°C. When the COP reaches the maximum, the refrigerant at the outlet of EEV1 is just saturated liquid under cooling conditions, while it is located in the two-phase region under heating conditions. Besides, the optimal COP regulation strategy of the CO 2 vapor-injection heat pump was further formulated based on two modes, as the strategy in the cooling mode differs from that in the heating mode. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a soft magnetic core (SMC) using laminated nanocrystalline flake ribbons (NFR) to improve the efficiency and thermal performance of EV wireless charging. The nanocrystalline ribbons, with configurable permeability, offer high flexibility, high power density, and low core loss for the vehicle assembly (VA) side in inductive power transfer (IPT) applications. However, the lamination structure and high conductivity of the material present challenges in analyzing loss characteristics. This paper systematically addresses these issues. Firstly, NFR materials with varying permeabilities are analyzed using traditional toroidal measurements. Next, the flux distribution and eddy current losses due to norm flux in IPT are considered. Finite element method (FEM) simulations are then utilized to illustrate the impact of additional eddy current losses and the imbalance caused by the lamination structure. A multi-segment, multi-permeability core structure is proposed to optimize core loss, coupling coefficient, and flux density distributions. Finally, experiments are conducted to assess the new design's performance. The proposed structure achieves a DC-DC efficiency of 95.68% at 12.78 kW output power and maintains optimized efficiency even with aluminum shielding. Additionally, improvements in temperature distribution uniformity and thermal stability are observed, demonstrating superior performance compared to traditional ferrites. • Soft magnetic cores affect efficiency & thermal performance in EV wireless charging • Loss analysis conducted for permeability-configurable NFR cores in IPT applications • FEM reveals the loss features and imbalances in flux and loss distributions • Optimization process considers the core loss, coupling, and thermal distributions • The multi-segment multi-permeability core improves the overall system performance [ABSTRACT FROM AUTHOR]

The Electric Vehicle Parking Lot (EVPL) aims to address the growing demand for EV charging infrastructure. EVs connected to EVPLs have extended access times compared to those at public charging stations. Consequently, EVPL owners can aggregate the schedulable capacity of connected EVs to participate in day-ahead and ancillary service markets, thereby gaining economic benefits. However, achieving this objective is hindered by the lack of accurate day-ahead forecasts of EV charging behavior. To address this issue, this paper introduces a novel day-ahead forecasting method for EV charging behavior at EVPLs, alongside a strategy for calculating EV schedulable capacity based on these forecasts. Unlike existing methods, this paper presents a day-ahead time-of-use clustering forecasting strategy, which provides more detailed and accurate predictions of EV charging behavior, eliminating the need for numerous assumptions during schedulable capacity calculations. The study demonstrates that the proposed method, validated using actual historical data, enables precise forecasting of EV charging behavior. Furthermore, the proposed day-ahead schedulable capacity calculation strategy is shown to be both effective and practical. • A day-ahead forecasting method for EVCB is proposed, considering the variations in EVCB at different time periods. • To achieve accurate day-ahead estimation of EVCB for EVPL, minimizing the need for extensive assumptions. • A classification and analysis of EVCB within EVPL, combined with the physical constraints of EV, to construct EVSFD. • Based on the day-ahead planning curve and EVSFD, a method for calculating the EVSC of EVPL is proposed. [ABSTRACT FROM AUTHOR]

• Propose a zonal price optimization mechanism based on regional energy redundancy. Targeting the issue of different regional energy imbalances in IES, different EV charging prices are used in different regions to guide EV charging demand at different locations and times. Compared to Time of Use pricing (TOU), the use of Regional Time of Use pricing (RTOU) reduces EV charging costs by 48.31 %. Simulation results demonstrate the superiority of RTOU in guiding EV charging demand. • Addressing the multi-objective optimization issue in IES optimization dispatch, propose a multi-objective weight optimization approach based on single-indicator weight sensitivity. The key focus of the proposed method lies in considering the sensitivity of individual indicators as the weight increases. By allocating weights based on the differences in indicator sensitivity to weights, the Energy Input-Output (EIO) increases by 1.73 %, validating the effectiveness and superiority of this optimization approach. • Introduce an EW-TOPSIS method for multi-index weight optimization. Utilizing the concept of entropy weight to characterize the sensitivity of a single indicator as the weight changes, the EW-TOPSIS algorithm combines the TOPSIS method to compare and evaluate multiple indicators and ultimately quantitatively assign weights to the indicators. Compared to the weights optimized using EW, the EW-TOPSIS method increases the EIO by 1.66 %, demonstrating that the EW-TOPSIS algorithm effectively evaluates the differentiated results of multiple indicators as the weight increases and has a good application effect in multi-objective weight optimization problems. The integration of Electric Vehicles (EV) and diverse energy users in an Integrated Energy System (IES) poses a key challenge in IES optimization dispatch. This paper presents an IES optimization dispatch method that incorporates EV zoning electricity prices and multi-objective weight optimization. Firstly, the power flow calculation is integrated into the IES optimization dispatch, establishing a comprehensive IES network power flow model that considers electricity, gas, and heat. Next, an innovative Regional Time of Use Price (RTOU) is developed based on regional energy redundancy and the division of IES regions. This RTOU guides the charging demand of EVs in different regions and at different times. Subsequently, an approach for multi-objective weight optimization is proposed, which takes into account the differences in weight response and the incongruity of indicator meanings in the IES context. This approach utilizes a single-indicator weight sensitivity matrix to optimize the weights and incorporates the EW-TOPSIS evaluation method to achieve multi-dimensional weight optimization in IES. Finally, the second-order cone programming method is employed to solve the IES power flow model. This verifies the practical effectiveness of the proposed weight optimization approach based on the single-indicator weight sensitivity matrix and analyzes the application effect of the EW-TOPSIS algorithm in multi-objective optimization. Simulation results demonstrate the positive outcomes of this research: (1) The use of RTOU reduces the additional charging cost of EVs by 5.24 % compared to the use of Time of Use (TOU), validating the effectiveness of the improved pricing optimization mechanism. (2) The weight optimization method based on weight sensitivity increases the weight utilization index by 1.73 % compared to the use of normalized weights. The findings in this paper serve as a valuable reference for addressing multi-objective optimization problems in future IES dispatch work. [ABSTRACT FROM AUTHOR]

