Search

An efficient thermal regulation strategy is of great significance in ensuring the safe operation of electric vehicles (EVs). However, the commonly used thermal management systems suffer from the problem of not being able to accurately and uniformly control the temperature distribution of lithium-ion batteries (LIBs), which poses great risks to the thermal safety control of batteries. In this paper, an advanced thermal management system for LIBs based on thermoelectric cooler (TEC) was designed to overcome the above problems. First, a temperature regulation mechanism model for LIB was constructed. Then, a novel temperature distribution modeling method for TEC was developed by using spectral method. This modeling process took into account the unsteady heat transfer characteristics, which can achieve high modeling accuracy. Next, a state space model of temperature control was constructed by combining a differential model of LIB with the proposed cooler model. On this basis, a temperature control strategy for LIB using nonlinear model predictive control (NMPC) method was proposed to optimize the cooling process because of its superior processing ability to constraints and nonlinearity. A various of experiments and verifications demonstrated that the presented thermal regulation strategy was effective and feasible. • An advanced thermal regulation system for lithium-ion battery based on thermoelectric cooler is designed. • A novel temperature distribution modeling method for TEC is proposed. • A regulation strategy is developed to control the uniformity of temperature distribution. • The paper provides a good guidance for the application and further study of advanced thermal management system in electric vehicle. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy. • Four FRA techniques, Impedance, MLECM, DRT and NFRA-based method are developed. • Two frameworks, MLF and CFF, are applied to each of these four techniques. • The estimation accuracy and estimation range of the eight models are compared. • Application scenarios are identified by using three classification methods. [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]

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs. • "Opinions" measures encourage NEV to innovate independently, jointly and efficiently. • "Supply-side" tax incentives have a stronger innovation effect. • "Demand side" government procurement has a stronger signal effect. • Government policy and venture capital play an innovative synergistic effect. • There is an innovation failure in policy mix before venture capital intervention. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency. • Euclidean distance evaluation is used for cell voltage inconsistency fault diagnosis. • Forgetting factor is used to mitigate fault diagnosis delay and memory effect defects of time window. • Optimized correlation coefficient is used to isolate pattern of cell voltage inconsistent fluctuations. • The accuracy is more than 99%. [ABSTRACT FROM AUTHOR]

Under the guidance of the "Dual Carbon" goal, integrated energy supply stations have gradually become an essential facility for the energy transition. Promoting user repurchase has become a vital marketing strategy for integrated energy supply station enterprises. This paper proposes a prediction method based on the user profiling method to predict user repurchase behavior accurately. First, using an improved RFM model and the K-means algorithm, this paper constructs user profiles by dividing 10,000 users into three clusters: general-value developmental users, high-value new users, and low-value loyal users. Next, this paper uses the random forest, light gradient boosting machine, and extreme gradient boosting to predict the repurchase behavior of non-clustered users and the three clusters and compares their prediction performance. In addition, this paper adopts the stacking method for model fusion to improve the prediction performance further. The results show that the accuracies of the best prediction models for the three clusters are 93.28 %, 93.68 %, and 92.84 %, respectively. Finally, this paper provides each cluster with the corresponding prediction model of user repurchase behavior and marketing strategy. For the application scenario of integrated energy supply stations, this study accurately predicts the repurchase behavior of each cluster with unique consumption characteristics. It helps to provide personalized services for new energy vehicle consumers, optimize their consumption experience, and facilitate sustainable consumption. • A method for predicting user repurchase behavior for integrated energy supply stations based on the user profiling method. • This paper clusters users based on the unique consumption characteristics and constructs user profiles for each cluster and individual. • Three mainstream ensemble learning algorithms to predict the repurchase behavior of each cluster. • Stacking algorithm for model fusion to improve the prediction performance further. [ABSTRACT FROM AUTHOR]

Due to the widespread application of lithium-ion batteries in new energy vehicles and energy storage fields, fractional-order theory and coupling modeling methods have developed rapidly in battery modeling. This paper proposes an asynchronous fractional-order hot zone coupling modeling method based on frequency domain separation to adapt to battery characteristics. Specifically, in the electrochemical impedance spectroscopy test, the behavior of the battery exhibits different frequency characteristics. Furthermore, a model parameter identification strategy based on frequency domain separation is constructed, and asynchronous parameter updates are achieved through mutually coupled sub-algorithms with different time scales. Additionally, the robustness of the asynchronous fractional-order thermal-electric coupling model (AFO-TCM) was validated under low ambient temperatures, along with its superiority in state of charge (SOC) estimation. Compared to models that do not consider frequency characteristics, the proposed model achieved a reduction in the root mean square error (RMSE) of SOC estimation results by 57 %, 52 %, and 45 % at 0 °C, −10 °C, and −20 °C environments, respectively, under the US06 Supplemental Federal Test Procedure Cycle (US06) driving cycle. This method provides a novel approach to addressing the interdependence between the order of fractional models and temperature. • An asynchronous fractional-order thermoelectric coupling model is established. • An online asynchronous parameter identification strategy was established based on frequency characteristic separation. • The performance of the model was evaluated through multiple sets of low-temperature experiments. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been popularly adopted and deployed over the past few years. However, the mismatch between EVs and charging infrastructure has become one of the major roadblocks to restricting EV promotion. Target at improve the temporal and spatial utilization rate of charging infrastructure, this paper presents a new "1 to N" automatic charging system with the combination of charging pile and special robotic arm. The connection between the charging pile and arrived EVs can be automatically switched by the robotic arm and the charging demand of EVs parking at the random parking spots could be satisfied. Based on the "1 to N" charging scenario a two-layer iterative charging scheduling strategy is proposed benefits of restraining the impact of the charging demand uncertainty while realizing the fairness in charging behavior. In addition, both the simulations and hardware in the loop experiments are implemented to verify the feasibility and real-time performance of the proposed "1 to N" charging system and charging schedule strategy. • A "1 to N" automatic charging pile is designed and tested for the first time. • The temporal and spatial utilization rate of charging devices is improved. • The fairness in charging behavior is realized by a layered charging strategy. • The impact of the charging demand uncertainty is investigated and restrained. [ABSTRACT FROM AUTHOR]

