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
Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transport...Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.展开更多
As a good measure to tackle the challenges from energy shortages and environmental pollution,Electric Vehicles(EVs)have entered a period of rapid growth.Battery swapping station is a very important way of energy suppl...As a good measure to tackle the challenges from energy shortages and environmental pollution,Electric Vehicles(EVs)have entered a period of rapid growth.Battery swapping station is a very important way of energy supply to EVs,and it is urgently needed to explore a coordinated control strategy to effectively smooth the load fluctuation in order to adopt the large-scale EVs.Considering bidirectional power flow between the station and power grid,this paper proposed a SFLA-based control strategy to smooth the load profile.Finally,compared simulations were performed according to the related data.Compared to particle swarm optimization(PSO)method,the presented SFLA-based strategy can effectively lower the peak-valley difference with the faster convergence rate and higher convergence precision.It is important for the swapping station that energy exchanging mode can supply energy for large-scale EVs with a smoother load profile than one-way charging mode.展开更多
Combined with the perspective of State Grid Corporation of China （SGCC）, the paper researches the EV battery swapping mode and its advantages, analyses standards requirements of battery swapping system and propos...Combined with the perspective of State Grid Corporation of China （SGCC）, the paper researches the EV battery swapping mode and its advantages, analyses standards requirements of battery swapping system and proposes the corresponding standards system展开更多
With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and effici...With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and efficiently satisfy the power supply requirements of diverse vehicle types.In order to solve this problem,a joint planning method of charging piles and charging-battery swapping stations(CBSSs)is proposed in this paper.In this method,the influence of geospatial constraints on the layout scale of charging piles is considered,and the Monte Carlo simulation method is used to predict the spatiotemporal distribution of charging and battery swapping demands of private electric vehicles(PEVs)and the battery swapping demands of taxi electric vehicles(TEVs)respectively.On this basis,the layout scale of charging piles of each functional area is determined during the maximum charging demand period in a day to meet the demands of PEVs for charging convenience.Then,an operating state model of CBSS is established for calculation of the objective function.At the same time,a planning model of CBSSs is established to minimize the annual social comprehensive cost,which takes into account the economy of CBSSs and the battery swapping convenience of EVs.The planning of CBSSs can meet the demands of TEVs and some PEVs for a rapid power supply.Finally,using the urban transportation network of Changchun and IEEE 33-node system as an example,the planning of charging piles and CCBSs in direct charging mode and peak shifting mode are simulated and analyzed.The simulation results show that the proposed method enables PEVs and TEVs to access convenient and rapid power supply,and the planning result of CBSSs in direct charging mode is more economical,while peak shifting mode is more conducive to the safe operation of distribution networks.展开更多
With the popularity of electric vehicles(EVs),the impact of disorderly charging on power grid stability cannot be ignored.As one of the energy supply methods of electric vehicles,the optimal scheduling of battery swap...With the popularity of electric vehicles(EVs),the impact of disorderly charging on power grid stability cannot be ignored.As one of the energy supply methods of electric vehicles,the optimal scheduling of battery swap station(BSS)can not only reduce the cost of BSS,but also ensure the stable operation of the power system.Therefore,this paper designs a BSS electricity price mechanism based on load demand forecasting and a user adaptive response mechanism based on battery health status and user satisfaction.On this basis,an optimal scheduling model of BSS system aiming at maximizing the revenue of BSS system is established.The simulation results show that the pricing strategy proposed in this paper can reduce the fluctuation of battery demand at each time and increase the revenue of BSS by 8.76%.In addition,the BSS optimal scheduling model proposed in this paper reduces the peak-to-average ratio of charging load from 2.52 to 2.10,realizing peak shaving and valley filling in the power system.展开更多
Insufficiencies in charging facilities limit the broad application of electric vehicles(EVs).In addition,EV can hardly represent a green option if its electricity primarily depends on fossil energy.Considering these t...Insufficiencies in charging facilities limit the broad application of electric vehicles(EVs).In addition,EV can hardly represent a green option if its electricity primarily depends on fossil energy.Considering these two problems,this paper studies a battery swapping-charging system based on wind farms(hereinafter referred to as W-BSCS).In a W-BSCS,the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station(CCS),which can centrally charge EV batteries and then distribute them to multiple battery swapping stations(BSSs).The operational framework of the W-BSCS is analyzed,and some preprocessing technologies are developed to reduce complexity in modeling.Then,a joint optimal scheduling model involving a wind power generation plan,battery swapping demand,battery charging and discharging,and a vehicle routing problem(VRP)is established.Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem.Numerical results verify the effectiveness of the joint optimal scheduling model,and they also show that the W-BSCS has great potential to promote EVs and wind power.展开更多