• Three types of IBC structures were conducted for perovskite solar cells by numerical simulations to review their optical response. • Anti-reflection coating with an optimal thickness of 70–80 nm plays an important in determining the optical performance. • The optimal sizes are confirmed by sweeping the structure parameters, suggesting a high photocurrent density of 24.49 mA/cm2. • The light-trapping mechanism is revealed by the position-dependent current density and electric-field distributions. Interdigitated back-contact (IBC) structure design is a promising alternative to further promote the optical absorption and the power conversion efficiency of perovskite solar cells (PSCs). However, their full potential has not been reached owing to the absence of the advanced structure designs and a lack of a deep understanding on the related physics mechanism. In this paper, we proposed three typical IBC configurations featuring flat perovskite layer, textured electron transport layer (ETL) and hole transport layer (HTL), and textured HTL but flat ETL. By the numerical simulations, the optical properties of the three related designs are investigated systematically. The simulation results reveal that anti-reflection coating (ARC) thickness plays an important in determining the device performance and the optimal ARC thickness is suggested to be 70–80 nm. The optimal structure sizes are confirmed by optimizing the structure parameters. In addition, the light-trapping mechanism is clarified by the position-determined photocurrent density and the spatial electric field distributions. Moreover, dependence of optical response under the various perovskite thickness is checked, suggesting that textured IBC structure design is good to achieve high optical absorption. The simulation results demonstrated in this paper provide a valuable guide to design IBC PSCs with high optical performance. [ABSTRACT FROM AUTHOR]

• An all-solid-state thin-layer laminated cell realized calibration-free coulometric determination of K+ ions. • The cell achieved complete mass transport of the target ion from a 1 mm3 drop of the aqueous sample. • An originally prepared conducting-polymer-dispersed ink was cast on the carbon electrode. • The cell allows the calibration-free determination of K+ ions in a 1 mm3 drop of ten-fold-diluted serum. Calibration-free coulometric determination of K+ ions was realized using an all-solid-state thin-layer laminated cell with a simple laminate structure. The fabricated cell consists of a Ag/AgCl electrode, a plastic spacer for positioning the aqueous sample solution, an organic ion-selective membrane, and a conducting polymer/carbon electrode. An originally prepared conducting-polymer-dispersed ink was cast on a conductive carbon sheet (a screen-printed graphite electrode or carbon paper) to fabricate the conducting polymer/carbon electrode. The cell achieved complete mass transport of the target ion from a 1 mm3 drop of the aqueous sample to the organic ion-selective membrane under a constant potential between the Ag/AgCl electrode and the conducting polymer/carbon electrode, which generated electrical current for enabling the ion transfer. The electric charge obtained by integrating the electric current was used to estimate the molar amount of the target ion in the sample drop. For tetraethylammonium cations (TEA+; 20–200 μmol dm−3), the evaluated molar amounts corresponded to 92%–94% of the initial mole amounts. The repeatability estimated using the same cell was ±4%–6%, and the device-to-device reproducibility was ±4% for five cells. In terms of K+-ion detection (0.1–0.8 mmol dm−3) in sample drops containing NaCl (14 mmol dm−3), the evaluated molar amounts corresponded to 86%–92% of the initial molar amounts, indicating the promise of the all-solid-state thin-layer laminated cell for achieving the calibration-free determination of K+ ions in a 1 mm3 drop of ten-fold-diluted serum. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a novel GaSe/ZrS 2 heterojunction is designed and systematically studied. The monolayers in this heterojunction can be easily fabricated and the heterojunction has good stability. More importantly, electronic structures, band alignment, differential charge density and the built-in electric field show that it is a direct Z-scheme photocatalyst that can not only effectively separate photogenerated carriers but also improve the redox ability of carriers. The mobility of the electrons in this heterostructure is as large as 1450.38 cm2V−1s−1 (same magnitude order of black phosphorene) along the zigzag direction, which is beneficial to photocatalytic performance. The band alignment shows that GaSe/ZrS 2 is suitable for overall water splitting and the splitting can happen spontaneously. Moreover, GaSe/ZrS 2 heterojunction has excellent absorption coefficients (higher than 105cm−1) in both visible and ultraviolet ranges which is very beneficial to photocatalytic overall water splitting. The study of electric field and biaxial strain effects on GaSe/ZrS 2 heterojunction show that the light absorption coefficients of GaSe/ZrS 2 heterojunction can be obviously further improved by applying biaxial strain. Our work shows that GaSe/ZrS 2 heterojunction is a promising photocatalysts for overall water splitting. • GaSe/ZrS 2 heterojunction is a direct Z-scheme heterojunction with suitable redox potential for overall water splitting. • GaSe/ZrS 2 heterojunction has excellent absorption coefficients (>105cm−1) in both visible and ultraviolet ranges. • The mobility of electrons in GaSe/ZrS 2 heterostructure is as large as 1450.38 cm2 V−1s−1 along zigzag direction. [ABSTRACT FROM AUTHOR]

• Optimal location of interurban electric vehicle charging station infrastructure. • Multi-objective genetic algorithm to improve stalls utility and stations coverage. • Experimental results in the USA improve the current infrastructure. In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm. [ABSTRACT FROM AUTHOR]

Under the pressure of energy and environmental protection, new energy vehicles have become the future direction of automotive development. However, the safety performance of the power battery has always been the most critical indicator in the new energy vehicle industry. The battery will be aged in the continuous charging and discharging cycle, and the aging will cause safety hazards when it reaches a limit. A model that can predict the battery life can be obtained using Machine Learning. To obtain models that can predict power battery life relatively accurately, this paper revolves around the chaos sparrow search optimization algorithm, Random Forest, XGBoost, LightGBM, CatBoost, and NN, the importance assessment of the features, the hyperparameter search process, and the comparison of the differences and performance between the different algorithms are discussed. CatBoost has the highest prediction accuracy, with the amount of predicted data with a relative error of less than 10% being 88.44%. (a total of 10,275 data in the test set). And finally comes up with a general approach to predicting power battery life using Machine Learning. [ABSTRACT FROM AUTHOR]

