Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements.Mathematical optimization becomes too slow at scale,while online reinforce...Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements.Mathematical optimization becomes too slow at scale,while online reinforcement learning struggles with sparse rewards and safety.This paper proposes GNN-DT,a topology-aware Decision Transformer that combines graph neural network embeddings with sequence modeling to learn charging policies from offline trajectories.The method operates over variable numbers of vehicles and chargers without retraining.Evaluated on realistic smart charging scenarios,GNN-DT achieves near-optimal performance,reaching rewards within 5 percent of an oracle solver while using up to 10×fewer training trajectories than baseline methods.It consistently outperforms online and offline reinforcement learning approaches and generalizes to unseen fleet sizes and network topologies.Inference runs in milliseconds,making the approach suitable for real-time deployment in large-scale charging systems.展开更多
The decarbonization of power and transportation systems faces critical challenges in infrastructure coordination and grid stability,despite rapid growth in electric vehicles(EVs)and renewable energy.This commentary pr...The decarbonization of power and transportation systems faces critical challenges in infrastructure coordination and grid stability,despite rapid growth in electric vehicles(EVs)and renewable energy.This commentary proposes the 5S framework—smart charging,synergistic infrastructure,and storable grid for a stable and sustainable power system—to harmonize these systems across individual,regional,and trans-regional levels.The 5S framework highlights the transformative potential of autonomous vehicles,V2X connectivity,and AI in achieving stable,sustainable,and synergistic energy-transportation systems.This approach offers a scalable roadmap for global stakeholders to accelerate Net Zero Emissions goals while addressing infrastructure gaps and systemic inefficiencies.展开更多
The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)i...The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy markets.This study presents an assessment of various strategies for EV aggregators.In this analysis,the smart charging methodology proposed in a previous study is considered.The smart charging technique employs charging power rate modulation and considers user preferences.To adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,Ecuador.Different actions are simulated,and the EV aggregator costs and technical conditions are evaluated.展开更多
With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging sta...With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.展开更多
This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles(SEVs).The model takes into account two prevalent smart charging strategies:the Ti...This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles(SEVs).The model takes into account two prevalent smart charging strategies:the Time-of-Use(TOU)tariff and Vehicle-to-Grid(V2G)technology.We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users,utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset.Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations.For SEV operators,the use of TOU and V2G strategies could potentially reduce charging costs by 17.93%and 34.97%respectively.In the scenarios with V2G applied,the average discharging demand is 2.15kWh per day per SEV,which accounts for 42.02%of the actual average charging demand of SEVs.These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.展开更多
The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for i...The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.展开更多
Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited commun...Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs,as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself.A straightforward and interoperable method for extracting information from charging vehicles(e.g.,vehicle model,battery capacity,and State of Charge)could significantly enhance the implementation of advanced smart charging strategies,unlocking the flexibility of connected EVs,enabling cost reductions and supporting the provision of ancillary services to the grid.This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors.The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs(e.g.,brand,model,year,battery capacity,End-of-Charge status)by exclusively considering their charging profile in response to specific prescribed current setpoints.Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs,an essential component in vehicle-to-grid(V2G)applications.Extensive practical demonstrations based on experimental data are provided to validate the identification procedure.An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.展开更多
This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of r...This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of ride matching,vehicle routing,rebalancing,and charging.The system ensures that electric autonomous vehicles provide transportation services for up to two customers to share a ride and that they can be charged automatically during the operating period.The RE-AMoD problem is formulated as a network flow optimization problem considering ride-sharing and charging control.The objective is to minimize the customers’waiting time while minimizing the system’s energy consumption.An iterative MPC is developed to compute the optimal control policy for real-time control.The case study uses real-world data from San Francisco to validate the model performance by comparing benchmark models in an RE-AMoD simulation platform and investigating the impact of ridesharing and smart charging strategies on system performance by comparing models with no ride-sharing and heuristic charging strategies.The results show that the smart charging policy is critical for realizing ride-sharing's full advantages in RE-AMoD systems.Allowing the sharing of trips significantly improves system performance in terms of reducing fleet sizes and energy consumption while improving the customer level of service.展开更多
This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy syste...This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near future.Following a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the UK.It then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging facilities.Circuit topologies of charging infrastructure are reviewed.Next,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in detail.Furthermore,the management and operation of charging infrastructure including different types of business models are summarized.Last but not least,challenges and future trends are discussed.展开更多
