The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated ...Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated charging scheduling strategy for multiple types of electric vehicles based on the degree of urgency of vehicle use.First,considering the range loss characteristics,dynamic time-sharing tariff mechanism,and user incentive policy in the lowtemperature environment of northern winter,a differentiated charging model is constructed for four types of vehicles:family cars,official cars,buses,and cabs.Then,we innovatively introduce the urgency parameter of charging demand for multiple types of vehicles and dynamically divide the emergency and non-emergency charging modes according to the difference between the regular charging capacity and the user’s minimum power demand.When the conventional charging capacity is less than the minimum power demand of the vehicle within the specified time,it is the emergency vehicle demand,and this type of vehicle is immediately charged in fast charging mode after connecting to the grid.On the contrary,it is a non-emergency demand,and the vehicle is connected to the grid to choose the appropriate time to charge in conventional charging mode.Finally,by optimizing the objective function to minimize the peakto-valley difference between the grid and the vehicle owner’s charging cost,and designing the charging continuity constraints to avoid battery damage,it ensures that the vehicle is efficiently dispatched under the premise of meeting the minimum power demand.Simulation results show that the proposed charging strategy can reduce the charging cost of vehicle owners by 26.33%,reduce the peak-to-valley difference rate of the grid by 29.8%,and significantly alleviate the congestion problem during peak load hours,compared with the disordered charging mode,while ensuring that the electric vehicles are not overcharged and meet the electricity demand of vehicle owners.This paper solves the problems of the existing research on the singularity of vehicle models and the lack of environmental adaptability and provides both economic and practical solutions for the cooperative optimization of electric vehicles and power grids in multiple scenarios.展开更多
The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on e...The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.展开更多
With increasingly serious environmental pollution and the energy crisis,fuel cell hybrid electric vehicles have been considered as an ideal alternative to traditional hybrid electric vehicles.Nevertheless,the total co...With increasingly serious environmental pollution and the energy crisis,fuel cell hybrid electric vehicles have been considered as an ideal alternative to traditional hybrid electric vehicles.Nevertheless,the total costs of fuel cell systems are still too high,thus limiting the further development of fuel cell hybrid electric vehicles.This paper presents an energy management strategy(EMS)based on deep reinforcement learning for the energy management of fuel cell hybrid electric vehicles.The energy management model of a fuel cell hybrid electric bus and its main components are established.Considering the power response characteristics of the fuel cell system,the power change rate of the fuel cell system is reasonably limited and introduced as action variables into the network of Double Deep Q-Learning(DDQL),and a novel DDQL-based EMS is developed for the fuel cell hybrid electric bus.Subsequently,a comparative test is conducted with the DP-based and the Rule-based EMS to analyze the performance of the DDQL-based EMS.The results indicate that the proposed EMS achieves good fuel economy performance,with an improvement of 15.4%compared to the Rule-based EMS under the training scenarios.In terms of generalization performance,the proposed EMS also achieves good fuel economy performance,which improves by 13.3%compared to the Rule-based energy management strategy under the testing scenario.展开更多
An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS fo...An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.展开更多
In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including i...In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.展开更多
Overviewing the air pollution situation in Hong Kong,energy generation and transportation are part of the contribution to the carbon emissions.Electric vehicles do not have engines and no air pollutants emissions.The ...Overviewing the air pollution situation in Hong Kong,energy generation and transportation are part of the contribution to the carbon emissions.Electric vehicles do not have engines and no air pollutants emissions.The promotion of electric vehicles serves as an important strategy to Hong Kong's goal to achieve carbon neutrality by 2050.This paper illustrated the financial incentives the Hong Kong Government has launched,including First Registration Tax concessions,profits tax deduction,One-for-One Scheme,lower license fee,subsidy support for e-buses and e-taxis,free charging services at government car parks,EV-charging at home Subsidy Scheme,etc.By comparing the cost of purchasing and owning vehicles with the cost of purchasing and owning electric vehicles as well as the market performance of electric vehicles to examine whether the financial incentives in Hong Kong can promote electric vehicles and serve as a prerequisite to low carbon transition.The results show that under government support and promotion associated with preferential policy,electric vehicles will become the future trend in Hong Kong with the advantage of lower emissions,energy saving,and environmental protection.展开更多