The mutual integration of different energy sectors and their conversion to electricity could promote the integration of renewable energy sources. This paper analyses 100 % renewable electrical power system of the Dubrovnik wider urban area. The Calliope energy system model was developed and compared based on 10-min and hourly data. The energy scenario is modelled up to the year 2050. It includes the replacement of all conventional passenger vehicles with electric vehicles. The vehicle-to-grid model was developed using the Calliope modelling tool. All fossil fuels used in the heating sector are replaced by seawater heat pump systems. Additional storage is added to the system and the system is connected to the adjacent electricity market. The 10-min model led to a reduction in cross-border transmission capacity of around 60 % and a 7-fold reduction in the difference between import and export compared to the hourly model. This led to a more stable system operation, as the import and export capacities were equalised. The vehicle-to-grid technology in the regulated system led to a reduction in import and export transmission capacities of up to 17 % and 42 % respectively compared to the unregulated system, including extremely fast charging and discharging in a short-term step model. • Energy scenario of a 100 % renewable energy system is modelled until 2050. • V2G model is developed in the Calliope tool using fast charging and discharging. • Short-term step model based on a 10 min time step is compared with an hourly model. • Cross-border transmission capacity is reduced by 60 % in a short-term step model. • V2G regulation resulted in a reduction of the import and export up to 17 % and 42 %. [ABSTRACT FROM AUTHOR]

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles. • The study proposes a novel two-stage adaptive co-evolutionary clustering algorithm. • The proposed method shows superior advantages in energy conservation and emission reduction. • The impact of the vehicle path decision on the charging station location plans is considered. • Analyzing the impact of network scale, traffic volume, and grid capacity on the model. [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]

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs. • The precision and efficiency of PSO are improved. • The effective interaction between EV and power grid is solved. • It realizes the mutual supplement of energy and the safe operation of power grid. [ABSTRACT FROM AUTHOR]

With the development of intelligentization and network connectivity of new energy vehicles, the estimation of power lithium-ion battery state of charge (SOC) using artificial intelligence methods is becoming a research hotspot. This paper proposes an optimized multi-segment long short-term memory (MSLSTM) network strategy for SOC estimation of powered lithium-ion batteries' adaptive wide temperatures. First, the multi-timescale electrochemical processes during the charging and discharging of power lithium-ion batteries are efficiently analyzed, and the analytically measurable external parameters are classified into subsets based on the analysis. Secondly, the idea of segment long short-term memory (SLSTM) estimation is proposed to enhance the data linkage between the SOC and the nonlinearly varying parameters and to improve the prediction accuracy. Finally, an optimized MSLSTM neural network is proposed for nonlinear regression prediction of SOC in subset intervals through a combination of segmented estimation idea and SLSTM neural network. The proposed algorithm is validated under a variety of temperatures and operating conditions, and the accuracy of the SOC estimation is improved by at least 66.770 % or more. It provides a solution idea for intelligent estimation of power lithium-ion battery SOC. • The subset estimation idea is established using battery intrinsic properties. • Subneural network for SOC estimation is proposed, using SLSTM. • An MSLSTM SOC estimation strategy is proposed for adaptive wide temperature. • The proposed MSLSTM algorithm realizes full SOC estimation with high accuracy. [ABSTRACT FROM AUTHOR]

In order to tackle the increasingly severe energy crisis, improving the efficiency of the powertrain system in battery electric vehicles has become an effective approach. Based on a 6 × 4 battery electric tractor truck (BETT), the paper focuses on enhancing the dynamic and economic performance of vehicles through advancements in configuration, control strategies, and parameter design. Firstly, a novel multi-motor torque coupling powertrain system based on the parallel-axle transmission is proposed and modeled. Then, an instantaneous minimum energy consumption control strategy for the economic performance simulation and an instantaneous maximum acceleration control strategy for the dynamic performance simulation are designed. Furthermore, a conventional heuristic single-objective optimization algorithm, artificial rabbit optimization (ARO), is improved into a multi-objectives version, non-dominated sorting artificial rabbit multi-objective optimization (NSARMO), by combining the fast non-dominated sorting (FNS) and modifying the calculation method of the energy factor. Finally, an optimization framework consisting of a control layer, model layer, and optimization layer is established and applied to the parameter optimization of the proposed powertrain system. Simulation results demonstrate that the optimization results of the proposed method are superior to those optimized by commonly used non-dominated sorting genetic algorithm-II (NSGA-II) and NSARMO without the modified energy factor, achieving a better balance between dynamic performance and economic performance. Moreover, the proposed method exhibits demonstrates 40.67% and 24.67% improvement in its ability to search for optimal Pareto solutions compared to NSARMO (-)(- means the calculation method of energy factor is not modified) and NSGA-II. At last, a hardware-in-the-loop (HIL) experiment bench is established, and the experiment results indicate that a scheme optimized by the proposed NSARMO exhibits only an approximate performance in energy consumption in the HIL environment compared to the simulation environment, which signifies the effectiveness of control strategy equipped with the optimization results optimized by the proposed method. • A novel multi-objective optimization algorithm is developed. • An optimization framework considering control, model and optimization is proposed. • The parameter optimization problem of powertrain system for 6 × 4 battery electric tractor trucks is solved. [ABSTRACT FROM AUTHOR]