In this paper, two distributed finite-time neurodynamic algorithms are proposed to collaboratively manage the charging scheme of electric vehicles (EVs) in the microgrid scenario. First, the upper level model is constructed to optimize the disorderly charging problem of EV users under private charging posts , and explore the optimal charging scheme under charging constraints and time-varying conditions to ensure the benefits of users. The lower layer model explores the optimal public charging scheme under the system operation constraint and the supply–demand balance constraint with the objective of minimizing the overall microgrid operation cost. The optimal solution of the upper model, i.e., the load of EV users under the private charging post, is considered as a parameter of the lower model. In this context, two finite-time neurodynamics with fast convergence rate, executed in a distributed manner, are proposed to track the optimal solution of the problem in real time. Furthermore, the stability and convergence in finite time of the two proposed algorithms are proved using Lyapunov theorem and finite time theorem. Numerical case studies of small-scale and large-scale power systems demonstrate the effectiveness, robustness, and real-time performance of the two proposed algorithms. [ABSTRACT FROM AUTHOR]

• A significant wake is formed behind thin cylindrical Langmuir probes. • Thin Langmuir probes ought to be at least ∼15 Debye lengths apart to not interfere. • An external magnetic field enhances and skews the wake behind a thin probe. This paper presents a simulation study of the wake formation behind a Langmuir probe thinner than the Debye Length in the space environments such as the ionosphere's F region. We find that the wakes formed in plasma density and electric potential behind the positively biased probe can extend up to 15 Debye lengths in the subsonic plasma flow. Higher electric bias and flow velocity can further enhance the plasma wake perturbations. With an external magnetic field parallel to the object's axis, the plasma wake becomes asymmetric and more extensive than for the unmagnetized case. The wing structures in the electron and ion densities are also observed along the background magnetic field in the case of subsonic plasma flow. The quantitative results in this paper may provide a practical reference for data processing and the future design of the Langmuir probe instrument flying in the F region on such as sounding rockets and low Earth orbit (LEO) satellite missions. In particular, since the Langmuir probe instrument may comprise two or more biased Debye-scale Langmuir probes, the plasma wake formed behind a single probe may influence the measurements from other instruments onboard. [ABSTRACT FROM AUTHOR]

In this paper, six kinds of two-stage ESPs with various ratios of charger and collector units are designed to research the capture enhancement mechanism of submicron particles. The working conditions are optimized by studying the influence of gas velocity, charger current, and electric field strength of collector. Results show that the electrostatic field characteristics of the two-stage ESP with Ra = 1/6 is beneficial to improve the collection efficiency while slightly less stable. The indicator factor of particle charging enhancement effect of two-stage ESP with Ra = 6/1 fluctuates between 0.06 and 0.1, which suggests that the two-stage ESP with a higher proportion of charger units cannot achieve the satisfying performance for the limitation of ionic wind effect. Under comprehensive consideration of ionic wind effect and ozone emission, the two-stage ESP with Ra = 2/5 is selected at the first priority by exerting the advantages of fully particle charging and enough collection space, which has a strong potential in both particle charging enhancement and electric field enhancement. However, for improving the collection efficiency of submicron particle with a size ranging from 0.2 to 0.5 µm which suffers from both of weak particle charging and agglomeration effect, the two-stage ESP with Ra = 4/3 is not recommended. [Display omitted] • Submicron particles with the lowest collection efficiency are selected as target. • Capture enhancement mechanism is investigated by designing two-stage ESPs. • Working conditions are optimized by studying the effect of operating parameters. • The electrostatic capture equipment with appropriate unit proportion is selected. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of ductility changes, ratcheting, and low-cycle fatigue behavior of materials after hydrogen charging. Hot-rolled ribbed bar 400 (HRB400) steel was electrochemically charged with H 2 SO 4 solution, and the samples subjected to different hydrogen charging times were subjected to quasi-static tensile and low-cycle fatigue tests under asymmetric cyclic loading. The tests indicated that hydrogen charging changed the initial yield stress of the material, but the ultimate strength remained almost unchanged; hydrogen charging significantly changed the cyclic hysteresis behavior of the material, which increased the elastic range of the stable hysteresis curve; hydrogen charging changed the pattern of ratcheting deformation. The pattern of ratcheting deformation in the later stage was very different from that of the sample without hydrogen charging; hydrogen charging reduced the ductility of the material and resulted in a significant decrease in fatigue life. The fracturing of the hydrogen-charged samples exhibited apparent quasi-cleavage and intergranular fracture characteristics. The non-hydrogen-charged samples had two failure modes (fatigue failure and increased ratcheting deformation resulting in necking instability), whereas the hydrogen-charged samples had only one failure mode of fatigue failure. Based on the experimental analysis, a fatigue life equation that simultaneously considers the effect of hydrogen embrittlement index, mean stress, and stress amplitude is proposed. The equation was used to predict the low-cycle fatigue life of hydrogen-charged specimens under asymmetric cyclic loading, and the error between the results and the measured values was in the two-factor band. [Display omitted] • Hydrogen charging causes the ratchet deformation mode change of HRB400 steel. • Hydrogen charging greatly reduces the critical value of material fatigue damage. • Hydrogen charging weakens the material's energy dissipation capacity in hysteresis. • Position of fatigue sources varied with hydrogen charging. • A fatigue life formula considering the effect of hydrogen is proposed. [ABSTRACT FROM AUTHOR]

The governmental subsidy is an important instrument for catalyzing the adoption of electric vehicles (EVs). A sustainable subsidy policy must balance between promoting EVs and expanding the charging infrastructure. This paper proposes a dynamic game approach for computing the optimal subsidies, taking into consideration the cross-side network effect between EV adoption and charging infrastructure expansion. The government's objective is to expedite the adoption of EV technology, maintain sustainable EV-to-CS ratios, and maximize cumulative social benefit in the face of uncertainty. The case study of China's EV industry indicates that the existing EV subsidy program, known as the Dual-Credit Policy (DCP), will not be sufficient to support EV growth by 2035 due to inadequate infrastructure investment. In contrast, the optimal subsidies will achieve the target EV market penetration rate and ensure the accessibility to charging infrastructure by 2035. This non-myopic strategy can prioritize the infrastructure expansion, eliminate policy enforcement's monitoring costs, prevent compensation evasion, and help achieve long-term targets by making proactive regulatory policies. [ABSTRACT FROM AUTHOR]