To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control s...To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.展开更多
In March 2025,prominent Chinese automaker NIO(Shanghai,China),the global leader in electric vehicle(EV)battery swapping,and Contemporary Amperex Technology Co.,Ltd.(CATL)(Nindge,China),the world’s biggest manufacture...In March 2025,prominent Chinese automaker NIO(Shanghai,China),the global leader in electric vehicle(EV)battery swapping,and Contemporary Amperex Technology Co.,Ltd.(CATL)(Nindge,China),the world’s biggest manufacturer of EV batteries,announced a strategic partnership to build the world’s largest battery swapping network,while also promoting unified standards and technologies[1].Just weeks later,CATL announced another partnership,this one with Chinese state-owned oil giant Sinopec(Beijing,China)to build 10000 new battery swapping stations in China,at least 500 in 2025[2].展开更多
Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems insta...Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems installed on roadways,they constitute a charging infrastructure or electrified roads(eRoads)that have many advantages.For instance,the large battery size of heavy-duty EVs can significantly be downsized due to charging-whiledriving.However,a high power demand of the DWC system,especially during traffic rush periods,could lead to voltage instability in the grid and undesirable power demand curves.In this paper,a model for the power demand is developed to predict the DWC system's power demand at various levels of EV penetration rate.The DWC power demand profile in the chosen 550 km section of a major highway in Canada is simulated.Solar photovoltaic(PV)panels are integrated with the DWC,and the integrated system is optimized to mitigate the peak power demand on the electrical grid.With solar panels of 55,000 kW rated capacity installed along roadsides in the study region,the peak power demand on the electrical grid is reduced from 167.5 to 136.1 MW or by 18.7%at an EV penetration rate of 30%under monthly average daily solar radiation in July.It is evidenced that solar PV power has effectively smoothed the peak power demand on the grid.Moreover,the locally generated renewable power could help ease off expensive grid upgrades and expansions for the eRoad.Also,the economic feasibility of the solar PV integrated DWC system is assessed using cost analysis metrics.展开更多
The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailin...The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailing eco-CACC implementations still exhibit limitations in fully harnessing the potential energy savings.This can be attributed to the intricate nature of the problem,characterized by its high nonlinearity and non-convexity,making it challenging for conventional solving methods to find solutions.In this paper,a novel strategy based on a decentralized model predictive control(MPC)framework,called predictive ecological cooperative control(PECC),is proposed for vehicle platoon control on hilly roads,aiming to maximize the overall energy efficiency of the platoon.Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories,this strategy employs an approach based on the combination of a long short-term memory network(LSTM)and genetic algorithm(GA)optimization(GA-LSTM)to predict the future speed of the leading vehicle.Notably,a function named the NotchFilter function(NF(?))is introduced to transform the hard state constraints in the eco-CACC problem,thereby alleviating the burden of problem-solving.Finally,through simulation comparisons between PECC and a strategy based on the common eco-CACC modifications,the effectiveness of PECC in improving platoon energy efficiency is demonstrated.展开更多
Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasi...Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasing system reliability and operational efficiency.These devices are crucial in supporting the extensive incorporation of electric vehicles(EVs)and renewable energy sources(RESs)into new,load-centric environments.This study evaluates four unique FID-based configurations for distribution network interconnections,revealing their distinctive features.We developed a comprehensive evaluation framework and tool by integrating the analytic hierarchy process(AHP)and fuzzy comprehensive evaluation(FCE),which includes five key performance indicators to assess these configurations.The study identifies the optimal application scenarios for each configuration and discusses their roles in enabling the seamless integration of EVs and RESs.The findings provide essential insights and guidelines for the design and implementation of adaptable,interconnected distribution networks that are equipped to meet the growing demands of future urban environments.展开更多