Dual-credit policy promotes new-energy vehicle (NEV) automakers to invest in technology R & D by granting NEV with better technological performance to higher NEV-credit. Based on the complex network evolutionary game theory, this paper constructs a technology R & D diffusion model of automakers considering the technology spillover effects, and explores the dynamic impact of adjusting different dual-credit policy parameters on technology R & D diffusion among automakers. Our results show that: (1) increasing the annual increment of NEV-credit requirement ratio can encourage more NEV automakers to invest in technology R & D, but also lead to significant increase in NEV-credit price. (2) Reducing the CAFE target coefficient can simultaneously promote the diffusion of NEV technology R & D and GV green-technology R & D, but excessive reduction can hinder GV automakers from entering NEV market. (3) Lowering the upper limit of credit per unit NEV suppresses the diffusion of NEV technology R & D. (4) Reducing technology-spillover is conducive to the incentive of dual-credit policy on the diffusion of technology R & D. • An evolutionary game with technology-spillover model in complex network was constructed. • Impact of dual-credit policy on diffusion of technology R & D among automakers was discussed. • Dynamic changes of NEV-credit market, were discussed. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) combined with bidirectional home chargers (vehicle-to-home (V2H) units) and electricity interchange between residences are promising options for the self-consumption of rooftop photovoltaics (PVs) in residences. Furthermore, considering that EVs have large storage capacities, one EV + V2H might support neighborhood-scale electricity storage by electricity interchange between residences, resulting in more cost-efficient self-consumption systems. Therefore, this paper investigates how permitting electricity interchange between residences affects the economically optimal design of distributed energy systems, such as PVs, EVs and V2H units. For this purpose, a mixed-integer linear programming model is developed to optimize the configurations and operation strategies of distributed energy resources (DERs) considering electricity interchange in neighborhoods. V2H units, which supply electricity from EVs to residences, are considered DER options independent of EVs. Furthermore, the optimization results are compared under two scenarios wherein electricity interchange is and is not permitted between two residences. The results indicate that permitting electricity interchange improves the self-consumption rate of PV power while reducing investment in storage facilities. The one influencing factor is sharing one EV + V2H between two residences and effectively utilizing the available storage capacity. • Comprehensive optimization of energy system configurations in residences. • Installation of bidirectional chargers for electric vehicles is also optimized. • Impact of permitting electricity interchange between residences is investigated. • Sharing electric vehicle batteries is valuable for photovoltaic self-consumption. • Electric vehicles can replace the role of the neighborhood's electricity storage. [ABSTRACT FROM AUTHOR]

This study presents a novel Vehicle-to-Grid (V2G) integration strategy. By utilizing the energy stored in electric vehicles (EVs) to inject power into the grid optimally during peak-load/high-tariff periods. This V2G strategy has two main objectives: enhancement of the system performance and offering financial profit to EV owners. The V2G strategy optimizes the energy exchange between EVs and the grid based on load leveling methodology through intelligent scheduling of charging/discharging times to alleviate grid stress during peak demand, leading to enhanced system performance. Also, it enables EV owners to sell their excess energy to the utility at favorable prices, which not only helps EV owners offset their charging costs but also turns their EVs into profitable assets. The contribution over recent similar studies is targeting multiple objectives simultaneously, besides employing a comprehensive set of factors, involving all relevant participants, and rigorous load modelling. The effectiveness of this strategy is demonstrated through comprehensive simulations with two different charging scenarios applied to two networks, RBTS-bus2 and ShC-D8. The results show the enhancement of system performance, as evidenced by improvements in voltage profiles and load curves, besides reductions in losses, operational costs, and feeder and transformer loading. Furthermore, the proposed approach maximizes the profit of EV owners. • This paper presents a novel V2G strategy for distribution systems. • The proposed methodology can enhance electric vehicles profitability. • Real measurement data are incorporated in the model. • Different types of nonlinearity and uncertainty are added to the model. • The results of proposed model are compared with other recent models. [ABSTRACT FROM AUTHOR]