The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection.This paper focuses on the...The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection.This paper focuses on the optimization of EV charging,which cannot be ignored in the rapid development of EVs.The increase in the penetration of EVs will generate new electrical loads during the charging process,which will bring new challenges to local power systems.Moreover,the uncoordinated charging of EVs may increase the peak-to-valley difference in the load,aggravate harmonic distortions,and affect auxiliary services.To stabilize the operations of power grids,many studies have been carried out to optimize EV charging.This paper reviews these studies from two aspects:EV charging forecasting and coordinated EV charging strategies.Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models.At the end of this paper,recommendations are given to address the challenges of EV charging and associated charging strategies.展开更多
To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging...To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.展开更多
Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehi...Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.展开更多
Across the U.S.,the increasing demand for electric vehicle(EV)charging infrastructure is placing new demands on the power grid,challenging its stability and efficiency.To address these challenges,this study proposes a...Across the U.S.,the increasing demand for electric vehicle(EV)charging infrastructure is placing new demands on the power grid,challenging its stability and efficiency.To address these challenges,this study proposes a Vehicle-to-Building-to-Grid(V2B2G)framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging.Using anonymized GPS traces from the study community,we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user.The framework was tested in a mixed-use community in Phoenix,Arizona that includes both residential and commercial buildings.A comprehensive decentralized model predictive control(MPC)framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort.Four different control strategies were designed and evaluated,the strategy which balances both user and grid benefits demonstrated the best performance,achieving:(1)a flattened grid net load curve,with a 56%reduction in on-peak demand and a 56%decrease in peak load rebound;(2)a 37.96%reduction in grid net load compared to the baseline control;and(3)a 68.05%performance improvement when considering six flexibility factors:cost savings,total-energy reduction,on-peak demand reduction,off-peak demand reduction,load shifting from peak to non-peak hours,and peak-load-rebound reduction.These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management.展开更多
In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on mi...In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed.The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners.The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically.Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account.The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.展开更多
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金DriVe2X research and in-novation project from the European Commission with grant number 101056934use of computational re-sources of the DelftBlue supercomputer,provided by Delft High Per-formance Computing Centre(https://www.tudelft.nl/dhpc)Dutch national e-infrastructure with the support of the SURF Cooperative,using grant no.EINF-5716.
文摘Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements.Mathematical optimization becomes too slow at scale,while online reinforcement learning struggles with sparse rewards and safety.This paper proposes GNN-DT,a topology-aware Decision Transformer that combines graph neural network embeddings with sequence modeling to learn charging policies from offline trajectories.The method operates over variable numbers of vehicles and chargers without retraining.Evaluated on realistic smart charging scenarios,GNN-DT achieves near-optimal performance,reaching rewards within 5 percent of an oracle solver while using up to 10×fewer training trajectories than baseline methods.It consistently outperforms online and offline reinforcement learning approaches and generalizes to unseen fleet sizes and network topologies.Inference runs in milliseconds,making the approach suitable for real-time deployment in large-scale charging systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.72288101,72431009,72171210,and 72350710798)Natural Science Foundation of Zhejiang Province,China(LZ23E080002).
文摘The decarbonization of power and transportation systems faces critical challenges in infrastructure coordination and grid stability,despite rapid growth in electric vehicles(EVs)and renewable energy.This commentary proposes the 5S framework—smart charging,synergistic infrastructure,and storable grid for a stable and sustainable power system—to harmonize these systems across individual,regional,and trans-regional levels.The 5S framework highlights the transformative potential of autonomous vehicles,V2X connectivity,and AI in achieving stable,sustainable,and synergistic energy-transportation systems.This approach offers a scalable roadmap for global stakeholders to accelerate Net Zero Emissions goals while addressing infrastructure gaps and systemic inefficiencies.
文摘The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy markets.This study presents an assessment of various strategies for EV aggregators.In this analysis,the smart charging methodology proposed in a previous study is considered.The smart charging technique employs charging power rate modulation and considers user preferences.To adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,Ecuador.Different actions are simulated,and the EV aggregator costs and technical conditions are evaluated.
基金supported in part by the National Key Research and Development Program of China under Grant No.2017YFF0108800in part by the National Natural Science Foundation of China under Grant No.61773109in part by the Major Program of National Natural Foundation of China under Grant No.61573094。
文摘With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.
基金National Natural Science Foundation of China(52002345)Public Policy Research Funding Scheme of The Government of the Hong Kong Special Administrative Region(Project Number:2023.A6.232.23B)+2 种基金Hong Kong Polytechnic University[P0013893P0038213P0041230].