This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendri...This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendrites.These dendrites may pierce the separator,leading to the failure of the insulation layer between electrodes and causing micro short circuits.When a micro short circuit occurs,the electrolyte typically undergoes exothermic reactions,leading to thermal runaway and posing a safety risk to users.Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention.To address this issue,the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries.This algorithm can effectively diagnose micro short circuits,aging,and normal batteries using minimal battery data,thereby improving diagnostic accuracy and enhancing the flexibility of fault detection.Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm,demonstrating its ability to distinguish between short-circuited,aged,and normal batteries under different conditions.This technology can be applied to electric vehicles and energy storage systems,enabling early warnings to ensure safety and prevent thermal runaway.展开更多
Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. batte...Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.展开更多
Aim Toshorten integral design period of electric vehicles Methods The electric vehicle simulation program(EVSP), a modular user-friendly program which is written in Borland C++ OWL for Windows was developed. Results ...Aim Toshorten integral design period of electric vehicles Methods The electric vehicle simulation program(EVSP), a modular user-friendly program which is written in Borland C++ OWL for Windows was developed. Results EVSP allows simulating the dynamic and the economy performance of electric vehicles.EVSP provides many kinds of data input module,a large components library of electric vehicles and several kinds of speed cycle with these library,it is easily to develop a new concept of different drive trains or even to compare or improve the existing electric vehicles. The paper simulated the performance of YW6120DD Electric Bus, and analyzed the test results comparing with simulation results Conclusion The simulation results indicate that the EVSP may contribute to the developments of electric vehicles in general and the definition of the optimal match management in the drive train in particular.展开更多
The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although ...The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.展开更多
The global adoption of Electric Vehicles(EVs)is on the rise due to their advanced features,with projections indicating they will soon dominate the private vehicle market.However,improper management of EV charging can ...The global adoption of Electric Vehicles(EVs)is on the rise due to their advanced features,with projections indicating they will soon dominate the private vehicle market.However,improper management of EV charging can lead to significant issues.This paper reviews the development of high-power,reliable charging solutions by examining the converter topologies used in rectifiers and converters that transfer electricity from the grid to EV batteries.It covers technical details,ongoing developments,and challenges related to these topologies and control strategies.The integration of rapid charging stations has introduced various Power Quality(PQ)issues,such as voltage fluctuations,harmonic distortion,and supra-harmonics,which are discussed in detail.The paper also highlights the benefits of controlled EV charging and discharging,including voltage and frequency regulation,reactive power compensation,and improved power quality.Efficient energy management and control strategies are crucial for optimizing EV battery charging within microgrids to meet increasing demand.Charging stations must adhere to specific converter topologies,control strategies,and industry standards to function correctly.The paper explores microgrid architectures and control strategies that integrate EVs,energy storage units(ESUs),and Renewable Energy Sources(RES)to enhance performance at charging points.It emphasizes the importance of various RES-connected architectures and the latest power converter topologies.Additionally,the paper provides a comparative analysis of microgrid-based charging station architectures,focusing on energy management,control strategies,and charging converter controls.The goal is to offer insights into future research directions in EV charging systems,including architectural considerations,control factors,and their respective advantages and disadvantages.展开更多
This article presents the research and development of an electric vehicle（EV） in Department of Human-Robotics Saitama Institute of Technology,Japan.Electric mobile systems developed in our laboratory include a conve...This article presents the research and development of an electric vehicle（EV） in Department of Human-Robotics Saitama Institute of Technology,Japan.Electric mobile systems developed in our laboratory include a converted electric automobile,electric wheelchair and personal mobile robot.These mobile systems contribute to realize clean transportation since energy sources and devices from all vehicles,i.e.,batteries and electric motors,does not deteriorate the environment.To drive motors for vehicle traveling,robotic technologies were applied.展开更多
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
基金funded by Science and Technology Project of SGCC(SGJLCC00KJJS2203595).