To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The C...To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The CLSC is used to depict the battery-swapping-charging process between the battery charging stations(BCSs)and battery swapping stations(BSSs).The arrival,departure and swapping service of electric vehicles(EVs)at BSSs is modeled as distinct queues based on the network calculus theory.The depleted batteries(DBs)and well-charging batteries(WBs)based interaction among BCSs and BSSs is formulated as a Stackelberg game.In the game,one BCS acts as the leader and the BSSs act as the followers.The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs,supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service(QoS)needed for battery swapping.The existence of Stackelberg equilibriums(SEs)of the proposed game is proved.A differential evaluation based hybrid algorithm is proposed to compute an SE.The effectiveness of proposed method has been demonstrated by the simulation results,guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.展开更多
Integration of electric vehicles(EVs),demand response and renewable energy will bring multiple opportunities for low carbon power system.A promising integration will be EV battery swapping station(BSS)bundled with PV(...Integration of electric vehicles(EVs),demand response and renewable energy will bring multiple opportunities for low carbon power system.A promising integration will be EV battery swapping station(BSS)bundled with PV(photovoltaic)power.Optimizing the configuration and operation of BSS is the key problem to maximize benefit of this integration.The main objective of this paper is to solve infrastructure configuration of BSS.The principle challenge of such an objective is to enhance the swapping ability and save corresponding investment and operation cost under uncertainties of PV generation and swapping demand.Consequently this paper mainly concentrates on combining operation optimization with optimal investment strategies for BSS considering multiscenarios PV power generation and swapping demand.A stochastic programming model is developed by using state flow method to express different states of batteries and its objective is to maximize the station’s net profit.The model is formulated as a mixed-integer linear program to guarantee the efficiency and stability of the optimization.Case studies validate the effectiveness of the proposed approach and demonstrate that ignoring the uncertainties of PV generation and swapping demand may lead to an inappropriate batteries,chargers and swapping robots configuration for BSS.展开更多
Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,bat...Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,battery swapping stations and ships are being explored since battery charging ships may not be feasible for inland long-distance trips.However,improper infrastructure planning for battery swapping stations and ships will increase costs and decrease operation efficiency.Therefore,a bilevel optimal infrastructure planning method is proposed in this paper for battery swapping stations and ships.First,the energy consumption model for the battery swapping ship is established considering the influence of the sailing environment.Second,a bilevel optimization model is proposed to minimize the total cost.Specifically,the battery swapping station(BSS)location problem is investigated at the upper level.The optimization of battery size in each battery swapping station and ship and battery swapping scheme are studied at the lower level based on speed and energy optimization.Finally,the bilevel self-adaptive differential evolution algorithm(BlSaDE)is proposed to solve this problem.The simulation results show that total cost could be reduced by 5.9%compared to the original results,and the effectiveness of the proposed method is confirmed.展开更多
Towards the analysis of the developmental situation of wind power generation and electric vehicles,a novel idea for stabilizing the fl uctuation of wind farms’output by the use of battery swapping stations of electri...Towards the analysis of the developmental situation of wind power generation and electric vehicles,a novel idea for stabilizing the fl uctuation of wind farms’output by the use of battery swapping stations of electric vehicles is put forward in this paper,to effectively alleviate the impact of grid-connected operation of wind farms on the power system while promoting the fi eld operation of charging and battery swapping stations.A battery swapping station is treated as a capacity-variable energy storage power station,connected to the output terminal of a wind farm.A combined operation model for wind farm and battery swapping station is established based on the MATLAB/SIMULINK simulation platform and the control strategy is proposed for the operation of battery swapping stations.The simulation results show that the introduction of a battery swapping station can effectively stabilize the fl uctuation of wind farm output.展开更多