In this paper, a novel Energy Management System (EMS) algorithm to achieve optimal Electric Vehicle (EV) charging scheduling at the parking lots of electric railway stations is proposed. The proposed approach uncovers the potential of leveraging EV charging flexibility to prevent overloading in the combined EV charging and railway operation along with renewable generation, railway regenerative capabilities, and energy storage. Specifically, to realize end-user flexibility, each EV state of charge at departure time is introduced as an optimization variable. Peak load constraints are included in the railway EMS to properly adjust EV charging requirements during periods of high railway demand. A comprehensive numerical study using a scenario-tree approach on an actual railway line in Switzerland demonstrates the effectiveness and the feasibility of the proposed method in a practical setting under multiple scenarios. • Railway energy management can include optimal flexible EV charging scheduling. • Regenerative braking and renewable generation can enhance EV customer satisfaction. • Peak load constraints can reduce the power spikes compared to uncoordinated charging. [ABSTRACT FROM AUTHOR]

The fast proliferation of electric vehicles (EVs) profoundly affects the stable operation of the power system. Since EV charging demand is affected by users volatile behaviors and many other environmental and social factors, their uncertainty is quite pronounced. Traditional deterministic forecasting methods which provide little information on the uncertainty may not be informative to the decision-making of power system with massive EVs. This paper proposes a novel feature-enhanced deep learning method (FEDM) for EV charging demand probabilistic forecasting of individual station, in which a two-stage feature selection model is constructed to introduce the Pearson correlation coefficient between charging demand and external features as a prior knowledge to reweight the initial weights. The performance is improved compared to which is without feature-enhanced. In the comparison with eight benchmarks, FEDM shows superiority in both deterministic and probabilistic evaluation metrics. With high computational efficiency, FEDM has low dependence on the historical data stock, which can still achieve good performance in the case of low data volume. It provides a fast and accurate forecasting scheme for the demand forecasting of individual stations under the limitation of low data stock. • The proposed approach improves the accuracy rate, qualified rate, sharpness and computational efficiency. • A novel feature enhanced mechanism is proposed to introduce the correlation coefficient as the prior knowledge of the neural network. • The proposed approach can provide an excellent forecasting performance in the constraints of low data volume. [ABSTRACT FROM AUTHOR]

The use of electric vehicles and renewable energy sources can significantly contribute to the reduction of pollution levels, fostering a cleaner and more sustainable environment. In addition to their positive environmental impact, electric vehicles can also serve as energy storage units, enabling a bidirectional energy flow between the vehicles and the grid. Despite these promising prospects, a disjointed approach to charging infrastructure planning overlooks the synergies between the energy and transportation ecosystem. This calls for a holistic and coordinated approach to infrastructure development that harnesses the benefits emerging from the convergence of electric vehicles, renewable energy, and transport planning. This paper presents a methodology for the optimal placement of future Energy Hubs for electric vehicle charging and renewable generation. The methodology uses data from open-source datasets involving renewable energy sources, traffic density, charging patterns, road topology, and land use. The optimization problem is formulated as a multi-objective linear programming problem, with the objectives of minimizing the number of Energy Hub units, minimizing electric vehicles charging radius coverage and distance from power substations, and maximizing energy generation. The methodology is demonstrated through an application in Trondheim, Norway, which produces a Pareto front for decision-makers to evaluate the best trade-offs between objectives. • Energy hubs concept is proposed for electric vehicle charging and renewable generation. • Energy hubs placement is based on multi-objective optimization and open-source data. • The methodology is applied to a case study based in Trondheim, Norway. • Results in a Pareto Front support evaluation of trade-off between adverse objectives. [ABSTRACT FROM AUTHOR]

• Novel energy management for battery & fuel cell bus charging stations is proposed. • Comparative grid vs. stand-alone electric bus charging analysis is demonstrated. • Achieving optimal energy management with reliable scheduling for buses charging. • Novel priority selection for bus charging based on trip energy consumption. • Grid-connected charging station gives lower energy cost than standalone. The global shift towards sustainable transportation has led to a surge in the adoption of electric and fuel cell vehicles. This paper introduces a novel energy management approach for the charging stations that serve both battery electric buses and fuel cell buses. Addressing the challenge of guaranteeing enough energy for all buses in the system, a novel priority charging approach is adopted. This approach involves a sophisticated control system that predicts which buses will receive the required energy to complete their designated routes. Minimizing the cost of the charging energy while ensuring adequate charging for buses across their entire routes without energy interruption is pursued. Six scenarios have been studied and compared, comprising a standalone and grid-connected photovoltaic-based charging station, considering all buses are either battery electric buses, fuel cell buses or a hybrid combination of both. The obtained results show that the grid-connected with battery electric buses gives the lowest energy cost of $0.0162/kWh. Furthermore, the study highlights the potential of grid-connected charging station accommodating both electric and fuel cell buses striking a balance between cost-effectiveness and fleet diversity. [ABSTRACT FROM AUTHOR]

• The failure of BEVs to provide long ranges in short charging times is a barrier to their adoption. • It can be overcome if consumers are willing to trade off range for charging infrastructure. • We use meta-regression to analyse the trade-offs individuals are willing to make. • Consumers are willing to accept compensations, but only if ranges are higher than 250–300 km. • This shows that an important technical barrier can be overcome with infrastructure investments. The failure of battery electric vehicles (BEVs) to provide sufficiently long ranges in a relatively short charging time (as compared with conventional vehicles) is one of the main technical barriers to consumers' adoption of these vehicles. However, this technical barrier could be mitigated if consumers were willing to trade-off lower ranges with better charging infrastructure. This paper analyses trade-offs individuals are willing to make between range and en-route charging station availability when considering the purchase of BEVs. By means of a meta-regression analysis of 33 stated preference studies, we find that consumers are willing to accept compensations for lower ranges in form of more dense charging infrastructure in their BEV purchase decision, but only when BEV ranges are longer than 250–300 km. This result is very relevant to promote the transition to BEVs because it shows that, in some circumstances, a significant technical barrier can be overcome with infrastructure investments. [ABSTRACT FROM AUTHOR]