文摘Aiming at the problem of increasing the peak-to-valley difference of grid load and the rising cost of user charging caused by the disorderly charging of large-scale electric vehicles,this paper proposes a coordinated charging scheduling strategy for multiple types of electric vehicles based on the degree of urgency of vehicle use.First,considering the range loss characteristics,dynamic time-sharing tariff mechanism,and user incentive policy in the lowtemperature environment of northern winter,a differentiated charging model is constructed for four types of vehicles:family cars,official cars,buses,and cabs.Then,we innovatively introduce the urgency parameter of charging demand for multiple types of vehicles and dynamically divide the emergency and non-emergency charging modes according to the difference between the regular charging capacity and the user’s minimum power demand.When the conventional charging capacity is less than the minimum power demand of the vehicle within the specified time,it is the emergency vehicle demand,and this type of vehicle is immediately charged in fast charging mode after connecting to the grid.On the contrary,it is a non-emergency demand,and the vehicle is connected to the grid to choose the appropriate time to charge in conventional charging mode.Finally,by optimizing the objective function to minimize the peakto-valley difference between the grid and the vehicle owner’s charging cost,and designing the charging continuity constraints to avoid battery damage,it ensures that the vehicle is efficiently dispatched under the premise of meeting the minimum power demand.Simulation results show that the proposed charging strategy can reduce the charging cost of vehicle owners by 26.33%,reduce the peak-to-valley difference rate of the grid by 29.8%,and significantly alleviate the congestion problem during peak load hours,compared with the disordered charging mode,while ensuring that the electric vehicles are not overcharged and meet the electricity demand of vehicle owners.This paper solves the problems of the existing research on the singularity of vehicle models and the lack of environmental adaptability and provides both economic and practical solutions for the cooperative optimization of electric vehicles and power grids in multiple scenarios.
文摘The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.
基金Supported by National Natural Science Foundation of China(Grant Nos.U1864205,Grant No.52172377).
文摘With increasingly serious environmental pollution and the energy crisis,fuel cell hybrid electric vehicles have been considered as an ideal alternative to traditional hybrid electric vehicles.Nevertheless,the total costs of fuel cell systems are still too high,thus limiting the further development of fuel cell hybrid electric vehicles.This paper presents an energy management strategy(EMS)based on deep reinforcement learning for the energy management of fuel cell hybrid electric vehicles.The energy management model of a fuel cell hybrid electric bus and its main components are established.Considering the power response characteristics of the fuel cell system,the power change rate of the fuel cell system is reasonably limited and introduced as action variables into the network of Double Deep Q-Learning(DDQL),and a novel DDQL-based EMS is developed for the fuel cell hybrid electric bus.Subsequently,a comparative test is conducted with the DP-based and the Rule-based EMS to analyze the performance of the DDQL-based EMS.The results indicate that the proposed EMS achieves good fuel economy performance,with an improvement of 15.4%compared to the Rule-based EMS under the training scenarios.In terms of generalization performance,the proposed EMS also achieves good fuel economy performance,which improves by 13.3%compared to the Rule-based energy management strategy under the testing scenario.
基金supported in part by the National Natural Science Foundation of China(52172377).
文摘An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.
基金the support of the National Natural Science Foundation of China(72325006,72488101,and 72293601)the Sze Family Foundationthe Climate Imperative Foundation(#2024-001465)
文摘In China,electric vehicle(EV)fast-charging power has quadrupled in the past five years,progressing toward 10-minute ultrafast charging.This rapid increase raises concerns about the impact on the power grid including increased peak power demand and the need for substantial upgrades to power infrastruc-ture.Here,we introduce an integrated model to assess fast and ultrafast charging impacts for represen-tative charging stations in China,combining real-world charging patterns and detailed station optimization models.We find that larger stations with 12 or more chargers experience modest peak power increases of less than 30%when fast-charging power is doubled,primarily because shorter charg-ing sessions are less likely to overlap.For more typical stations(e.g.,8-9 chargers and 120 kW·charger^(−1)),upgrading chargers to 350-550 kW while allowing managed dynamic waiting strategies(of∼1 minute)can reduce overall charging times to∼9 minutes.At stations,deploying battery storage and/or expanding transformers can help manage future increases in station loads,yet the primary device cost of the former is∼4 times higher than that of the latter.Our results offer insights for charging infrastructure planning,EV-grid interactions,and associated policymaking.