文摘This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles(SEVs).The model takes into account two prevalent smart charging strategies:the Time-of-Use(TOU)tariff and Vehicle-to-Grid(V2G)technology.We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users,utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset.Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations.For SEV operators,the use of TOU and V2G strategies could potentially reduce charging costs by 17.93%and 34.97%respectively.In the scenarios with V2G applied,the average discharging demand is 2.15kWh per day per SEV,which accounts for 42.02%of the actual average charging demand of SEVs.These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.
文摘The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.
文摘Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs,as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself.A straightforward and interoperable method for extracting information from charging vehicles(e.g.,vehicle model,battery capacity,and State of Charge)could significantly enhance the implementation of advanced smart charging strategies,unlocking the flexibility of connected EVs,enabling cost reductions and supporting the provision of ancillary services to the grid.This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors.The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs(e.g.,brand,model,year,battery capacity,End-of-Charge status)by exclusively considering their charging profile in response to specific prescribed current setpoints.Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs,an essential component in vehicle-to-grid(V2G)applications.Extensive practical demonstrations based on experimental data are provided to validate the identification procedure.An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.
基金supported by the Swedish Strategic Research Area in Transportation(TRENoP).
文摘This study proposes an integer linear program model for ride-sharing,electric,autonomous mobility on demand(REAMoD)system operations and develops a model predictive control(MPC)algorithm to optimize the decisions of ride matching,vehicle routing,rebalancing,and charging.The system ensures that electric autonomous vehicles provide transportation services for up to two customers to share a ride and that they can be charged automatically during the operating period.The RE-AMoD problem is formulated as a network flow optimization problem considering ride-sharing and charging control.The objective is to minimize the customers’waiting time while minimizing the system’s energy consumption.An iterative MPC is developed to compute the optimal control policy for real-time control.The case study uses real-world data from San Francisco to validate the model performance by comparing benchmark models in an RE-AMoD simulation platform and investigating the impact of ridesharing and smart charging strategies on system performance by comparing models with no ride-sharing and heuristic charging strategies.The results show that the smart charging policy is critical for realizing ride-sharing's full advantages in RE-AMoD systems.Allowing the sharing of trips significantly improves system performance in terms of reducing fleet sizes and energy consumption while improving the customer level of service.
基金a research project in collaboration with and sponsored by XU JI Power Co.,Ltd.,Xuchang,China。
文摘This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near future.Following a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the UK.It then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging facilities.Circuit topologies of charging infrastructure are reviewed.Next,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in detail.Furthermore,the management and operation of charging infrastructure including different types of business models are summarized.Last but not least,challenges and future trends are discussed.
基金supported in part by UKRI EPSRC (No.EP/N032888/1)。
文摘The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection.This paper focuses on the optimization of EV charging,which cannot be ignored in the rapid development of EVs.The increase in the penetration of EVs will generate new electrical loads during the charging process,which will bring new challenges to local power systems.Moreover,the uncoordinated charging of EVs may increase the peak-to-valley difference in the load,aggravate harmonic distortions,and affect auxiliary services.To stabilize the operations of power grids,many studies have been carried out to optimize EV charging.This paper reviews these studies from two aspects:EV charging forecasting and coordinated EV charging strategies.Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models.At the end of this paper,recommendations are given to address the challenges of EV charging and associated charging strategies.
文摘To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.
文摘Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.
基金supported by the U.S.National Science Foundation(Award No.1949372).
文摘Across the U.S.,the increasing demand for electric vehicle(EV)charging infrastructure is placing new demands on the power grid,challenging its stability and efficiency.To address these challenges,this study proposes a Vehicle-to-Building-to-Grid(V2B2G)framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging.Using anonymized GPS traces from the study community,we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user.The framework was tested in a mixed-use community in Phoenix,Arizona that includes both residential and commercial buildings.A comprehensive decentralized model predictive control(MPC)framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort.Four different control strategies were designed and evaluated,the strategy which balances both user and grid benefits demonstrated the best performance,achieving:(1)a flattened grid net load curve,with a 56%reduction in on-peak demand and a 56%decrease in peak load rebound;(2)a 37.96%reduction in grid net load compared to the baseline control;and(3)a 68.05%performance improvement when considering six flexibility factors:cost savings,total-energy reduction,on-peak demand reduction,off-peak demand reduction,load shifting from peak to non-peak hours,and peak-load-rebound reduction.These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management.
基金This work was supported in part by the National Natural Science Foundation of China(No.51477067)in part by the China-UK Joint Project of the National Natural Science Foundation of China(No.51361130150)in part by the Fundamental Research Funds for the Central Universities(No.2014QN219).
文摘In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed.The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners.The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically.Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account.The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.