This paper provides insights for cyber defenders of power systems with high penetration of electric vehicles (EVs) by proposing a novel consecutive attack model based on load redistribution for price control in both transmission networks (TNs) and distribution networks (DNs). The target of the attack is to induce massive EV users in DNs to charge simultaneously and cause a spike of loads in the TN at peak hours. The problem is formulated as a multi-slot bi-level optimization problem, where the upper level describes the hacker behavior and attacking constraints. The lower level explains the TN operator and DN operators' behaviors and the relationship between the local marginal price and retail charging price. The bi-level problem is converted into an equivalent single-level mix-integer linear problem and is solved based on greedy algorithm. Simulations on IEEE 30-bus system prove the effectiveness of the attack strategy. • The local marginal price and electric vehicle charging price are introduced into the load redistribution model. • Hackers trigger a load spike of EVs in peak hours by load redistribution attacks. • The multi-slot bi-level optimization problem is reduced to a mix-integer linear problem. • The instant attack and consecutive attack are carried out in the IEEE 30-bus test system. [ABSTRACT FROM AUTHOR]

• A V2G demand response mode for industrial and commercial enterprise (ICE) is proposed. • The spatiotemporal characteristics of EV are analysed. • A pricing strategy of V2G demand response for ICE based on cooperative game is put forward. The electricity consumption and electricity cost of industrial and commercial enterprises (ICEs) are high, and the disorderly charging and discharging of electric vehicles (EVs) brings a great risk to the safe and stable operation of the power grid. Therefore, this paper proposes a V2G demand response pricing strategy for ICE based on cooperative game. Firstly, a V2G demand response mode of ICE is proposed, in which the costs and benefits of both ICE and the corresponding EV employees (EVEs) are analysed, laying the foundation for determining the optimal V2G demand response price. Then, an optimal pricing strategy of V2G demand response for ICE based on cooperative game is put forward, considering the benefits of both ICE and EVE. Finally, an enterprise located in a high-tech zone in Zhejiang province is taken as a case study. The results show that the proposed cooperative game-based pricing strategy of V2G demand response for ICE can increase the benefit of EVE, and reduce the electricity consumption cost of ICE, thus realizing the win–win situation for both ICE and EVE. [ABSTRACT FROM AUTHOR]

Electric Vehicles are emerging as preferred means of transport due to rising concerns regarding the increased harmful emission with fossil fuels. The integration of the EV in the distribution network has severe effects which create unbalance in the network and will lead to a reduction in the reliability of the network. This work deals with the impact analysis of the electric vehicle charging stations on the distribution system and assesses the hosting capability of the distribution system by formulating several scenarios. In this paper, the IEEE-33 & IEEE-69 bus systems have been modified to mimic the practical distribution network. The proposed formulation is done on MATLAB, and results show that up-to 40% EV penetration is tolerable by the existing distribution networks. and as the penetration increases, the permissible limit gets violated. This research work provides a comprehensive analysis of the impact caused due to residential and public based charging of electric vehicles and can be applied to different networks to assess the hosting capacity of the distribution network. [ABSTRACT FROM AUTHOR]

Policymakers are in a balancing act when creating local energy transition strategies. Embedding new technologies in an existing energy system is highly complex. Policymakers must deal with multi-system interactions such as sector coupling, multi-scale effects such as bottom-up behavior and top-down policies, and requirements from local spatial planning, grid constraints, and resource availability. Decision support tools can help to navigate this complex landscape. This paper showcases a tool to support policymakers with heating strategies for Dutch neighborhoods. The tool is a GIS-based simulation model of the energy system developed using a collaborative approach and applied in a scenario study. Energy calculations are done over a year with an hourly resolution, while scenarios can include any future energy system configuration. The results highlight trade-offs between heating strategies, interaction effects with the mobility and electricity transitions, and bottlenecks in transition pathways. Collective district heating has less grid impact but higher emissions and costs, while individual (hybrid) heat pumps have lower emissions and costs but more grid impact. No-regrets and enabling technologies are insulation and smart charging of electric vehicles and boilers. Collaborative modeling with a GIS-based user-interface increases system understanding, including trade-offs, transition pathways, and bottlenecks, in a collective and interactive way. This creates a shared and well-grounded vision, resulting in robust local renewable energy strategies. • A multi-system, multi-scale decision support tool is an essential contribution to local renewable energy system planning. • There is no clear winner in renewable heating strategies in residential neighborhoods in The Netherlands. • Trade-offs occur in terms of costs, grid congestion, and emission reductions. • Models can be used to identify lock-ins and bottlenecks in energy transition pathways. • Collaborative modeling for renewable energy strategies increases system understanding and more robust decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the topological numbers of rotating charged AdS black holes in both four- and five-dimensional gauged supergravity theories. Our analysis is conducted within the framework of the thermodynamical topological approach to black holes, utilizing the generalized off-shell Helmholtz free energy. We demonstrate that the number of rotation parameters plays a significant role in determining the topological numbers of five-dimensional rotating AdS black holes. Moreover, our findings indicate that the topological numbers of both four- and five-dimensional rotating AdS black holes are not influenced by the number of electric charge parameters. This highlights a distinct difference in how rotation and electric charge parameters impact the thermodynamic topological properties of these black holes. [ABSTRACT FROM AUTHOR]