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs' power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level. • Proposing a new hybrid architecture for predicting the large-scale EVs' power demand. • Constructing a new transfer learning strategy to improve the prediction accuracy. • The proposed hybrid model significantly improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe operation of electric vehicles. This paper proposes an innovative battery voltage abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles. Vehicle and laboratory data are collected and analyzed, with joint preprocessing to improve data quality, and battery voltages are log-transformed to improve the contribution of anomalous voltage fluctuations. The normalized coefficient of variation is proposed to detect the fluctuation inconsistency of cell voltage, and the risk coefficient rule is formulated by Z-score and normalization. Furthermore, the validity and robustness are verified by laboratory and real-world battery faults. The results demonstrate that the optimal slide step and calculation window for real-world under-voltage fault are 10 and 40, and those for laboratory lithium plating and real-world thermal runaway are both 10 and 50, respectively. More importantly, this study introduces a battery abnormality diagnosis strategy based on the vehicle T-box, anticipated to be widely implemented to ensure the safety of real-vehicle operations. This method not only enhances the accuracy and efficiency of detecting electric vehicle battery abnormalities, but also offers a practical solution to prevent battery related faults. • The abnormal diagnosis of battery voltage is crucial to electric vehicle safety. • A innovative diagnosis method based on the multi-scale NCOV is proposed. • The validity and robustness are verified by experimental and real-vehicle data. • A actual battery thermal runaway process is discussed and analyzed in detail. • A real-time diagnosis scheme based on vehicle-end edge computing is presented. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles has become pivotal in attaining emissions reduction objectives, and the rationale behind designing and situating charging stations determines the unfurling of electric vehicles. This scholarly work fully mines the geographical situational particulars underlying the functioning statistics of charging stations in Beijing during the period from January to March 2016. By incorporating the urban road network, we construct a charging stations accessibility index to investigate the influence of stations accessibility on utilization rates. The findings reveal a substantial positive relationship between the overall accessibility of charging stations and their utilization rates. However, the limited number of charging stations hinders the agglomeration effect from significantly boosting utilization rates. Moreover, the impact of charging station accessibility on utilization rates varies significantly across regions with different levels of development, indicating pronounced heterogeneity. The research conclusions of this paper provide useful suggestions for promoting the rational layout of charging stations. [Display omitted] • Charging station accessibility depends on driving distance to important city areas. • The accessibility of charging stations significantly impacts their utilization rate. • Charging station accessibility's impact on utilization rate varies significantly across regions. • Insufficient charging stations limit agglomeration's impact on utilization rates. [ABSTRACT FROM AUTHOR]

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively. • A multi-objective optimal allocation model is proposed. • Multiple demand response strategies and their uncertainties are considered. • Hybrid time series and Kalman Filter is used for photovoltaic output prediction. • Orderly charging influences more than the incentive scheduling strategy. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEV) have become an important driving force for carbon reduction in the transportation industry. This paper adopts the extended environmental pressure assessment (STIRPAT) model and the time-varying difference in differences (DID) model to evaluate the effectiveness of the NEV subsidy policy to achieve sustainable governance. The results show that the NEV subsidy policy can significantly decrease the carbon emission of the transport industry. The mechanism analysis results indicate that the NEV subsidy policy can decrease carbon emissions by promoting a green transformation of the consumer car purchasing structure. Further analysis found that a "fraudulent subsidy" can significantly weaken the policy effect of subsidies. The results of heterogeneity analysis indicate that the subsidy policies introduced by different levels of government have different effects. The subsidy policy of the central government has a more significant effect, and repeated subsidies from central and local governments will weaken the policy's effectiveness. These findings provide valuable insights for policymakers in developing countries to optimize subsidies for new energy vehicles in reducing carbon emissions in the transportation industry. • We explore the impact of the NEV subsidy policy on carbon emissions of the urban transport industry in Chinese cities. • The NEV subsidy policy significantly reduces the carbon emissions of the urban transport industry. • The NEV subsidy policy can promote a green transformation of the consumer car purchasing structure. • The fraud of government subsidies will weaken the carbon reduction effect of the NEV subsidy policy. • The impact of NEV subsidy policy from central and local governments on urban carbon reduction may vary. [ABSTRACT FROM AUTHOR]

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure. • An innovative Internet of Vehicles (IoV) framework for depicting electricity demand on the demand side in V2G scenarios. • In-depth experimentation and evaluation of the state-of-the-art approaches of clustering and forecasting. • The devised model successfully projected a 10% decrease in demand side for consumers. [ABSTRACT FROM AUTHOR]

CO 2 is assumed to be one of the most potential refrigerant alternatives for electric vehicles for its excellent properties. However, the charge determination of CO 2 in current studies remain controversial. In this study, a transcritical CO 2 air-conditioning heat pump system was established and experimentally tested to analyze the optimal charge amount. Based on two conflicting methods of charge determining proposed by the preceding research, this paper substantiated the existing controversy and subsequently proposed a more comprehensive method. The effects of different refrigerant charge on the system characteristics were investigated. The influence of the compressor speed on the optimal refrigerant charge and system characteristics was also analyzed. It was found that the optimal charge plateau occurred from refrigerant of 500 g–580 g at the compressor speed of 3000 r·min−1. However, the optimal charge declined with the increment of compressor speed from 3000 r·min−1 to 4500 r·min−1. Among three models, Hughmark's model was proved to be the most appropriate for the theoretical calculation of optimal charge within an error of 6.09%. Further study illustrates that refrigerant mass in high-pressure pipe and intermediate heat exchanger/accumulator (IHX/A) accounted for the main proportion about 52.6%–55.14%. • A novel principle to determine the optimal charge plateau is verified. • The optimal charge of the CO 2 ACHP system is tested under different compressor speeds. • Hughmark's model is the most accurate within the error of 6.09%. • High-pressure pipe accounts for the largest proportion of refrigerant mass. [ABSTRACT FROM AUTHOR]