An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and ...An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.展开更多
The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
With the integration of wind power,photovoltaic power,gas turbine,and energy storage,the novel battery charg-ing and swapping station(NBCSS)possesses significant opera-tional flexibility,which can aid in the service r...With the integration of wind power,photovoltaic power,gas turbine,and energy storage,the novel battery charg-ing and swapping station(NBCSS)possesses significant opera-tional flexibility,which can aid in the service restoration of dis-tribution system(DS)during power outages caused by extreme events.This paper presents an integrated optimization model for DS restoration that considers NBCSS,repair crews,and net-work reconfigurations simultaneously.The objective of this model is to maximize the restored load while minimizing gener-ation costs.To address the uncertainties associated with renew-able energies,a two-stage stochastic optimization framework is employed.Additionally,copula theory is also applied to capture the correlation between the output of adjacent renewable ener-gies.The conditional value-at-risk(CVaR)measure is further in-corporated into the objective function to account for risk aver-sion.Subsequently,the proposed optimization model is trans-formed into a mixed-integer linear programming(MILP)prob-lem.This transformation allows for tractable solutions using commercial solvers such as Gurobi.Finally,case studies are conducted on the modified IEEE 33-bus and 69-bus DSs.The results illustrate that the proposed method not only restores a greater load but also effectively mitigates uncertainty risks.展开更多
Charging infrastructure supports the rapid development of China's new energy vehicle industry.It not only plays a decisive role in providing accessible and convenient services for electric vehicle(EV)users but als...Charging infrastructure supports the rapid development of China's new energy vehicle industry.It not only plays a decisive role in providing accessible and convenient services for electric vehicle(EV)users but also,in one of the seven new infrastructure areas,plays an important role in stabilizing growth and unleashing economic potential during the new coronavirus(COVID-19)pandemic,impacting China's economy.In this study,the system dynamics model was used to predict the development of the EV industry and the demand for charging infrastructure,while considering the influence of policy,increase in EV mileage,and consumer purchase intention index.Furthermore,using the matching of EVs and charging infrastructure in Beijing and policy oriented sensitivity analysis,a simulation of the construction of battery swap taxis and power stations under three policy scenarios was conducted.This research shows that with policies implemented to support charging infrastructure and swapping compatible taxis,Beijing can achieve its goal of replacing all EVs with fast-swap batteries and fast-charging functions within three years.展开更多
Based on the Baa S model,a new energy vehicle supply chain game model composed of battery-swapping operators and vehicle manufacturers was constructed,and the corresponding optimal decisions of the supply chain member...Based on the Baa S model,a new energy vehicle supply chain game model composed of battery-swapping operators and vehicle manufacturers was constructed,and the corresponding optimal decisions of the supply chain members were obtained.The influence of related parameters on the equilibrium results was analyzed,and the Matlab was used for example analysis.The results show that:(1)The increase in the average consumer commuter mileage over the life of the vehicle can promote the increase in the demand for new energy vehicles and the profits of the supply chain members,which has a driving effect on the development of the battery swap industry.(2)Consumer sensitivity coefficient to the price of battery swap has a negative impact on battery-swapping price,new energy vehicle price,market demand for new energy vehicles,and profits of vehicle manufacturers and battery-swapping operators.展开更多
Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles ha...Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.展开更多
Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation...Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power grid.The proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,simultaneously.Afterwards,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling problem.Finally,simulation studies verify the effectiveness of the proposed multi-objective operation method.展开更多
This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping techno...This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology.In addition,some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing.The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics,such as location and charge level.The problem is formulated as a mixed binary problem,which minimizes operational costs and total unmet demand while maximizing the system's profit.To solve the optimization problem,a Branch and Bound method is employed.Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem.We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies.The results indicate that the proposed method enhances system performance and user travel times.展开更多
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supported by National Natural Science Foundation of China(No.62073212)Shanghai Science and Technology Commission(No.23ZR1426600).