This paper fills the research gap between theoretical vehicle routing algorithms and practical solutions in the field. We use commercially developed prediction algorithms for the energy consumption of vehicles and solve for the energy-efficient routing and charging strategies of an electric vehicle fleet to visit a given set of destinations using meta-heuristics. Then we validate the energy saving performance of the efficient routing solutions with real-world vehicle measurements in a real traffic network. We also conduct a sensitivity analysis via simulation to explore some critical factors in reducing energy consumption. Current literature on vehicle routing problems is limited because they do not validate the energy consumption or the travel time of the solution in the field. This opens questions regarding the routing performance, which faces significant uncertainty from a real traffic network. Also, the travel costs in the network, such as energy consumption and travel time, have been predicted with simple or physics-based models in the theoretical studies, which are limited in capturing the complexity of a real traffic network. Our contributions are as follows. First, we develop a comprehensive framework for routing and charging an electric fleet, integrated with a high-precision prediction algorithm of energy and travel time based on learning a large driving data set. Second, we validate our routing performance in the San Francisco East Bay area with an energy consumption reduction of up to 31% compared to a baseline. Third, we conduct a sensitivity analysis via simulation on some critical constraints of vehicle routing. We observe that relaxing the limitation on operation duration and battery size on the vehicle fleet can expand the solution's feasible space and reduce the optimal energy consumption; however, the benefits diminish as constraints are relaxed to a certain point. • We develop an electric vehicle routing solution with precise travel cost prediction. • We validate the vehicle routing solution in the San Francisco bay area. • Field experiments show energy savings up to 31%. • Simulation tests analyze critical factors of energy savings for electric logistics. [ABSTRACT FROM AUTHOR]

Intelligent adaptive extended Kalman filter (IAEKF) is based on the maximum likelihood (ML) method and is widely used to estimate the state of charge (SOC) of EV lithium batteries. The method changes the length of the error innovation sequence (EIS - composed of several error innovation points) in real-time by monitoring whether the error innovation point (EIP), which is the difference between the predicted and observed values in the Kalman filter, at k-1 time changes the distribution of EIS. Subsequently, the classical noise adaptive formula can be used to update the covariance R and Q values at k time. However, in cases where the EIP at k-1 time does not cause changes in the EIS distribution but the EIP at k time results in alterations in the EIS distribution, employing the EIP at k-1 time to assess changes in the EIS distribution and adjusting the EIS length will yield significant errors. Moreover, IAEKF is greatly affected by the accuracy of model parameters and has high requirements for parameter identification and precision of various sensors. To address these limitations, this paper proposed an estimation method based on genetic algorithm and long short term memory (GA-LSTM) and improved Intelligent adaptive Kalman filter (IAKF). The output state of charge (SOC) value of the LSTM neural network is used to replace the observed terminal voltage value in IAEKF, and the initial parameter of the LSTM network is obtained by GA. The improved IAKF employs a symmetric assignment method to determine whether the EIP at k time causes changes in the EIS distribution; furthermore, the noise adaptive formula was modified based on the EIS distribution variation to greatly improve the estimation accuracy and robustness of SOC. Finally, under DST, FUDS and US06 conditions, the maximum errors (ME) were 1.87%, 1.87% and 1.84%, and the root mean squared error (RMSE) was 0.77%, 0.78% and 0.95%, respectively. The results revealed that the proposed method had good accuracy and robustness. • A combined state of charge estimation method of neural network and filter. • Genetic algorithm was used to optimize the long short term memory neural network. • The criterion of judging the length of error innovation sequence was improved. • The noise adaptive formula of adaptive Kalman filter was modified classificationally. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of urban charging facilities expansion in response to the growing charging demand and proposes a data-driven bi-level optimization framework. The proposed framework combines a mixed-integer programming model for siting and capacity planning, an improved sparrow search algorithm (ISSA) for hyperparameter optimization of the CNN-BiLSTM model, and the application of Benders decomposition with Lagrangian relaxation using Gurobi for solving the model. The proposed approach is empirically evaluated through a case study conducted in an administrative area of a Chinese city, using comprehensive charging records and other relevant information. The results demonstrate that the ISSA consistently outperforms other algorithms, showing an average improvement of 23.97 % in root mean square error, 19.25 % in average absolute percentage error, and 1.50 % in the determination coefficient. The modified Diebold-Mariano test confirms the significant advantage of ISSA. Based on the model analysis, the study recommends constructing 20 new charging stations in the case city, with more than half equipped with fast chargers. This strategy ensures that 95 % of the area can find a charging station within 5 min while also ensuring the operator's profitability. The research findings provide effective support for investment decisions related to charging facilities. • A data-driven optimization framework is designed to integrate diverse urban data. • An optimization model based on benders decomposition is studied. • A case study in a Chinese city is implement to validate the proposed method. • An investment analyze is made to evaluate expansion plan rationality. [ABSTRACT FROM AUTHOR]

• Charging frequencies and patterns are estimated to be radically different between irregular and regular chargers. • Regular chargers are found to charge their vehicles 1.5 times more than irregular chargers. • Neither technological or user factors are predicted to substantially affect the inter-charging duration of irregular chargers. Electrification of transport is deemed by many countries worldwide as one of the key strategies to mitigate CO 2 emissions, yet the availability of reliable public charging infrastructure systems represents a potential serious bottleneck to such endeavours. Existing studies exploring battery electric vehicle (BEV) charging behaviour are typically based on either non-representative samples or stated choices experiments. This paper analyses observational data from a representative sample of German BEV owners who provided information on mileage and charging activities over a timeframe of eight weeks. BEV charging patterns, related vehicles kilometres travelled (VKT) and battery charging behaviour are assessed via a multifaceted empirical framework that pairs a hazard survival-based model with a log linear regression approach. A latent class method is also employed to segment BEV owners into different charging segments. The model suggests two types of charging behaviour exist, consisting of regular and irregular chargers. Charging frequencies and patterns are found to be radically different between the two groups under study, with regular chargers estimated to charge their vehicles 1.5 times more than irregular chargers. Lastly, the framework proposed is used to explore how charging behaviour will mutate due to both technology advancements (BEV driving range improvements) and user-centric factors (VKT variations). Neither technological or user factors are predicted to substantially affect the inter-charging duration of irregular chargers, whereas both increasing BEV driving ranges and reducing VKT results in a longer elapsed time between two consecutive charges for regular chargers. [ABSTRACT FROM AUTHOR]