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons. • Standalone multi-carrier energy system including electricity-hydrogen-gas is developed. • Hybrid renewable generation comprising solar and hydroelectricity is integrated. • Power to hydrogen is manipulated for connection between electricity and hydrogen. • Hourly, daily, and seasonal hydrogen storage systems are modeled. • Electric vehicle charging station and fuel cell vehicle refueling station are integrated. [ABSTRACT FROM AUTHOR]

in recent years, large-scale electric vehicles connected to the power grid has a significant impact on demand-side resources. In addition, the vehicle-to-grid technology of electric vehicles introduces more uncertainties, which brings great challenges to the optimal scheduling of integrated energy systems. To solve this problem, this paper fully considers the uncertainty and fluctuation of wind power. Firstly, the objective function of joint configuration is constructed to maximize the utilization of wind power and minimize the planned generation cost of the system. Secondly, the linearized power flow equations are used to represent the relations among the state variables, and the second-order cone relaxation method is applied to deal with the branch power flow constraints, which is transformed into a solvable mixed-integer second-order cone programming model. Finally, the validity of the model is verified by an example. The system achieves obvious peak-shaving and valley-filling effect and improves the economic benefit of the system. • The load characteristics and energy storage characteristics of the electric vehicle fully absorb the wind energy. • The proposed second-order cone optimal dispatching strategy realizes the supplementary supply of grid and renewable energy. • The optimal dispatching strategy provides theoretical support for the efficient operation of grid. [ABSTRACT FROM AUTHOR]

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods. • An eco-driving strategy at continuous speed-limit signalized intersections. • The varying speed limits, traffic signal period, and queue effect are considered. • The powertrain efficiency and constraints are incorporated into motion planning. • A long-term co-optimization model of vehicle motion and powertrain operation. • A short-term optimal velocity tracking control for smooth powertrain operation. [ABSTRACT FROM AUTHOR]

This paper develops a robust and sustainable energy management framework to model a multi-vectored energy hub (MVEH) system. A max-min-based static robust optimization model is developed to counter the uncertainties. Furthermore, a flexible energy demand management program, battery electric vehicles (BEVs), and compressed air battery storage (CABS) are integrated to enhance MVEH's flexibility. Hybrid power-to-X units (electrolyser, methanization reactor and fuel cells) are modelled to reduce green energy spillage and improve operational independence, including heat pumps. Moreover, the emission (carbon cap and trade) market along with carbon capture storage and utilization units (CCS–U), are incorporated to establish a low-emission multi-energy model. Simulation results demonstrate that with flexible resources and power-to-X units, MVEH reduces the operating energy cost by 46.4 % and improves the system independence by 19.06 %. Besides, CCS-U units produce 92 % less emission while increasing the energy cost by 5.1 %. However, MVEH gains the right to sell carbon credits under the emission market. Thus, the multi-vectored system's economical, reliable, and environmental viability are ensured. Finally, sensitivity analysis proves the robustness of the proposed framework against natural gas and carbon price fluctuations. • Robust energy management for multi-vectored energy hubs is proposed. • A max-min uncertainty budget based robust stochastic algorithm is applied. • To reduce green energy spillage, power-to-gas and electric vehicles are modelled. • To model low emission hub, carbon capture units are integrated. • Further, emission markets are developed to make hubs economically viable. [ABSTRACT FROM AUTHOR]

Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions. • Extract strongly correlated health features from the real-world fast charging process. • Construct a feature pool that accurately represents EVs' practical characteristics. • Automatic selection of relevant features and elimination of interference components. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of charge (SOC), state of energy (SOE), and state of health (SOH) online is a critical and urgent concern in the management of lithium-ion batteries for electric vehicle applications, particularly in terms of safety and reliability. This paper develops a hybrid neural network, abbreviated as CNN-SAM-LSTM model, which combines a convolutional neural network, self-attention mechanism, and long-short term memory neural network to jointly estimate the state parameters of lithium-ion batteries, including SOC, SOE, and SOH. Additionally, a joint loss function considering homoscedastic uncertainty is developed to optimize weight adjustments for the training losses associated with the three state parameters. Experimental data collected under UDDS, BBDST and CC discharge conditions are employed to showcase the effectiveness of the proposed CNN-SAM-LSTM model. The results demonstrate that the proposed model is capable of simultaneously and accurately estimating SOC, SOE, and SOH for lithium-ion batteries under different dynamical operating conditions. Moreover, when applied to randomly segmented data, the proposed model exhibits robustness, effectively handling deviations from random discharge segments. • A CNN-SAM-LSTM hybrid neural network is developed to estimate SOC, SOE and SOH. • The loss function of the hybrid network considers homoscedastic uncertainty. • The proposed method achieves high accuracy under dynamic operating conditions. • The proposed method is strongly robust against random discharge segments. [ABSTRACT FROM AUTHOR]