文摘Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.
文摘As a good measure to tackle the challenges from energy shortages and environmental pollution,Electric Vehicles(EVs)have entered a period of rapid growth.Battery swapping station is a very important way of energy supply to EVs,and it is urgently needed to explore a coordinated control strategy to effectively smooth the load fluctuation in order to adopt the large-scale EVs.Considering bidirectional power flow between the station and power grid,this paper proposed a SFLA-based control strategy to smooth the load profile.Finally,compared simulations were performed according to the related data.Compared to particle swarm optimization(PSO)method,the presented SFLA-based strategy can effectively lower the peak-valley difference with the faster convergence rate and higher convergence precision.It is important for the swapping station that energy exchanging mode can supply energy for large-scale EVs with a smoother load profile than one-way charging mode.
文摘Combined with the perspective of State Grid Corporation of China （SGCC）, the paper researches the EV battery swapping mode and its advantages, analyses standards requirements of battery swapping system and proposes the corresponding standards system
基金supported by the Major Science and Technology Special Project of Jilin Province(No.20240204001SF).
文摘With the rapid adoption of electric vehicles(EVs),more charging and battery swapping facilities are needed to meet growing demand.However,a single type of charging or swapping facility cannot simultaneously and efficiently satisfy the power supply requirements of diverse vehicle types.In order to solve this problem,a joint planning method of charging piles and charging-battery swapping stations(CBSSs)is proposed in this paper.In this method,the influence of geospatial constraints on the layout scale of charging piles is considered,and the Monte Carlo simulation method is used to predict the spatiotemporal distribution of charging and battery swapping demands of private electric vehicles(PEVs)and the battery swapping demands of taxi electric vehicles(TEVs)respectively.On this basis,the layout scale of charging piles of each functional area is determined during the maximum charging demand period in a day to meet the demands of PEVs for charging convenience.Then,an operating state model of CBSS is established for calculation of the objective function.At the same time,a planning model of CBSSs is established to minimize the annual social comprehensive cost,which takes into account the economy of CBSSs and the battery swapping convenience of EVs.The planning of CBSSs can meet the demands of TEVs and some PEVs for a rapid power supply.Finally,using the urban transportation network of Changchun and IEEE 33-node system as an example,the planning of charging piles and CCBSs in direct charging mode and peak shifting mode are simulated and analyzed.The simulation results show that the proposed method enables PEVs and TEVs to access convenient and rapid power supply,and the planning result of CBSSs in direct charging mode is more economical,while peak shifting mode is more conducive to the safe operation of distribution networks.
基金supported by the National Natural Science Foundation of China(Nos.62373320,U23A20333,and 62122065)the Hebei Natural Science Foundation(Nos.F2024203011 and F2023203099)+1 种基金the S&T Program of Hebei(No.226Z4501G)the Science Research Project of Hebei Education Department(No.JZX2024005).
文摘With the popularity of electric vehicles(EVs),the impact of disorderly charging on power grid stability cannot be ignored.As one of the energy supply methods of electric vehicles,the optimal scheduling of battery swap station(BSS)can not only reduce the cost of BSS,but also ensure the stable operation of the power system.Therefore,this paper designs a BSS electricity price mechanism based on load demand forecasting and a user adaptive response mechanism based on battery health status and user satisfaction.On this basis,an optimal scheduling model of BSS system aiming at maximizing the revenue of BSS system is established.The simulation results show that the pricing strategy proposed in this paper can reduce the fluctuation of battery demand at each time and increase the revenue of BSS by 8.76%.In addition,the BSS optimal scheduling model proposed in this paper reduces the peak-to-average ratio of charging load from 2.52 to 2.10,realizing peak shaving and valley filling in the power system.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2572020BF04).