In this paper, we study the properties of electrically charged anisotropic and spherically symmetric compact stars in f (Q) gravity theory. The field equations have been solved using the MIT-Bag Equation of State (EoS) along with the Vaidya–Tikekar potential. However, the specific form of charge E 2 (r) = q 0 L 2 r 2 (1 + L r 2) 2 has been used to study the charged compact star model. The interior charge anisotropic solution has been matched with the charged Schwarzchild-(Anti)-de-Sitter metric. In particular, this study has been conducted on different compact stars viz, EXO 1785-248, PSR J1614-2230, PSR J0740+620 and SMC X-1. The mass–radius ratio of these compact stars has been predicted, and the physical viability of the model has been checked through the behaviour of causality condition, matter variables, energy constraints, modified TOV equation, and adiabatic index of the star. In conclusion, we observe a well-behaved model of a compact star in f (Q) gravity with charged matter distribution. • We have investigated charged anisotropic solutions for strange star model. • The Vaidya–Tikekar potential has been used for strange star model. • The system of equations have been solved using MIT-Bag EoS. • The obtained charged fluid solution is viable for modelling of compact objects. [ABSTRACT FROM AUTHOR]

The recent surge in electric vehicle (EV) adoption has presented various challenges, notably in the charging and discharging processes of EV batteries, each characterized by unique traits. While conventional charging stations remain popular, battery swap stations (BSS) offer a compelling alternative, addressing issues like prolonged waiting times and potential battery degradation from fast charging. BSS, with its extensive array of battery systems, ensures efficient services for EVs. However, meticulous planning for the charging and discharging operations is imperative for both BSS and the overall grid to guarantee optimal functionality. This paper proposes an efficient approach to enhance the efficiency of battery swapping and charging mechanisms (BSCM) for electric vehicles, leveraging the bat algorithm. The BSCM is conceived as a system that incorporates both the battery swapping mechanism (BSM) and the battery charging mechanism (BCM). The key contribution lies in designing an effective BSCM where the BSM functions as a manager, handling battery swapping requests from EV users, while the BCM acts as a supporter, interfacing with the grid to regulate battery charging and discharging power. To efficiently address the mixed-integer nonlinear program (MINLP) inherent in this system, a Bat algorithm is developed. The results clearly demonstrate the effectiveness of the proposed algorithm in efficiently addressing large-scale problems, producing solutions that closely approach optimality. It promptly achieves a substantial reduction in battery swapping energy by 30% and 24%, respectively, and significantly enhances charging station utilization by 25% and 21% compared to the LSTM-Based Rolling Horizon Approach and Bilevel Optimization Approach. Additionally, the algorithm showcases remarkable improvements in battery swapping performance, boasting a 25% and 19% enhancement, and noteworthy increases in charging station utilization by 20% and 17% compared to the aforementioned approaches. This enhancement in the energy exchange with grid and regulation contributes to the overall efficiency and sustainability of electric vehicle operations. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]

The randomness and uncertainty of charging behavior of EV users bring great challenges to the short-term prediction of charging load of EV charging stations. Aiming at the low precision of short-term load forecasting of EV charging station under the influence of many factors, this paper takes the optimization of EV charging loss as the objective of optimization, firstly, the time series attributes are combined with the nonlinear attributes, and PLSR and LightGBM are used to extract the attributes from EV charging load series, and the fusion prediction model is established. Secondly, the Bayesian optimization method is introduced to optimize hyper-parameters of the model, and the self-adaptive adjustment of the hyper-parameters are realized, which improves performance of the algorithm and the prediction precision. Finally, taking the actual load data of EV charging pile in a certain area as an example, the experimental analysis is carried out, the second-order cone optimal dispatching scheme is adopted to optimize the EV charging schedule. The results show that the EV prediction model constructed by this paper has higher prediction accuracy than other artificial intelligence prediction algorithms, and provides more accurate data for the second-order cone optimal scheduling scheme, the utility model effectively realizes the safe and stable operation of the power grid. • The accuracy of EV load forecasting is improved by using fusion algorithm. • The generalization ability of prediction model is improved by Bayesian optimization algorithm. • It provides an accurate data base for EV optimal scheduling. [ABSTRACT FROM AUTHOR]

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output. • Modeling of 5G base station backup energy storage. Aiming at the shortcomings of existing studies that ignore the time-varying characteristics of base station's energy storage backup, based on the traditional base station energy storage capacity model in the paper [ 18 ], this paper establishes a distribution network vulnerability index to quantify the power supply reliability of the distribution network nodes by adopting the voltage Theil's entropy and the modified Gini coefficient. Based on the power supply reliability of power grid nodes and combined with load level weights, a model for the backup energy storage time of base stations affected by power supply reliability is established. • The model of 5G base station energy storage participating in power supply recovery In view of the impact of changes in communication volume on the emergency power supply output of base station energy storage in distribution network fault areas, this paper introduces a two-stage robust optimization model to deal with the uncertainty of communication volume. Based on the base station energy storage capacity model established in contribution (1), an objective function is established to minimize the system operating cost in the fault area, and the base station energy storage owned by mobile operators is used as an emergency power source to participate in power supply restoration. Establish sub-objective functions of the loss cost of base station energy storage charging and discharging, the subsidy cost of base station energy storage charging and discharging, and the base station energy storage operating cost to supplement the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

• An equivalent circuit model that considers the non-uniformity of the stress distribution is developed. • Non-uniform radial current distribution during charging of NI RP coils due to non-uniform stress distribution. • The non-uniformity of the radial current distribution can be significantly improved by applying pressure to the straight part of the RP coil. Racetrack superconducting coils are widely used in magnetic levitation (maglev), generators, and accelerators, showing very important application prospects in many fields. However, due to the special geometry, the straight part and the arc part of the coil are given different radial stresses during the winding, cooling, and excitation, which will cause a difference in their transient performance during charging/discharging. In this paper, we investigated the transient electromagnetic behavior of the no-insulation (NI) REBCO racetrack coil during charging/discharging. First, an equivalent circuit model of a NI racetrack pancake (RP) coil was established. The stress distribution calculated by the analytical and finite element methods was used to consider the difference in contact resistance, the model can predict the current distribution of the straight and arc parts of different turns of the coil during charging/discharging. Afterward, A small-scale NI RP coil was successfully wound to validate the model, and the calculation results show good consistency with the experimental results. Finally, we investigated the transient behavior of the NI RP coil under different stress distributions. A targeted charging protection strategy for the coil was proposed and its effectiveness was verified experimentally. The work in this paper can help us to better understand the transient behavior of different parts of the NI RP coil during charging/discharging and provide guidance for its future construction. [ABSTRACT FROM AUTHOR]