Accurately predicting the health status of batteries through easily available data is crucial for the battery management system (BMS) in electric vehicles. This paper utilizes the battery aging information obtained from the partial charging process as the input to accurately predict the battery charging process based on the bi-directional long short-term memory (Bi-LSTM) network. Firstly, in order to obtain a precise incremental capacity (IC) curve containing battery aging information, a generalized derivative method based on the back propagation (BP) neural network for discrete data is proposed. Then, using this derivative method, the interrelated three-dimensional features, namely charged capacity (Q), voltage (V), and the d Q /d V values, are identified from the IC curve containing a complete peak with an interval length of 100 mV. Subsequently, a Bi-LSTM network is constructed to learn the correlation between the three-dimensional features and charging capacity, and predict the charging process within the interval of 3.8V–4.2V. Finally, the Oxford battery degradation dataset is used for verification. The results show that the battery aging information extracted during the partial charging process is closely related to battery capacity degradation, and the proposed capacity prediction method with fusing aging information can accurately predict the charging process of lithium-ion batteries. • A generalized derivative method based on BP neural network for discrete data. • Capacity prediction method of lithium-ion batteries with fusing aging information. • A Bi-LSTM network with the interrelated 3D features for capacity prediction. [ABSTRACT FROM AUTHOR]

Under the context of carbon peak and carbon neutrality, new energy vehicle (NEV) is considered as ultimate objective in development of automotive industry. To facilitate sustainable growth of automotive market and ensure a smooth transition for fuel vehicle (FV) enterprises, this paper constructs a tripartite evolutionary game model based on tripartite Hoteling model, which involving NEV enterprises, FV enterprises and governments. The herd effect and time-delay effect are considered to improve the tripartite evolutionary game. Results indicate that: (1) Fund feedback to NEV enterprises will counterintuitively decrease probability of active assistance from NEV enterprises. (2) Governments' willingness for strict involvement positively correlates with supervision costs but negatively correlates with additional benefits. (3) Time delay in funds leads to weak effect of high default penalties in promoting active assistance of NEV enterprises in early stage, but opposite situation appears in later stage. (4) Higher NEV production proportion counterintuitively stimulates FV enterprises to give up transition. (5) High rewards drive diffusion of herd effect, further increasing competition among vehicle enterprises leading to FV enterprises giving up transition. (6) Herd effect and time-delay effect increase dependence of FV enterprises on funds results in higher sensitivity of credit trading price compared to credit ratio requirement. [Display omitted] • Consider transition of FV enterprises to a tripartite evolutionary game model. • A tripartite evolutionary game model is integrated with tripartite Hoteling model. • Consider adaptive punishments for FV enterprises during the transition process. • Check impacts of two players on strategy choice of a third player at the same time. • Consider the herd effect and time-delay effect to improve the evolutionary game. [ABSTRACT FROM AUTHOR]

This paper presents a novel architecture, termed Fusion-Fission Optimisation (FuFi) based Convolutional Neural Network with Bi-Long Short Term Memory Network (FuFi-CNN-Bi-LSTM), to enhance state of charge (SoC) estimation performance. The proposed FuFi-CNN-Bi-LSTM model leverages the power of both Convolutional Neural Networks (CNN) and Bi-Long Short Term Memory Networks (Bi-LSTM) while utilizing FuFi optimization to effectively tune the hyperparameters of the network. This optimization technique facilitates efficient SoC estimation by finding the optimal configuration of the model. A comparative analysis is conducted against FuFi Algorithm-based models, including FuFi-CNN-LSTM, FuFi-Bi-LSTM, FuFi-LSTM, and FuFi-CNN. The comparison involves assessing performance on SoC estimation tasks and identifying the strengths and limitations of models. Furthermore, the proposed FuFi-CNN-Bi-LSTM model undergoes rigorous testing on various drive cycle tests, including HPPC, HWFET, UDDS, and US06, at different temperatures ranging from -20 to 25 degrees Celsius. The model's robustness and reliability are assessed under different real-world operating conditions using well-established evaluation indexes, including Relative Error (RE),Mean Absolute Error (MAE), R Square (R 2) , and Granger Causality Test. The results demonstrate that the proposed FuFi-CNN-Bi-LSTM model achieves efficient SoC estimation performance across a wide range of temperatures at higher and lower ranges. This finding signifies the model's efficacy in accurately estimating SoC in various operating conditions. • FuFi-CNN-Bi-LSTM combines CNN and Bi-LSTM with FuFi optimization for SoC estimation. • Optimal hyperparameter tuning in FuFi-CNN-Bi-LSTM enhances SoC estimation accuracy. • Comparison with FuFi-based models shows FuFi-CNN-Bi-LSTM's superior performance. • Tested across HPPC, HWFET, UDDS, US06 cycles, proving robustness in diverse conditions. • Achieves accurate SoC estimation across temperature ranges, demonstrating reliability. [ABSTRACT FROM AUTHOR]