文摘Insufficiencies in charging facilities limit the broad application of electric vehicles(EVs).In addition,EV can hardly represent a green option if its electricity primarily depends on fossil energy.Considering these two problems,this paper studies a battery swapping-charging system based on wind farms(hereinafter referred to as W-BSCS).In a W-BSCS,the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station(CCS),which can centrally charge EV batteries and then distribute them to multiple battery swapping stations(BSSs).The operational framework of the W-BSCS is analyzed,and some preprocessing technologies are developed to reduce complexity in modeling.Then,a joint optimal scheduling model involving a wind power generation plan,battery swapping demand,battery charging and discharging,and a vehicle routing problem(VRP)is established.Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem.Numerical results verify the effectiveness of the joint optimal scheduling model,and they also show that the W-BSCS has great potential to promote EVs and wind power.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2014XS09)the China Scholarship Council of the Ministry of Education.
文摘To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The CLSC is used to depict the battery-swapping-charging process between the battery charging stations(BCSs)and battery swapping stations(BSSs).The arrival,departure and swapping service of electric vehicles(EVs)at BSSs is modeled as distinct queues based on the network calculus theory.The depleted batteries(DBs)and well-charging batteries(WBs)based interaction among BCSs and BSSs is formulated as a Stackelberg game.In the game,one BCS acts as the leader and the BSSs act as the followers.The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs,supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service(QoS)needed for battery swapping.The existence of Stackelberg equilibriums(SEs)of the proposed game is proved.A differential evaluation based hybrid algorithm is proposed to compute an SE.The effectiveness of proposed method has been demonstrated by the simulation results,guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.
基金the National Natural Science Foundation of China(Grant No.51207050).
文摘Integration of electric vehicles(EVs),demand response and renewable energy will bring multiple opportunities for low carbon power system.A promising integration will be EV battery swapping station(BSS)bundled with PV(photovoltaic)power.Optimizing the configuration and operation of BSS is the key problem to maximize benefit of this integration.The main objective of this paper is to solve infrastructure configuration of BSS.The principle challenge of such an objective is to enhance the swapping ability and save corresponding investment and operation cost under uncertainties of PV generation and swapping demand.Consequently this paper mainly concentrates on combining operation optimization with optimal investment strategies for BSS considering multiscenarios PV power generation and swapping demand.A stochastic programming model is developed by using state flow method to express different states of batteries and its objective is to maximize the station’s net profit.The model is formulated as a mixed-integer linear program to guarantee the efficiency and stability of the optimization.Case studies validate the effectiveness of the proposed approach and demonstrate that ignoring the uncertainties of PV generation and swapping demand may lead to an inappropriate batteries,chargers and swapping robots configuration for BSS.
基金supported by the Foundation of National Key Laboratory of Science and Technology(No.614221722040401)Green Intelligent Ship Standardization Leading Project(No.CBG4N21-4-2).
文摘Green shipping and electrification have been the main topics in the shipping industry.In this process,the pure battery-powered ship is developed,which is zero-emission and well-suited for inland shipping.Currently,battery swapping stations and ships are being explored since battery charging ships may not be feasible for inland long-distance trips.However,improper infrastructure planning for battery swapping stations and ships will increase costs and decrease operation efficiency.Therefore,a bilevel optimal infrastructure planning method is proposed in this paper for battery swapping stations and ships.First,the energy consumption model for the battery swapping ship is established considering the influence of the sailing environment.Second,a bilevel optimization model is proposed to minimize the total cost.Specifically,the battery swapping station(BSS)location problem is investigated at the upper level.The optimization of battery size in each battery swapping station and ship and battery swapping scheme are studied at the lower level based on speed and energy optimization.Finally,the bilevel self-adaptive differential evolution algorithm(BlSaDE)is proposed to solve this problem.The simulation results show that total cost could be reduced by 5.9%compared to the original results,and the effectiveness of the proposed method is confirmed.
文摘Towards the analysis of the developmental situation of wind power generation and electric vehicles,a novel idea for stabilizing the fl uctuation of wind farms’output by the use of battery swapping stations of electric vehicles is put forward in this paper,to effectively alleviate the impact of grid-connected operation of wind farms on the power system while promoting the fi eld operation of charging and battery swapping stations.A battery swapping station is treated as a capacity-variable energy storage power station,connected to the output terminal of a wind farm.A combined operation model for wind farm and battery swapping station is established based on the MATLAB/SIMULINK simulation platform and the control strategy is proposed for the operation of battery swapping stations.The simulation results show that the introduction of a battery swapping station can effectively stabilize the fl uctuation of wind farm output.