The promotion of electric vehicles can help reduce carbon emissions and mitigate the greenhouse effect. Lithium-ion batteries are one of the main power sources for electric vehicles, whose state of charge is a key parameter of the battery management system to ensure the safe operation of electric vehicles. The complex structure of the battery makes the state of charge unable to be directly measured, so this paper establishes a mathematical model to estimate the state of charge based on four parameters (voltage, current, wavelength and light intensity), aiming to provide a high-precision model for state of charge measurement and establish a mathematical model which can help guide actual production. These four parameters can be measured by voltmeters, ammeters, and optical fibers. In this paper, the least squares method is used to perform global linear regression and global quadratic regression of experimental data, and a data pre-treatment is proposed to improve the behavior of the linear equation system. Also, the ridge regression method is used to achieve an effective solution of the ill-conditioned linear equation system. Then, the piecewise linear regression is introduced to optimize the global regression model, and effectively reduces the regression error by capturing the local features of the data. In order to meet the actual engineering requirements, constraints satisfying segmental continuity are added in the piecewise linear regression model. Finally, the regression results are compared with the results obtained by the Machine Learning method. Besides, the proposed methods outperform the Machine Learning method with a sufficient number of segments. The state of charge estimation model established in this paper has high approximation accuracy, strong interpretability, low operational complexity, and has certain engineering application value. • An efficient model is built to estimate the state of charge of lithium-ion battery. • The global linear and quadratic regressions are performed. • A data pre-treatment is proposed to improve the linear equation system. • The ridge regression method is used for the ill-conditioned linear equation system. • The piecewise linear regression is used to optimize the global regression model. • To meet the actual requirements, constraints of segmental continuity are added. • The proposed methods outperform the Machine Learning method. [ABSTRACT FROM AUTHOR]

• The relationship linking the power grid, aggregator, and EVs is defined. • The pricing of the aggregator and the allocation of EV charging stations are divided into two stages. • A two-layer deep learning model is built to solve the final pricing and EV charging strategy model, and the concept of humanity is introduced. In response to the increasing number of EVs(electric vehicles) being charged in a disorderly manner, this paper proposes a two-layer deep learning model based on aggregator pricing and EV user charging strategy. Firstly, the relationship linking the power grid, aggregator, and EVs is defined. Secondly, the pricing of the aggregator and the allocation of EV charging stations are divided into two stages. In the first stage, the aggregator sets the tariffs and sends them to the users, and the users receive the charging strategy. In the second stage, the aggregator adjusts the tariff on the basis of the charging strategy provided by the EVs and the actual situation of different charging stations and then releases it. Next, a two-layer deep learning model is built to solve the final pricing and EV charging strategy model, and the concept of humanity is introduced. The upper layer is the charging decision layer, which is solved by deep reinforcement learning. The lower layer is the charging station selection layer, which is solved by deep Q-learning. The method proposed in this paper can effectively improve the profits of the aggregator and reduce the charging cost of EVs. [ABSTRACT FROM AUTHOR]

It is widely recognized that electric vehicles (EVs) will play a crucial role in the electrification of transport, which is necessary for reaching a net-zero emissions economy. This recognition is reflected in the number of initiatives introduced worldwide to promote the EV industry, ranging from purchase subsidies to the provision of charging infrastructure and direct industry assistance. In this context, the Chinese government introduced a comprehensive program of government subsidies to support the sale of EVs. This paper estimates the impacts of these subsidies on EV sales in China using the difference-in-differences (DID) and propensity score matching (PSM) approach. Based on the panel data at city level from 2009 to 2018, we show that subsidies were the major contributor to the increase in EV sales. Our results suggest that the provision of infrastructure such as charging piles is also an important contributing factor. These findings are robust across model specifications and regression approaches. The heterogeneity analysis indicates that the treatment effect is heterogeneous across EV types, city sizes and regions. Our results provide empirical support for the current policy settings designed to promote EV sales in China. [ABSTRACT FROM AUTHOR]

• Internal micro-channel cold plates (IMCPs) were employed to electric vehicle film capacitor. • IMCPs were optimized using response surface methodology and genetic algorithms. • Capacitor with IMCPs exhibited temperature reductions of 45.50 °C and 48.45 °C. • Capacitor with IMCPs maintained normal functionality under ultra-high temperatures. The increasing voltage and power density demands of electric vehicles pose notable challenges to the high-temperature resistance performance of film capacitors within motor controllers. Existing methods primarily focus on material modifications and conventional thermal management designs, often tested at 85 °C, which fail to meet the escalating demands for high-temperature operations. This paper presents the design and optimization of a thermal management system for film capacitors through simulations and experiments under ultra-high temperatures. ANSYS electro-thermal coupled simulation was used to analyze the temperature distribution of the capacitor under different ripple currents, leading to the development of a thermal management system based on internal micro-channel cold plates (IMCPs). These IMCPs were optimized using response surface and genetic algorithms to enhance cooling performance. Experimental and simulation comparisons were made between standard capacitors and those with IMCPs under ripple currents of 120 A and 180 A at 85 °C. Further examinations were conducted at ultra-high temperatures of 125 °C and 140 °C with a ripple current of 180 A. The findings show that capacitors with IMCPs had average temperature reductions of 45.50 °C and 48.45 °C at 85 °C under 120 A and 180 A ripple currents, respectively. Additionally, standard capacitors developed cracks or exploded at 125 °C and 140 °C under 180 A ripple current, while capacitors with IMCPs maintained normal functionality. These results indicate that capacitors integrated with IMCPs meet the rigorous high-temperature operational standards required for motor controllers in high-voltage electric vehicles. [ABSTRACT FROM AUTHOR]