This article focuses on optimizing electric vehicle charging in distribution networks, emphasizing technical and economic considerations. Unlike traditional methods, the proposed intelligent approach tailors each EV's charging based on specific daily trip energy requirements. Vehicle owners provide trip data to the charge management system, enabling precise charging calculations considering factors such as energy tariffs, distribution network limits, and charging levels. The paper introduces the horse herd optimization algorithm, inspired by horse herd behavior, offering advantages like reduced computational time and improved convergence in maximizing power generation, especially under shading conditions. The comparative analysis of smart EV charging under normal and fast conditions, considering various constraints and load response programs, demonstrates the proposed method's effectiveness. Numerical results reveal a 46.02 % average load reduction and a 20.53 % peak load decrease with a Load Response Program. Charging costs are optimized, with Case 2 exhibiting a 2.61 % cost reduction compared to Case 3. The study delves into charging frequencies, discharge frequencies, total unsupplied energy, and unloaded energy for each case, providing crucial insights into algorithmic performance. The horse herd optimization algorithm-based approach proves superior, offering a promising solution for efficient and cost-effective electric vehicle charging in distribution networks. Furthermore, graphical representations illustrate the algorithm's impact on charging power, energy allocation, power passing through distribution posts, and megavolt-ampere flow through network lines. These numerical and graphical analyses provide a comprehensive understanding of the horse herd optimization algorithm capabilities, emphasizing its potential to optimize power distribution, reduce costs, and enhance the resilience of residential distribution networks. • Prioritizes EV charging based on daily travel and battery levels using discrete charge rates. • Compares normal and fast charging rates, considering transformer overloads. • Adapts network and load model from real data, balancing subscriber and operator interests. • Assesses method's impact with and without a load response program for adaptability. • Introduces the Horse Herd Optimization Algorithm (HOA) inspired by horse herd behavior. [ABSTRACT FROM AUTHOR]

With the rapid development of electric vehicle charging stations, effective management of them has become challenging due to the high uncertainty of electric vehicles, the pricing mechanisms of charging stations, and their coupling with distribution networks. To address these challenges, this paper proposes a two-stage framework for energy management at charging stations. In the first stage, a resource allocation model considering the profits of distribution systems, charging stations, and electric vehicle users is established based on the aggregate feasible power regions of charging stations. The aggregate feasible region is obtained based on the combination of Minkowski summation and the data-driven method, which can preserve the privacy of electric vehicle data and reduce the computational burden. In the second stage, a novel hierarchical pricing mechanism is developed, which encompasses both the clearing price between charging stations and distribution networks and the retail electricity price between charging stations and electric vehicle users. Notably, charging stations participate in the power clearing of distributed networks based on the aggregate feasible power region, while a two-stage robust pricing strategy is established between electric vehicle users and charging stations. The model is finally optimized through a distributed coordination mechanism with a clear physical interpretation. The simulation results show that the proposed aggregation method enables charging stations to achieve a total economic profit at least 1.76 % higher than three competitive methods. The hierarchical pricing mechanism allows charging stations to achieve total economic profits 18.60 % and 2.94 % higher than those in the centralized dispatch and price-taker modes, respectively, while simultaneously reducing operating costs for the distributed network by 25.96 % and 27.99 %. • A novel two-stage framework for energy management of electric vehicle charging stations is proposed. • A hierarchical pricing mechanism for charging stations is defined. • A new computation method for the aggregate feasible power region of charging stations. • A novel distributed management mechanism is designed to achieve the coordination of three participants. • Comprehensive performance comparisons are conducted to highlight the advantages of the framework. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has emerged as a promising approach for optimizing energy management strategies (EMS) in new energy vehicles. Nevertheless, existing studies typically focus on evaluating the performance of a particular algorithm, overlooking critical implementation details and lacking comprehensive analysis of their real-world applicability. In this paper, we mitigate this issue by conducting a thorough practicability analysis of DRL-based EMS methods. First, we theoretically analyze the benefits and limitations of existing DRL-based EMS approaches based on taxonomy towards their practicability. Subsequently, a novel EMS method that leverages model-based DRL algorithms that other researchers typically underestimate is proposed. The method newly introduces an uncertainty-aware model-based algorithm known as Probabilistic Ensembles with Trajectory Sampling (PETS) and is validated utilizing a four-wheel-drive (4WD) battery electric vehicle (BEV). After that, we conduct a comprehensive practicability analysis of three state-of-the-art DRL algorithms considering critical aspects for real-world deployment, e.g., hyperparameter sensitivity and algorithm transferability. The results demonstrate that even though the on-policy DRL achieves better asymptotic rewards and the off-policy DRL possesses better convergence, the proposed model-based DRL, PETS-based EMS, outperforms others regarding superior robustness and promising transferability across different extents of relevance between tasks. Besides, energy consumption results demonstrate that the model-based EMS can achieve a considerable 96.6% optimality compared to the baseline dynamic programming (DP). Thus, motivated by the challenges of applying DRL algorithms to real-world EMS, our systematic investigation and new insights contribute to advancing the practical employment of DRL-based EMS for BEVs. • A practicability analysis of DRL-based EMS is conducted. • A novel EMS method leveraging a model-based DRL algorithm, PETS, is proposed. • The sensitivity of the DRL-based EMS to hyperparameters is studied. • The transferability between tasks of the DRL-based EMS is investigated. • EMS based on model-based DRL performs better robustness. [ABSTRACT FROM AUTHOR]