文摘Overviewing the air pollution situation in Hong Kong,energy generation and transportation are part of the contribution to the carbon emissions.Electric vehicles do not have engines and no air pollutants emissions.The promotion of electric vehicles serves as an important strategy to Hong Kong's goal to achieve carbon neutrality by 2050.This paper illustrated the financial incentives the Hong Kong Government has launched,including First Registration Tax concessions,profits tax deduction,One-for-One Scheme,lower license fee,subsidy support for e-buses and e-taxis,free charging services at government car parks,EV-charging at home Subsidy Scheme,etc.By comparing the cost of purchasing and owning vehicles with the cost of purchasing and owning electric vehicles as well as the market performance of electric vehicles to examine whether the financial incentives in Hong Kong can promote electric vehicles and serve as a prerequisite to low carbon transition.The results show that under government support and promotion associated with preferential policy,electric vehicles will become the future trend in Hong Kong with the advantage of lower emissions,energy saving,and environmental protection.
基金supported by the National Natural Science Foundation of China(52077078)China Southern Power Grid Company Limited 036000KK52220004(GDKJXM20220147).
文摘To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.
文摘In March 2025,prominent Chinese automaker NIO(Shanghai,China),the global leader in electric vehicle(EV)battery swapping,and Contemporary Amperex Technology Co.,Ltd.(CATL)(Nindge,China),the world’s biggest manufacturer of EV batteries,announced a strategic partnership to build the world’s largest battery swapping network,while also promoting unified standards and technologies[1].Just weeks later,CATL announced another partnership,this one with Chinese state-owned oil giant Sinopec(Beijing,China)to build 10000 new battery swapping stations in China,at least 500 in 2025[2].
基金Funding for this work was provided by Natural Resources Canada through the Program of Energy Research and Development.
文摘Electrification of roadways using dynamic wireless charging(DWC)technology can provide an effective solution to range anxiety,high battery costs and long charging times of electric vehicles(EVs).With DWC systems installed on roadways,they constitute a charging infrastructure or electrified roads(eRoads)that have many advantages.For instance,the large battery size of heavy-duty EVs can significantly be downsized due to charging-whiledriving.However,a high power demand of the DWC system,especially during traffic rush periods,could lead to voltage instability in the grid and undesirable power demand curves.In this paper,a model for the power demand is developed to predict the DWC system's power demand at various levels of EV penetration rate.The DWC power demand profile in the chosen 550 km section of a major highway in Canada is simulated.Solar photovoltaic(PV)panels are integrated with the DWC,and the integrated system is optimized to mitigate the peak power demand on the electrical grid.With solar panels of 55,000 kW rated capacity installed along roadsides in the study region,the peak power demand on the electrical grid is reduced from 167.5 to 136.1 MW or by 18.7%at an EV penetration rate of 30%under monthly average daily solar radiation in July.It is evidenced that solar PV power has effectively smoothed the peak power demand on the grid.Moreover,the locally generated renewable power could help ease off expensive grid upgrades and expansions for the eRoad.Also,the economic feasibility of the solar PV integrated DWC system is assessed using cost analysis metrics.
基金Supported by National Natural Science Foundation of China(Grant Nos.52172383,51805081)Jiangsu Provincial Postgraduate Research&Practice Innovation Program(Grant No.KYCX22_0196)。
文摘The integration of eco-driving and cooperative adaptive cruise control(CACC)with platoon cooperative control(eco-CACC)has emerged as a pivotal approach for improving vehicle energy efficiency.Nonetheless,the prevailing eco-CACC implementations still exhibit limitations in fully harnessing the potential energy savings.This can be attributed to the intricate nature of the problem,characterized by its high nonlinearity and non-convexity,making it challenging for conventional solving methods to find solutions.In this paper,a novel strategy based on a decentralized model predictive control(MPC)framework,called predictive ecological cooperative control(PECC),is proposed for vehicle platoon control on hilly roads,aiming to maximize the overall energy efficiency of the platoon.Unlike most existing literature that focuses on suboptimal coordination under predefined leading vehicle trajectories,this strategy employs an approach based on the combination of a long short-term memory network(LSTM)and genetic algorithm(GA)optimization(GA-LSTM)to predict the future speed of the leading vehicle.Notably,a function named the NotchFilter function(NF(?))is introduced to transform the hard state constraints in the eco-CACC problem,thereby alleviating the burden of problem-solving.Finally,through simulation comparisons between PECC and a strategy based on the common eco-CACC modifications,the effectiveness of PECC in improving platoon energy efficiency is demonstrated.
基金supported by the Science and Technology Project of the China Southern Power Grid Co.,Ltd.(Project number:SZKJXM20230085).