基金supported by the National Natural Science Foundation of China under Grant No.51007047
文摘An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.
文摘The implementation of the standard is expected to help electric vehicle battery swap stations to adapt to diversified needs and vehicle models,promoting the industry’s orderly and healthy development.
文摘Each morning at Yangluo Port in Wuhan,Hubei Province,the all-electric cargo vessel Huahang Xinneng No.1 completes a battery swap in under 10 minutes before returning to service with nearly 8,000 kWh of power onboard。
基金This work was funded by the National Natural Science Foundation of China(No.51936003).
文摘With the integration of wind power,photovoltaic power,gas turbine,and energy storage,the novel battery charg-ing and swapping station(NBCSS)possesses significant opera-tional flexibility,which can aid in the service restoration of dis-tribution system(DS)during power outages caused by extreme events.This paper presents an integrated optimization model for DS restoration that considers NBCSS,repair crews,and net-work reconfigurations simultaneously.The objective of this model is to maximize the restored load while minimizing gener-ation costs.To address the uncertainties associated with renew-able energies,a two-stage stochastic optimization framework is employed.Additionally,copula theory is also applied to capture the correlation between the output of adjacent renewable ener-gies.The conditional value-at-risk(CVaR)measure is further in-corporated into the objective function to account for risk aver-sion.Subsequently,the proposed optimization model is trans-formed into a mixed-integer linear programming(MILP)prob-lem.This transformation allows for tractable solutions using commercial solvers such as Gurobi.Finally,case studies are conducted on the modified IEEE 33-bus and 69-bus DSs.The results illustrate that the proposed method not only restores a greater load but also effectively mitigates uncertainty risks.
基金This research was funded by the National Social Science Fund of China[Grant number.16AGL004].
文摘Charging infrastructure supports the rapid development of China's new energy vehicle industry.It not only plays a decisive role in providing accessible and convenient services for electric vehicle(EV)users but also,in one of the seven new infrastructure areas,plays an important role in stabilizing growth and unleashing economic potential during the new coronavirus(COVID-19)pandemic,impacting China's economy.In this study,the system dynamics model was used to predict the development of the EV industry and the demand for charging infrastructure,while considering the influence of policy,increase in EV mileage,and consumer purchase intention index.Furthermore,using the matching of EVs and charging infrastructure in Beijing and policy oriented sensitivity analysis,a simulation of the construction of battery swap taxis and power stations under three policy scenarios was conducted.This research shows that with policies implemented to support charging infrastructure and swapping compatible taxis,Beijing can achieve its goal of replacing all EVs with fast-swap batteries and fast-charging functions within three years.
基金supported by the National Natural Science Foundation of China(Grant No.72161003)。
文摘Based on the Baa S model,a new energy vehicle supply chain game model composed of battery-swapping operators and vehicle manufacturers was constructed,and the corresponding optimal decisions of the supply chain members were obtained.The influence of related parameters on the equilibrium results was analyzed,and the Matlab was used for example analysis.The results show that:(1)The increase in the average consumer commuter mileage over the life of the vehicle can promote the increase in the demand for new energy vehicles and the profits of the supply chain members,which has a driving effect on the development of the battery swap industry.(2)Consumer sensitivity coefficient to the price of battery swap has a negative impact on battery-swapping price,new energy vehicle price,market demand for new energy vehicles,and profits of vehicle manufacturers and battery-swapping operators.
文摘Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.
基金This work was supported by the Key Scientific and Technological Research Project of State Grid Corporation of China(No.5400-202022113A-0-0-00).
文摘Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power grid.The proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,simultaneously.Afterwards,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling problem.Finally,simulation studies verify the effectiveness of the proposed multi-objective operation method.
文摘This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology.In addition,some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing.The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics,such as location and charge level.The problem is formulated as a mixed binary problem,which minimizes operational costs and total unmet demand while maximizing the system's profit.To solve the optimization problem,a Branch and Bound method is employed.Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem.We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies.The results indicate that the proposed method enhances system performance and user travel times.