State of health (SOH) is crucial to battery management system. However, SOH accuracy and reliability is difficult to be guaranteed owing to complex ageing mechanisms and driving conditions. This paper proposes a novel battery SOH prediction method based on gated recurrent unit (GRU) neural network for real-world vehicles. Firstly, real-vehicle operating data is extracted as well as cleaned and sliced to improve data reliability, then the Kalman filter and recursive least squares method are used to identify the ohmic internal resistance (OIR). The GRU neural network is established to realize the battery SOH prediction, and the average relative error of final validation set is less than 0.65 %. To further validate the reliability and applicability of the prediction method, four other vehicles data are set as input, and the SOH results indicate that all average relative errors are still within 4 %. This study demonstrates the feasibility of using OIR as a health factor for real-world vehicles, and also provides a new solution for accurately predicting battery SOH for real-world vehicles. • Precise SOH prediction is a key factor in preventing battery safety accidents. • A novel battery SOH prediction method based on GRU neural network is proposed. • Ohmic internal resistance from real-world vehicles is used as health factor. • The reliability and stability of the model is verified by accurate results. • A novel solution for real-world vehicle's battery SOH prediction is provided. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

This study evaluates the global warming potential (GWP) impact of producing lithium-ion batteries (LIBs) in emerging European Gigafactories. The paper presents a cradle-to-gate (CTG) life cycle assessment (LCA) of nickel-manganese-cobalt (NMC) chemistries for battery electric vehicle (BEV) applications. We consider three scenarios to cover the most probable production routes in Germany, France, and Italy, foreseen as the largest European LIB producers by 2030. The energy demand for manufacturing considers two cases: electricity only and a mix of heat and electricity. The results show that European Gigafactories can reduce the overall GWP relative to 1 kWh of NMC battery, with respect to Chinese NMC LIBs, in a range of 32–60%. This corresponds to a decrease in equivalent CO 2 emission of 32–81 kg CO 2 eq., depending on the location, the energy demand and the NMC chemistry, if the whole production takes place in the facility. French Gigafactories obtain the upper bound of this reduction. A sensitivity analysis of the source of the lithium compound, used to produce the active cathode material, shows that increasing the nickel content decreases the GWP impact per kWh of battery capacity. However, NMC622 generates less equivalent CO 2 than NMC811, for lithium compound produced from Chilean brine. In addition, a simplified analysis of the utilization phase of two different classes of BEVs shows the positive effects of the regional LIB production and of the low carbon intensity of the electricity mix. • Global warming potential impact of producing lithium-ion batteries in Europe. • Carbon footprint of nickel-manganese-cobalt chemistries for electric vehicle application. • European Gigafactories can reduce the overall Global warming potential impact. • Sensitivity analysis on critical material for the NMC batteries. • Comparison of the impact due to the lithium compound from ores or brines. [ABSTRACT FROM AUTHOR]

The reliability and effectiveness of today's electrical grids rely heavily on optimal reactive power dispatch (ORPD). The ORPD problem gets more challenging to resolve in the setting of ever-increasingly dynamic and complex systems. To handle the ORPD while also considering the existence of electric vehicles, this research introduces a novel technique: the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm. Byreducing both active power loss and voltage variation, the proposed PSOSHO approach aims at improving the efficiency and reliability of the power grid. Simulation studies on reference power grids, including the IEEE 30-bus and IEEE 57-bus networks, verified its efficacy. Existing metaheuristic optimization techniques are compared using the same restrictions, governing parameters, and data. The results show that the PSOSHO method is trustworthy and effective in resolving the ORPD problem. One of the most pressing issues facing today's electricity grids is how to accommodate the growing number of electric vehicles. Real data on electric vehicles are incorporated in the analyses to obtain a realistic study. The suggested PSOSHO algorithm is a significant step forward in this area, providing a reliable and effective answer to the problem of optimum reactive power dispatch and helping to ensure the long-term viability of power systems in the age of electromobility. • This paper presents a novel optimal energy management of smart grids. • The model includes electric vehicles variability and uncertainties. • A practical study is presented including real data of electric vehicles. • A novel hybrid optimization algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

Estimating state of health for battery module is one of the most significant and challenging techniques to promote the commercialization of electric vehicles. Based on the relationship changes of branch current and its estimation error during aging, a state of health estimation general framework is presented for battery module. Firstly, the parallel battery module aging experiment is designed. In addition, the consistency changes of branches were analyzed. A neural network model utilizing dual back-propagation for estimating branch current errors was developed by employing the experimental data of battery module. Through estimation error of branch current under five working conditions, two aging characteristics are extracted, one is the slope of compensation value and current, the other is the slope of compensation value and current change rate. These features are fed into gaussian process regression training to obtain a state of health estimation model for the battery module. Furthermore, the model is validated with new european driving cycle working condition. Finally, a dual bidirectional long short-term memory neural network is utilized to illustrate the versatility of the presented universal framework, which can effectively estimate state of health of battery module with the maximum relative error of 2.1226 %. • A state of health estimation general framework for battery module is proposed. • The consistency variation of parallel battery module branches is anayzed in this paper. • Slope features of compensation error with current and current change rate are extracted. • The utilization of dual bidirectional neural networks elucidates the general framework. [ABSTRACT FROM AUTHOR]