文摘Flexible interconnection devices(FIDs)significantly enhance the regulation and management of complex power flows in distribution networks.Voltage source converter(VSC)-based FIDs,in particular,are pivotal for increasing system reliability and operational efficiency.These devices are crucial in supporting the extensive incorporation of electric vehicles(EVs)and renewable energy sources(RESs)into new,load-centric environments.This study evaluates four unique FID-based configurations for distribution network interconnections,revealing their distinctive features.We developed a comprehensive evaluation framework and tool by integrating the analytic hierarchy process(AHP)and fuzzy comprehensive evaluation(FCE),which includes five key performance indicators to assess these configurations.The study identifies the optimal application scenarios for each configuration and discusses their roles in enabling the seamless integration of EVs and RESs.The findings provide essential insights and guidelines for the design and implementation of adaptable,interconnected distribution networks that are equipped to meet the growing demands of future urban environments.
文摘This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendrites.These dendrites may pierce the separator,leading to the failure of the insulation layer between electrodes and causing micro short circuits.When a micro short circuit occurs,the electrolyte typically undergoes exothermic reactions,leading to thermal runaway and posing a safety risk to users.Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention.To address this issue,the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries.This algorithm can effectively diagnose micro short circuits,aging,and normal batteries using minimal battery data,thereby improving diagnostic accuracy and enhancing the flexibility of fault detection.Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm,demonstrating its ability to distinguish between short-circuited,aged,and normal batteries under different conditions.This technology can be applied to electric vehicles and energy storage systems,enabling early warnings to ensure safety and prevent thermal runaway.
文摘Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.
文摘Aim Toshorten integral design period of electric vehicles Methods The electric vehicle simulation program(EVSP), a modular user-friendly program which is written in Borland C++ OWL for Windows was developed. Results EVSP allows simulating the dynamic and the economy performance of electric vehicles.EVSP provides many kinds of data input module,a large components library of electric vehicles and several kinds of speed cycle with these library,it is easily to develop a new concept of different drive trains or even to compare or improve the existing electric vehicles. The paper simulated the performance of YW6120DD Electric Bus, and analyzed the test results comparing with simulation results Conclusion The simulation results indicate that the EVSP may contribute to the developments of electric vehicles in general and the definition of the optimal match management in the drive train in particular.
文摘The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO<sub>2</sub> emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.
文摘The global adoption of Electric Vehicles(EVs)is on the rise due to their advanced features,with projections indicating they will soon dominate the private vehicle market.However,improper management of EV charging can lead to significant issues.This paper reviews the development of high-power,reliable charging solutions by examining the converter topologies used in rectifiers and converters that transfer electricity from the grid to EV batteries.It covers technical details,ongoing developments,and challenges related to these topologies and control strategies.The integration of rapid charging stations has introduced various Power Quality(PQ)issues,such as voltage fluctuations,harmonic distortion,and supra-harmonics,which are discussed in detail.The paper also highlights the benefits of controlled EV charging and discharging,including voltage and frequency regulation,reactive power compensation,and improved power quality.Efficient energy management and control strategies are crucial for optimizing EV battery charging within microgrids to meet increasing demand.Charging stations must adhere to specific converter topologies,control strategies,and industry standards to function correctly.The paper explores microgrid architectures and control strategies that integrate EVs,energy storage units(ESUs),and Renewable Energy Sources(RES)to enhance performance at charging points.It emphasizes the importance of various RES-connected architectures and the latest power converter topologies.Additionally,the paper provides a comparative analysis of microgrid-based charging station architectures,focusing on energy management,control strategies,and charging converter controls.The goal is to offer insights into future research directions in EV charging systems,including architectural considerations,control factors,and their respective advantages and disadvantages.
文摘This article presents the research and development of an electric vehicle（EV） in Department of Human-Robotics Saitama Institute of Technology,Japan.Electric mobile systems developed in our laboratory include a converted electric automobile,electric wheelchair and personal mobile robot.These mobile systems contribute to realize clean transportation since energy sources and devices from all vehicles,i.e.,batteries and electric motors,does not deteriorate the environment.To drive motors for vehicle traveling,robotic technologies were applied.
